Hit-to-Lead Optimization of Heterocyclic Carbonyloxycarboximidamides as Selective Antagonists at Human Adenosine A3 ReceptorClick to copy article linkArticle link copied!
- Xianglin HuangXianglin HuangDepartment of Pharmacology, University of Cambridge, Tennis Court Road, Cambridge CB2 1PD, U.K.More by Xianglin Huang
- Anna ChorianopoulouAnna ChorianopoulouLaboratory of Medicinal Chemistry, Section of Pharmaceutical Chemistry, Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Panepistimiopolis-Zografou 15771, Athens, GreeceMore by Anna Chorianopoulou
- Panagoula KalkounouPanagoula KalkounouLaboratory of Medicinal Chemistry, Section of Pharmaceutical Chemistry, Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Panepistimiopolis-Zografou 15771, Athens, GreeceMore by Panagoula Kalkounou
- Maria GeorgiouMaria GeorgiouLaboratory of Medicinal Chemistry, Section of Pharmaceutical Chemistry, Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Panepistimiopolis-Zografou 15771, Athens, GreeceMore by Maria Georgiou
- Athanasios PousiasAthanasios PousiasLaboratory of Medicinal Chemistry, Section of Pharmaceutical Chemistry, Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Panepistimiopolis-Zografou 15771, Athens, GreeceMore by Athanasios Pousias
- Amy DaviesAmy DaviesDepartment of Pharmacology, University of Cambridge, Tennis Court Road, Cambridge CB2 1PD, U.K.More by Amy Davies
- Abigail PearceAbigail PearceDepartment of Pharmacology, University of Cambridge, Tennis Court Road, Cambridge CB2 1PD, U.K.More by Abigail Pearce
- Matthew HarrisMatthew HarrisDepartment of Pharmacology, University of Cambridge, Tennis Court Road, Cambridge CB2 1PD, U.K.More by Matthew Harris
- George LambrinidisGeorge LambrinidisLaboratory of Medicinal Chemistry, Section of Pharmaceutical Chemistry, Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Panepistimiopolis-Zografou 15771, Athens, GreeceMore by George Lambrinidis
- Panagiotis MarakosPanagiotis MarakosLaboratory of Medicinal Chemistry, Section of Pharmaceutical Chemistry, Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Panepistimiopolis-Zografou 15771, Athens, GreeceMore by Panagiotis Marakos
- Nicole PouliNicole PouliLaboratory of Medicinal Chemistry, Section of Pharmaceutical Chemistry, Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Panepistimiopolis-Zografou 15771, Athens, GreeceMore by Nicole Pouli
- Antonios Kolocouris*Antonios Kolocouris*Email: [email protected]. Phone: +33 210-727-4834.Laboratory of Medicinal Chemistry, Section of Pharmaceutical Chemistry, Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Panepistimiopolis-Zografou 15771, Athens, GreeceMore by Antonios Kolocouris
- Nikolaos Lougiakis*Nikolaos Lougiakis*Email: [email protected]. Phone: +33 210-727-4759.Laboratory of Medicinal Chemistry, Section of Pharmaceutical Chemistry, Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Panepistimiopolis-Zografou 15771, Athens, GreeceMore by Nikolaos Lougiakis
- Graham Ladds*Graham Ladds*Email: [email protected]. Phone: +44 (0) 1223 334020.Department of Pharmacology, University of Cambridge, Tennis Court Road, Cambridge CB2 1PD, U.K.More by Graham Ladds
Abstract
Antagonism of the human adenosine A3 receptor (hA3R) has potential therapeutic application. Alchemical relative binding free energy calculations of K18 and K32 suggested that the combination of a 3-(2,6-dichlorophenyl)-isoxazolyl group with 2-pyridinyl at the ends of a carbonyloxycarboximidamide group should improve hA3R affinity. Of the 25 new analogues synthesized, 37 and 74 showed improved hA3R affinity compared to K18 (and K32). This was further improved through the addition of a bromine group to the 2-pyridinyl at the 5-position, generating compound 39. Alchemical relative binding free energy calculations, mutagenesis studies and MD simulations supported the compounds’ binding pattern while suggesting that the bromine of 39 inserts deep into the hA3R orthosteric pocket, so highlighting the importance of rigidification of the carbonyloxycarboximidamide moiety. MD simulations highlighted the importance of rigidification of the carbonyloxycarboximidamide, while suggesting that the bromine of 39 inserts deep into the hA3R orthosteric pocket, which was supported through mutagenesis studies 39 also selectively antagonized endogenously expressed hA3R in nonsmall cell lung carcinoma cells, while pharmacokinetic studies indicated low toxicity enabling in vivo evaluation. We therefore suggest that 39 has potential for further development as a high-affinity hA3R antagonist.
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You are free to share(copy and redistribute) this article in any medium or format and to adapt(remix, transform, and build upon) the material for any purpose, even commercially within the parameters below:
Creative Commons (CC): This is a Creative Commons license.
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License Summary*
You are free to share(copy and redistribute) this article in any medium or format and to adapt(remix, transform, and build upon) the material for any purpose, even commercially within the parameters below:
Creative Commons (CC): This is a Creative Commons license.
Attribution (BY): Credit must be given to the creator.
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Introduction
Results
Structure-Based Drug Design
Chemical Synthesis
In Vitro Pharmacological Characterization
Quantifying the Binding Affinity and Kinetics of Potential Antagonists at hA3R
All the equilibrium binding affinities (pKi) were determined with NanoBRET ligand binding assay and represented as mean ± standard error of the mean (SEM) of n independent repeats with experiment conducted in duplicates. Data of K18 was taken from ref (25). One-way ANOVA with Dunnett’s post-test was used to determine the statistical significance (*p < 0.05) compared to the pKi of K18.
compound | Kon(k3) (x106)/M–1 min–1 | Koff(k4)/min–1 | RT/min |
---|---|---|---|
37 | 1.60 ± 0.34 | 0.075 ± 0.002 | 13.5 ± 0.4 |
39 | 5.95 ± 0.42 | 0.046 ± 0.002 | 22.1 ± 1.0 |
40 | 0.23 ± 0.05 | 0.057 ± 0.001 | 17.5 ± 0.2 |
47 | 1.49 ± 0.44 | 0.049 ± 0.004 | 21.3 ± 2.2 |
48 | 1.01 ± 0.20 | 0.054 ± 0.003 | 14.7 ± 3.4 |
59 | 0.65 ± 0.07 | 0.050 ± 0.004 | 20.5 ± 1.9 |
60 | 0.35 ± 0.06 | 0.102 ± 0.015 | 10.7 ± 2.0 |
MRS1220 | 325 ± 2.8b | 0.025 ± 0.005b | 40.32b |
Kon(k3) and Koff(k4) for each compound determined using NanoBRET binding assay at Nluc-hA3R and fitted with the “kinetics of competitive binding model”. RT as determined by 1/Koff.
Indicated values from ref (25).
Seven High-Affinity Antagonists Displayed High Selectivity for hA3R over the Other AR Subtypes
Further Exploration of the Role of the 2,6-Dichlorophenyl Group in the Binding Affinity toward A3R
All the equilibrium binding affinities (pKi) were determined using NanoBRET binding assay and represented as mean ± SEM of n independent repeats with experiment conducted in duplicates. Data of K18 was taken from ref (25). One-way ANOVA with Dunnett’s post-test was used to determine the statistical significance (*p < 0.05) compared to the pKi of K18 (p1) and 37# or 39∧ (p2) as appropriate.
compound | Kon(k3) (x106)/M–1 min–1 | Koff (k4)/min–1 | RT/min |
---|---|---|---|
74 | 3.83 ± 0.54 | 0.092 ± 0.006 | 11.0 ± 0.7 |
75 | 2.41 ± 0.26 | 0.057 ± 0.006 | 18.4 ± 2.1 |
76 | 1.12 ± 0.23 | 0.081 ± 0.007 | 12.6 ± 1.1 |
77 | 1.51 ± 0.28 | 0.066 ± 0.002 | 15.1 ± 0.4 |
Kon(k3) and Koff(k4) for each compound determined using NanoBRET binding assay at Nluc-hA3R and fitted with the “kinetics of competitive binding model”. RT as determined by 1/Koff.
compound | pKi (equilibrium)a | pKd (kinetics)b | pKB (cAMP)c |
---|---|---|---|
37 | 7.33 ± 0.06 | 7.30 ± 0.10 | 7.45 ± 0.19 |
39 | 7.92 ± 0.06 | 8.11 ± 0.04 | #7.95 ± 0.15 |
40 | 6.79 ± 0.07 | 6.57 ± 0.09 | 7.05 ± 0.13 |
47 | 7.14 ± 0.04 | 7.43 ± 0.11 | 6.99 ± 0.18 |
48 | 7.01 ± 0.05 | 7.25 ± 0.08 | 7.09 ± 0.22 |
59 | 7.04 ± 0.08 | 7.11 ± 0.09 | 6.95 ± 0.13 |
60 | 7.23 ± 0.06 | 6.54 ± 0.09 | 6.89 ± 0.07 |
74 | 7.53 ± 0.06 | 7.62 ± 0.02 | 7.32 ± 0.05 |
75 | 7.56 ± 0.04 | 7.60 ± 0.12 | 7.39 ± 0.09 |
76 | 7.36 ± 0.07 | 7.34 ± 0.07 | 7.30 ± 0.14 |
77 | 6.90 ± 0.03 | 7.11 ± 0.10 | 6.55 ± 0.21 |
Binding Profile Investigation
MD Simulations
TI/MD Calculations
Mutagenesis Studies of Compound 39 at hA3R in Comparison with 37
Disease Model Validation and In Vitro Pharmacokinetic Profiling
Validation of Compound Selectivity at Endogenously Expressed Receptors in a Disease Model of Lung Cancer
Pharmacokinetic Assessment of Lead Compound 39
solution properties | ||||
---|---|---|---|---|
aqueous solubilitya | ||||
39 | diethyl stilbestrol | disulfiram | ||
In PBS, pH 7.4 (μM) | 0.10 | 5.22 | 45.64 | |
in simulated gastric fluid (μM) | 0.16 | N.D. | N.D. | |
in simulated intestinal fluid (μM) | 43.13 | N.D. | N.D. | |
partition coefficientb (n-octanol/PBS, pH 7.4) | ||||
39 | haloperidol | phenytoin | ||
Log D | 2.82 | 2.49 | 2.28 | |
protein bindingc | ||||
39 | sertraline | warfarin | ||
% protein bound | 99 | 99 | 95 | |
% recovery | 105 | 94 | 112 | |
In Vitro Absorption | ||||
permeability in Caco-2 celld | ||||
39 | propranolol | labetalol | ||
PappA-B (10–6 cm/s) | 7.2 | 32.2 | 8.3 | |
PappB-A (10–6 cm/s) | 0.6 | 32.6 | 38.2 | |
% recovery (A-B) | 26 | 74 | 99 | |
% recovery (B-A) | 19 | 96 | 103 | |
uptake ratio | 12 | 0.99 | 0.22 | |
In Vitro Metabolism | ||||
intrinsic clearance (human liver microsomes)e | ||||
39 | terfenadine | verapamil | ||
t1/2 (min) | 9.5 | 16.9 | 26.6 | |
CLint (μL/min/mg of microsomes) | 729 | 410.1 | 261.1 |
Aqueous solubility (μM) in PBS at pH 7.4/simulated gastric fluid/simulated intestinal fluid determined with high-performance liquid chromatography–ultraviolet spectroscopy.
The partition efficient of the compound between n-octanol and PBS at pH7.4, measuring the lipophilicity of the tested compound. Log D was calculated as Log10(the amount of compound in n-octanol/the amount of compound in PBS).
Measure of percentage of protein binding and percentage of compound recovery during the assay determined in equilibrium dialysis using human plasma.
The permeability of compounds assessed in bidirectional Caco-2 cell permeability assay with pH = 6.5 for donor chamber and pH = 7.4 for receiver chamber. The extent of permeability is measured as apparent permeability coefficient (Papp) from apical (A) to basolateral (B) or in reverse direction. The percentage recovery of the compound is calculated as the total amount of compound in the donor and the receiver at the end of experiment/the amount of initial compound present. The uptake ratio of the compound is calculated as PappA-B/PappB-A.
The metabolic stability of compounds was determined in 0.1 mg/mL human liver microsomes, measured as the half-life (t1/2) and apparent intrinsic clearance (CLint).
Discussion
Experimental Section
Biological Methods
Compounds
Constructs
Cell Culture and Transfection
NanoBRET Binding Assay
cAMP Competition Assay
Flow Cytometry
Proliferation Assay
In Vitro Pharmacokinetic Assessment
Data Analysis
Computational Medicinal Chemistry
Preparation of Model of the Unresolved Inactive hA3R
Molecular Docking Calculations
MD Simulations
Alchemical TI/MD Binding Free Energies Calculated with the MBAR Method
Chemistry
General Information
6-Chloro-9-methyl-9H-purine (15) and 6-Chloro-7-methyl-7H-purine (16)
9-Methyl-9H-purine-6-carbonitrile (17)
General Procedure for the Preparation of the Arylamidoximes 18–30
Method A
Method B
(Z)-N′-Hydroxypicolinimidamide (18)
(Z)-3,5-Dichloro-N′-hydroxypicolinimidamide (19)
(Z)-5-Bromo-N′-hydroxypicolinimidamide (20)
(Z)-N′-Hydroxy-6-methylpicolinimidamide (21)
(Z)-N′-Hydroxypyrimidine-2-carboximidamide (22)
(Z)-2-Chloro-N′-hydroxynicotinimidamide (23)
(Z)-N′-Hydroxy-6-methylnicotinimidamide (24)
(Z)-N′-Hydroxynicotinimidamide (25)
(Z)-N′-Hydroxy-3-methylbenzimidamide (26)
(Z)-N′-Hydroxy-2-(pyridin-2-yl)acetimidamide (27)
(Z)-N′-Hydroxy-2-(pyridin-3-yl)acetimidamide (28)
(Z)-N′-Hydroxy-1-methyl-1H-indole-5-carboximidamide (29)
(Z)-N′-Hydroxy-9-methyl-9H-purine-6-carboximidamide (30)
General Procedure for the Preparation of the Acyl Chlorides 34–36
General Procedure for the Preparation of the Target Derivatives 37–55
(Z)-N′-((3-(2,6-Dichlorophenyl)-5-methylisoxazole-4-carbonyl)oxy)picolinimidamide (37)
(Z)-3,5-Dichloro-N′-((3-(2,6-dichlorophenyl)-5-methylisoxazole-4-carbonyl)oxy)picolinimidamide (38)
(Z)-5-Bromo-N′-((3-(2,6-dichlorophenyl)-5-methylisoxazole-4-carbonyl)oxy)picolinimidamide (39)
(Z)-N′-((3-(2,6-Dichlorophenyl)-5-methylisoxazole-4-carbonyl)oxy)-6-methylpicolinimidamide (40)
(Z)-N′-((3-(2-Chlorophenyl)-5-methylisoxazole-4-carbonyl)oxy)-6-methylpicolinimidamide (41)
(Z)-N′-((3-(2-Chlorophenyl)-5-methylisoxazole-4-carbonyl)oxy)picolinimidamide (42)
(Z)-6-Methyl-N′-((5-methyl-3-phenylisoxazole-4-carbonyl)oxy)picolinimidamide (43)
(Z)-N′-((5-methyl-3-phenylisoxazole-4-carbonyl)oxy)picolinimidamide (44)
(Z)-N′-((3-(2,6-Dichlorophenyl)-5-methylisoxazole-4-carbonyl)oxy)pyrimidine-2-carboximidamide (45)
(Z)-2-Chloro-N′-((3-(2,6-dichlorophenyl)-5-methylisoxazole-4-carbonyl)oxy)nicotinimidamide (46)
(Z)-N′-((3-(2,6-Dichlorophenyl)-5-methylisoxazole-4-carbonyl)oxy)-6-methylnicotinimidamide (47)
(Z)-N′-((3-(2,6-Dichlorophenyl)-5-methylisoxazole-4-carbonyl)oxy)nicotinimidamide (48)
(Z)-N′-((3-(2,6-Dichlorophenyl)-5-methylisoxazole-4-carbonyl)oxy)-3-methylbenzimidamide (49)
(Z)-3-Methyl-N′-((5-methyl-3-phenylisoxazole-4-carbonyl)oxy)benzimidamide (50)
(Z)-N′-((3-(2-Chlorophenyl)-5-methylisoxazole-4-carbonyl)oxy)-3-methylbenzimidamide (51)
(Z)-N′-((3-(2,6-Dichlorophenyl)-5-methylisoxazole-4-carbonyl)oxy)-1-methyl-1H-indole-5-carboximidamide (52)
(Z)-N′-((3-(2,6-Dichlorophenyl)-5-methylisoxazole-4-carbonyl)oxy)-9-methyl-9H-purine-6-carboximidamide (53)
(Z)-N′-((3-(2,6-Dichlorophenyl)-5-methylisoxazole-4-carbonyl)oxy)-2-(pyridin-2-yl)acetimidamide (54)
(Z)-N′-((3-(2,6-Dichlorophenyl)-5-methylisoxazole-4-carbonyl)oxy)-2-(pyridin-3-yl)acetimidamide (55)
General Procedure for the Preparation of the Target Derivatives 56–61
5-(3-(2,6-Dichlorophenyl)-5-methylisoxazol-4-yl)-3-(pyridin-2-yl)-1,2,4-oxadiazole (56)
3-(5-Bromopyridin-2-yl)-5-(3-(2,6-dichlorophenyl)-5-methylisoxazol-4-yl)-1,2,4-oxadiazole (57)
5-(3-(2,6-Dichlorophenyl)-5-methylisoxazol-4-yl)-3-(pyrimidin-2-yl)-1,2,4-oxadiazole (58)
5-(3-(2,6-Dichlorophenyl)-5-methylisoxazol-4-yl)-3-(pyridin-3-yl)-1,2,4-oxadiazole (59)
5-(3-(2,6-Dichlorophenyl)-5-methylisoxazol-4-yl)-3-(6-methylpyridin-3-yl)-1,2,4-oxadiazole (60)
5-(3-(2,6-Dichlorophenyl)-5-methylisoxazol-4-yl)-3-(pyridin-3-ylmethyl)-1,2,4-oxadiazole (61)
General Procedure for the Preparation of the Carboxylic Acids 70 and 71
3-(2,6-Dibromophenyl)-5-methylisoxazole-4-carboxylic Acid (70)
3-(2,6-Dimethylphenyl)-5-methylisoxazole-4-carboxylic Acid (71)
3-(2,6-Dibromophenyl)-5-methylisoxazole-4-carbonyl Chloride (72) and 3-(2,6-Dimethylphenyl)-5-methylisoxazole-4-carbonyl Chloride (73)
(Z)-N′-((3-(2,6-Dibromophenyl)-5-methylisoxazole-4-carbonyl)oxy)picolinimidamide (74)
(Z)-5-Bromo-N′-((3-(2,6-dibromophenyl)-5-methylisoxazole-4-carbonyl)oxy)picolinimidamide (75)
(Z)-N′-((3-(2,6-Dimethylphenyl)-5-methylisoxazole-4-carbonyl)oxy)picolinimidamide (76)
(Z)-5-Bromo-N′-((3-(2,6-dimethylphenyl)-5-methylisoxazole-4-carbonyl)oxy)picolinimidamide (77)
Data Availability
The following link is provided to access the starting structures (docking poses) and output frames of the complexes between our revised model of the inactive hA3R generated using the multistate AF2 method and ligands K18, 37–39, 56, 57, and 60 from the MD simulations: https://github.com/annachor/inactive_A3R_AF2-carbonyloxycarboximidamides_MDs.
Supporting Information
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jmedchem.4c01092.
Molecular formula strings (CSV)
Information on the pharmacological, computational, and chemical characterization of the tested compounds (PDF)
Terms & Conditions
Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.
Acknowledgments
This research represents part of X.H. and A.C. PhD theses and of the P.K. MRes thesis. This computational time on this project was supported by a grant from the Greek Research & Technology Network (GRNET) in the National HPC Facility ARIS (pr001007). We also gratefully acknowledge the support of Chiesi Hellas (A.K.), the Cambridge Trust and China Scholarship Council (X.H.), and BBSRC (A.D. (BB/X010899/1), and A.P. and G.L. (BB/W014831/1)). In addition, G.L. is funded by a Royal Society Industry Fellowship (INF/R2/212001].
ΔΔGb,TI/MD | relative binding free energy |
A2AR | adenosine A2A receptor |
A2BR | adenosine A2B receptor |
AF2 | Alphafold 2 |
ARs | adenosine receptors |
cAMP | 3′,5′-cyclic adenosine monophosphate |
CHO-K1 | Chinese hamster ovary K1 |
CCK-8 | cell counting kit-8 |
CLint | intrinsic clearance |
CPA | N6-cyclopentyl-adenosine |
dppf | 1,1′-ferrocenediyl-bis(diphenylphosphine) |
DMA | dimethylacetamide |
DMSO | dimethyl sulfoxide |
FACS | fluorescence-activated cell sorting |
FEP/MD | free energy perturbation coupled with molecular dynamics simulations |
Ff14sb | amber14sb force field |
Ff19sb | amber19sb force field |
GPCRs | G protein-coupled receptors |
hA1R | human adenosine A1 receptor |
hA3R | human adenosine A3 receptor |
HEK293T | human embryonic kidney 293T |
IB-MECA | 1-deoxy-1-[6-[[(3-iodophenyl)methyl]amino]-9H-purin-9-yl]-N-methyl-β-d-ribofuranuronamide |
MAPK | mitogen-activated protein kinase |
MD | molecular dynamics |
MM/PBSA | Poisson–Boltzmann surface area continuum solvation |
MM/GBSA | generalized Born surface area continuum solvation |
MRS1220 | (N-[9-chloro-2-(2-furanyl)-1,2,4-triazolo[1,5-c]quinazolin-5-yl]benzeneacetamide) |
PMEMD | particle mesh Ewald molecular dynamics |
Papp | permeability assay |
NECA | 5′-N-ethylcarboxamidoadenosine |
NanoBRET | Nanoluciferease-based bioluminescence resonance energy transfer |
Nluc | Nanoluciferease |
RT | residence time |
rmsd | root-mean-square deviation |
SAR | structure–activity relationships |
SBDD | structure-based drug design |
SEM | standard error of the mean |
TI/MD | thermodynamic integration coupled with molecular dynamics simulations method |
TM | transmembrane |
VS | virtual screening |
WT | wild type |
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- 7Cai, H.; Guo, S.; Xu, Y.; Sun, J.; Li, J.; Xia, Z.; Jiang, Y.; Xie, X.; Xu, H. E. Cryo-EM structures of adenosine receptor A3AR bound to selective agonists. Nat. Commun. 2024, 15 (1), 3252, DOI: 10.1038/s41467-024-47207-6Google ScholarThere is no corresponding record for this reference.
- 8Katritch, V.; Jaakola, V. P.; Lane, J. R.; Lin, J.; Ijzerman, A. P.; Yeager, M.; Kufareva, I.; Stevens, R. C.; Abagyan, R. Structure-based discovery of novel chemotypes for adenosine A2A receptor antagonists. J. Med. Chem. 2010, 53 (4), 1799– 1809, DOI: 10.1021/jm901647pGoogle Scholar8https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXptlKqsw%253D%253D&md5=5a54df43f6edd20d83e7e5942e2f9811Structure-Based Discovery of Novel Chemotypes for Adenosine A2A Receptor AntagonistsKatritch, Vsevolod; Jaakola, Veli-Pekka; Lane, J. Robert; Lin, Judy; IJzerman, Adriaan P.; Yeager, Mark; Kufareva, Irina; Stevens, Raymond C.; Abagyan, RubenJournal of Medicinal Chemistry (2010), 53 (4), 1799-1809CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)The recent progress in crystallog. of G-protein coupled receptors opens an unprecedented venue for structure-based GPCR drug discovery. To test efficiency of the structure-based approach, we performed mol. docking and virtual ligand screening (VLS) of more than 4 million com. available "drug-like" and "lead-like" compds. against the A2AAR 2.6 Å resoln. crystal structure. Out of 56 high ranking compds. tested in A2AAR binding assays, 23 showed affinities under 10 μM, 11 of those had sub-μM affinities and two compds. had affinities under 60 nM. The identified hits represent at least 9 different chem. scaffolds and are characterized by very high ligand efficiency (0.3-0.5 kcal/mol per heavy atom). Significant A2AAR antagonist activities were confirmed for 10 out of 13 ligands tested in functional assays. High success rate, novelty, and diversity of the chem. scaffolds and strong ligand efficiency of the A2AAR antagonists identified in this study suggest practical applicability of receptor-based VLS in GPCR drug discovery.
- 9Carlsson, J.; Yoo, L.; Gao, Z. G.; Irwin, J. J.; Shoichet, B. K.; Jacobson, K. A. Structure-based discovery of A2Aadenosine receptor ligands. J. Med. Chem. 2010, 53 (9), 3748– 3755, DOI: 10.1021/jm100240hGoogle Scholar9https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXkvFaqsL8%253D&md5=c36c941d52d2cec06387d79c4c423d46Structure-Based Discovery of A2A Adenosine Receptor LigandsCarlsson, Jens; Yoo, Lena; Gao, Zhan-Guo; Irwin, John J.; Shoichet, Brian K.; Jacobson, Kenneth A.Journal of Medicinal Chemistry (2010), 53 (9), 3748-3755CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)The recent detn. of X-ray structures of pharmacol. relevant GPCRs has made these targets accessible to structure-based ligand discovery. Here we explore whether novel chemotypes may be discovered for the A2A adenosine receptor, based on complementarity to its recently detd. structure. The A2A adenosine receptor signals in the periphery and the CNS, with agonists explored as anti-inflammatory drugs and antagonists explored for neurodegenerative diseases. We used mol. docking to screen a 1.4 million compd. database against the X-ray structure computationally and tested 20 high-ranking, previously unknown mols. exptl. Of these 35% showed substantial activity with affinities between 200 nM and 9 μM. For the most potent of these new inhibitors, over 50-fold specificity was obsd. for the A2A vs. the related A1 and A3 subtypes. These high hit rates and affinities at least partly reflect the bias of com. libraries toward GPCR-like chemotypes, an issue that we attempt to investigate quant. Despite this bias, many of the most potent new ligands were novel, dissimilar from known ligands, providing new lead structures for modulation of this medically important target.
- 10Lenselink, E. B.; Beuming, T.; van Veen, C.; Massink, A.; Sherman, W.; van Vlijmen, H. W. T.; Ijzerman, A. P. In search of novel ligands using a structure-based approach: a case study on the adenosine A2A receptor. J. Comput. Aided Mol. Des. 2016, 30 (10), 863– 874, DOI: 10.1007/s10822-016-9963-7Google ScholarThere is no corresponding record for this reference.
- 11Cescon, E.; Bolcato, G.; Federico, S.; Bissaro, M.; Valentini, A.; Ferlin, M. G.; Spalluto, G.; Sturlese, M.; Moro, S. Scaffold Repurposing of in-House Chemical Library toward the Identification of New Casein Kinase 1 Inhibitors. ACS Med. Chem. Lett. 2020, 11 (6), 1168– 1174, DOI: 10.1021/acsmedchemlett.0c00028Google Scholar11https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXot1Wms7c%253D&md5=191136a578c976cacb0219cf4612d219Scaffold Repurposing of in-House Chemical Library toward the Identification of New Casein Kinase 1 δ InhibitorsCescon, Eleonora; Bolcato, Giovanni; Federico, Stephanie; Bissaro, Maicol; Valentini, Alice; Ferlin, Maria Grazia; Spalluto, Gianpiero; Sturlese, Mattia; Moro, StefanoACS Medicinal Chemistry Letters (2020), 11 (6), 1168-1174CODEN: AMCLCT; ISSN:1948-5875. (American Chemical Society)Recent studies have highlighted the key role of Casein kinase 1 δ (CK1δ) in the development of several neurodegenerative pathologies, such as Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS). So far, CK1δ inhibitors are noncovalent ATP competitive ligands and no drugs are currently available for this mol. target, hence the interest in developing new CK1δ inhibitors. The study aims to identify new inhibitors able to bind the enzyme; by a dual approach in silico/in vitro, the virtual screening has been performed on an inhouse chem. library, which was previously designed and synthesized for other targets. The work can, therefore, be seen in the scaffold repurposing logic. The proposed strategy has led to the identification of two hits, having a novel scaffold in the landscape of CK1δ inhibitors and with an activity in the micromolar range.
- 12Langmead, C. J.; Andrews, S. P.; Congreve, M.; Errey, J. C.; Hurrell, E.; Marshall, F. H.; Mason, J. S.; Richardson, C. M.; Robertson, N.; Zhukov, A. Identification of novel adenosine A2A receptor antagonists by virtual screening. J. Med. Chem. 2012, 55 (5), 1904– 1909, DOI: 10.1021/jm201455yGoogle ScholarThere is no corresponding record for this reference.
- 13Jazayeri, A.; Andrews, S. P.; Marshall, F. H. Structurally enabled discovery of adenosine a2a receptor antagonists. Chem. Rev. 2017, 117 (1), 21– 37, DOI: 10.1021/acs.chemrev.6b00119Google Scholar13https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhtVWkur3L&md5=5e0a332b9f43eae4854f1681a79ff184Structurally Enabled Discovery of Adenosine A2A Receptor AntagonistsJazayeri, Ali; Andrews, Stephen P.; Marshall, Fiona H.Chemical Reviews (Washington, DC, United States) (2017), 117 (1), 21-37CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)A review. Over the past decade there has been a revolution in the field of G protein-coupled receptor (GPCR) structural biol. Many years of innovative research from different areas have come together to fuel this significant change in the fortunes of this field, which for many years was characterized by the paucity of high-resoln. structures. The detn. to succeed has been in part due to the recognized importance of these proteins as drug targets, and although the pharmaceutical industry has been focusing on these receptors, it can be justifiably argued and demonstrated that many of the approved and com. successful GPCR drugs can be significantly improved to increase efficacy and/or reduce undesired side effects. In addn., many validated targets in this class remain to be drugged. It is widely recognized that application of structure-based drug design approaches can help medicinal chemists a long way toward discovering better drugs. The achievement of structural biologists in providing high-resoln. insight is beginning to transform drug discovery efforts, and there are a no. of GPCR drugs that have been discovered by use of structural information that are in clin. development. This review aims to highlight the key developments that have brought success to GPCR structure resoln. efforts and exemplify the practical application of structural information for the discovery of adenosine A2A receptor antagonists that have potential to treat multiple conditions.
- 14Tian, S.; Wang, X.; Li, L.; Zhang, X.; Li, Y.; Zhu, F.; Hou, T.; Zhen, X. Discovery of Novel and Selective Adenosine A2A Receptor Antagonists for Treating Parkinson’s Disease through Comparative Structure-Based Virtual Screening. J. Chem. Inf. Model. 2017, 57 (6), 1474– 1487, DOI: 10.1021/acs.jcim.7b00188Google Scholar14https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXmvFamtb4%253D&md5=1d81fbc9dde5327024eaee18e944b926Discovery of Novel and Selective Adenosine A2A Receptor Antagonists for Treating Parkinson's Disease through Comparative Structure-Based Virtual ScreeningTian, Sheng; Wang, Xu; Li, Linlang; Zhang, Xiaohu; Li, Youyong; Zhu, Feng; Hou, Tingjun; Zhen, XuechuJournal of Chemical Information and Modeling (2017), 57 (6), 1474-1487CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)Among non-dopaminergic strategies for combating Parkinson's disease (PD), antagonism of the A2A adenosine receptor (AR) has emerged to show great potential. In this study, on the basis of two crystal structures of the A2A AR with the best capability to distinguish known antagonists from decoys, docking-based virtual screening (VS) was conducted to identify novel A2A AR antagonists. A total of 63 structurally diverse compds. identified by VS were submitted to exptl. testing, and 11 of them exhibited substantial activity against the A2A AR (Ki < 10 μM), including two compds. with Ki below 1 μM (compd. 43, 0.42 μM; compd. 51, 0.27 μM) and good A2A/A1 selectivity (fold < 0.1). Compds. 43 and 51 demonstrated antagonistic activity according to the results of cAMP measurements (cAMP IC50 = 1.67 and 1.80 μM, resp.) and showed good efficacy in the haloperidol-induced catalepsy (HIC) rat model for PD at doses of up to 30 mg/kg. Further lead optimization based on a substructure searching strategy led to the discovery of compd. 84 as an excellent A2A AR antagonist (A2AKi = 54 nM, A2A/A1 fold < 0.1, cAMP IC50 = 0.3 μM) that exhibited significant improvement in anti-PD efficacy in the HIC rat model.
- 15Lagarias, P.; Vrontaki, E.; Lambrinidis, G.; Stamatis, D.; Convertino, M.; Ortore, G.; Mavromoustakos, T.; Klotz, K. N.; Kolocouris, A. Discovery of Novel Adenosine Receptor Antagonists through a Combined Structure- and Ligand-Based Approach Followed by Molecular Dynamics Investigation of Ligand Binding Mode. J. Chem. Inf. Model. 2018, 58 (4), 794– 815, DOI: 10.1021/acs.jcim.7b00455Google Scholar15https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXjtlOktbw%253D&md5=c6e5de5973264c7dcab867f801e8bfa2Discovery of Novel Adenosine Receptor Antagonists through a Combined Structure- and Ligand-Based Approach Followed by Molecular Dynamics Investigation of Ligand Binding ModeLagarias, Panagiotis; Vrontaki, Eleni; Lambrinidis, George; Stamatis, Dimitrios; Convertino, Marino; Ortore, Gabriella; Mavromoustakos, Thomas; Klotz, Karl-Norbert; Kolocouris, AntoniosJournal of Chemical Information and Modeling (2018), 58 (4), 794-815CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)An intense effort is made by pharmaceutical and academic research labs. to identify and develop selective antagonists for each adenosine receptor (AR) subtype as potential clin. candidates for "soft" treatment of various diseases. Crystal structures of subtypes A2A and A1ARs offer exciting opportunities for structure-based drug design. In the first part of the present work, Maybridge HitFinder library of 14400 compds. was utilized to apply a combination of structure-based against the crystal structure of A2AAR and ligand-based methodologies. The docking poses were rescored by CHARMM energy minimization and calcn. of the desolvation energy using Poisson-Boltzmann equation electrostatics. Out of the eight selected and tested compds., five were found pos. hits (63% success). Although the project was initially focused on targeting A2AAR, the identified antagonists exhibited low micromolar or micromolar affinity against A2A/A3, ARs, or A3AR, resp. Based on these results, 19 compds. characterized by novel chemotypes were purchased and tested. Sixteen of them were identified as AR antagonists with affinity toward combinations of the AR family isoforms (A2A/A3, A1/A3, A1/A2A/A3, and A3). The second part of this work involves the performance of hundreds of mol. dynamics (MD) simulations of complexes between the ARs and a total of 27 ligands to resolve the binding interactions of the active compds., which were not achieved by docking calcns. alone. This computational work allowed the prediction of stable and unstable complexes which agree with the exptl. results of potent and inactive compds., resp. Of particular interest is that the 2-amino-thiophene-3-carboxamides, 3-acylamino-5-aryl-thiophene-2-carboxamides, and carbonyloxycarboximidamide derivs. were found to be selective and possess a micromolar to low micromolar affinity for the A3 receptor.
- 16Zwanzig, R. W. High-Temperature Equation of State by a Perturbation Method. I. Nonpolar Gases. J. Chem. Phys. 1954, 22 (8), 1420– 1426, DOI: 10.1063/1.1740409Google Scholar16https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaG2cXnsFCgsw%253D%253D&md5=8b0bce75afc2097b1c178dab250389a9High-temperature equation of state by a perturbation method. I. Nonpolar gasesZwanzig, Robert W.Journal of Chemical Physics (1954), 22 (), 1420-6CODEN: JCPSA6; ISSN:0021-9606.A theoretical study was made of the equations of state of A and N at high temp. and ds. The intermol. potential was of the Lennard-Jones form, with an adjustable rigid sphere cutoff. A perturbation theory was developed, by which the thermodynamic properties of 1 system could be related to those of a slightly different system and to the difference in the intermol. potentials of the 2 systems. The unperturbed system was a rigid-sphere fluid, and the Lennard-Jones potential was the perturbation. The results were in fair agreement with expt. and can be used as an exptl. test of the theoretical rigid-sphere equation of state.
- 17Kollman, P. Free Energy Calculations: Applications to Chemical and Biochemical Phenomena. Chem. Rev. 1993, 93 (7), 2395– 2417, DOI: 10.1021/cr00023a004Google Scholar17https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK3sXmt1Sktr0%253D&md5=f12326b24734ea092996cf22efb6ebd6Free energy calculations: Applications to chemical and biochemical phenomenaKollman, PeterChemical Reviews (Washington, DC, United States) (1993), 93 (7), 2395-417CODEN: CHREAY; ISSN:0009-2665.A review with 252 refs. about applications of free energy calcns. employing mol. dynamics or Monte Carlo methods to a variety of chem. and biochem. phenomena. The focus is on applications of such calcns. to mol. solvation, mol. assocn., macromol. stability, and enzyme catalysis. The mols. discussed range from monovalent ions and small mols. to proteins and nucleic acids.
- 18Chen, D.; Ranganathan, A.; Ijzerman, A. P.; Siegal, G.; Carlsson, J. Complementarity between in silico and biophysical screening approaches in fragment-based lead discovery against the A2A adenosine receptor. J. Chem. Inf. Model. 2013, 53 (10), 2701– 2714, DOI: 10.1021/ci4003156Google Scholar18https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtlWqsr%252FN&md5=9000cbd70a73169e869ac789fdea88adComplementarity between in Silico and Biophysical Screening Approaches in Fragment-Based Lead Discovery against the A2A Adenosine ReceptorChen, Dan; Ranganathan, Anirudh; Ijzerman, Adriaan P.; Siegal, Gregg; Carlsson, JensJournal of Chemical Information and Modeling (2013), 53 (10), 2701-2714CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)Fragment-based lead discovery (FBLD) is becoming an increasingly important method in drug development. We have explored the potential to complement NMR-based biophys. screening of chem. libraries with mol. docking in FBLD against the A2A adenosine receptor (A2AAR), a drug target for inflammation and Parkinson's disease. Prior to an NMR-based screen of a fragment library against the A2AAR, mol. docking against a crystal structure was used to rank the same set of mols. by their predicted affinities. Mol. docking was able to predict four out of the five orthosteric ligands discovered by NMR among the top 5% of the ranked library, suggesting that structure-based methods could be used to prioritize among primary hits from biophys. screens. In addn., three fragments that were top-ranked by mol. docking, but had not been picked up by the NMR-based method, were demonstrated to be A2AAR ligands. While biophys. approaches for fragment screening are typically limited to a few thousand compds., the docking screen was extended to include 328,000 com. available fragments. Twenty-two top-ranked compds. were tested in radioligand binding assays, and 14 of these were A2AAR ligands with Ki values ranging from 2 to 240 μM. Optimization of fragments was guided by mol. dynamics simulations and free energy calcns. The results illuminate strengths and weaknesses of mol. docking and demonstrate that this method can serve as a valuable complementary tool to biophys. screening in FBLD.
- 19Matricon, P.; Ranganathan, A.; Warnick, E.; Gao, Z. G.; Rudling, A.; Lambertucci, C.; Marucci, G.; Ezzati, A.; Jaiteh, M.; Dal Ben, D. Fragment optimization for GPCRs by molecular dynamics free energy calculations: Probing druggable subpockets of the A 2A adenosine receptor binding site. Sci. Rep. 2017, 7 (1), 6398, DOI: 10.1038/s41598-017-04905-0Google Scholar19https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC1cfhtFKktQ%253D%253D&md5=05f0210d0cbe5f4cd58677b8747dffa0Fragment optimization for GPCRs by molecular dynamics free energy calculations: Probing druggable subpockets of the A 2A adenosine receptor binding siteMatricon Pierre; Jaiteh Mariama; Carlsson Jens; Ranganathan Anirudh; Rudling Axel; Ezzati Aitakin; Warnick Eugene; Gao Zhan-Guo; Jacobson Kenneth A; Lambertucci Catia; Marucci Gabriella; Dal Ben DiegoScientific reports (2017), 7 (1), 6398 ISSN:.Fragment-based lead discovery is becoming an increasingly popular strategy for drug discovery. Fragment screening identifies weakly binding compounds that require optimization to become high-affinity leads. As design of leads from fragments is challenging, reliable computational methods to guide optimization would be invaluable. We evaluated using molecular dynamics simulations and the free energy perturbation method (MD/FEP) in fragment optimization for the A2A adenosine receptor, a pharmaceutically relevant G protein-coupled receptor. Optimization of fragments exploring two binding site subpockets was probed by calculating relative binding affinities for 23 adenine derivatives, resulting in strong agreement with experimental data (R(2) = 0.78). The predictive power of MD/FEP was significantly better than that of an empirical scoring function. We also demonstrated the potential of the MD/FEP to assess multiple binding modes and to tailor the thermodynamic profile of ligands during optimization. Finally, MD/FEP was applied prospectively to optimize three nonpurine fragments, and predictions for 12 compounds were evaluated experimentally. The direction of the change in binding affinity was correctly predicted in a majority of the cases, and agreement with experiment could be improved with rigorous parameter derivation. The results suggest that MD/FEP will become a powerful tool in structure-driven optimization of fragments to lead candidates.
- 20Matricon, P.; Vo, D. D.; Gao, Z. G.; Kihlberg, J.; Jacobson, K. A.; Carlsson, J. Fragment-based design of selective GPCR ligands guided by free energy simulations. Chem. Commun. 2021, 57 (92), 12305– 12308, DOI: 10.1039/D1CC03202JGoogle Scholar20https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXitlWgtbfK&md5=fb089f3548bf70c040f7e5a2370cc817Fragment-based design of selective GPCR ligands guided by free energy simulationsMatricon, Pierre; Vo, Duc Duy; Gao, Zhan-Guo; Kihlberg, Jan; Jacobson, Kenneth A.; Carlsson, JensChemical Communications (Cambridge, United Kingdom) (2021), 57 (92), 12305-12308CODEN: CHCOFS; ISSN:1359-7345. (Royal Society of Chemistry)Fragment-based drug discovery relies on successful optimization of weakly binding ligands for affinity and selectivity. Herein, we explored strategies for structure-based evolution of fragments binding to a G protein-coupled receptor. Mol. dynamics simulations combined with rigorous free energy calcns. guided synthesis of nanomolar ligands with up to >1000-fold improvements of binding affinity and close to 40-fold subtype selectivity.
- 21Jespers, W.; Oliveira, A.; Prieto-Díaz, R.; Majellaro, M.; Åqvist, J.; Sotelo, E.; Gutiérrez-de-Terán, H. Structure-Based Design of Potent and Selective Ligands at the Four Adenosine Receptors. Molecules 2017, 22 (11), 1945, DOI: 10.3390/molecules22111945Google Scholar21https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXisVGjur8%253D&md5=b22386d14fb1d7d5d20bfa8346f8c67bStructure-based design of potent and selective ligands at the four adenosine receptorsJespers, Willem; Oliveira, Ana; Prieto-Diaz, Ruben; Majellaro, Maria; Aqvist, Johan; Sotelo, Eddy; Gutierrez-de-Teran, HugoMolecules (2017), 22 (11), 1945/1-1945/17CODEN: MOLEFW; ISSN:1420-3049. (MDPI AG)The four receptors that signal for adenosine, A1, A2A, A2B and A3 ARs, belong to the superfamily of G protein-coupled receptors (GPCRs). They mediate a no. of (patho)physiol. functions and have attracted the interest of the biopharmaceutical sector for decades as potential drug targets. The many crystal structures of the A2A, and lately the A1 ARs, allow for the use of advanced computational, structure-based ligand design methodologies. Over the last decade, we have assessed the efficient synthesis of novel ligands specifically addressed to each of the four ARs. We herein review and update the results of this program with particular focus on mol. dynamics (MD) and free energy perturbation (FEP) protocols. The first in silico mutagenesis on the A1AR here reported allows understanding the specificity and high affinity of the xanthine-antagonist 8-Cyclopentyl-1,3-dipropylxanthine (DPCPX). On the A2AAR, we demonstrate how FEP simulations can distinguish the conformational selectivity of a recent series of partial agonists. These novel results are complemented with the revision of the first series of enantiospecific antagonists on the A2BAR, and the use of FEP as a tool for bioisosteric design on the A3AR.
- 22Mallo-Abreu, A.; Prieto-Díaz, R.; Jespers, W.; Azuaje, J.; Majellaro, M.; Velando, C.; García-Mera, X.; Caamaño, O.; Brea, J.; Loza, M. I. Nitrogen-Walk Approach to Explore Bioisosteric Replacements in a Series of Potent A 2B Adenosine Receptor Antagonists. J. Med. Chem. 2020, 63 (14), 7721– 7739, DOI: 10.1021/acs.jmedchem.0c00564Google Scholar22https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXht1amu73E&md5=33fad82df198f133dad6b4d866d36b42A Nitrogen-Walk Approach to Explore Bioisosteric Replacements in a Series of Potent A2B Adenosine Receptor AntagonistsMallo-Abreu, Ana; Prieto-Diaz, Ruben; Jespers, Willem; Azuaje, Jhonny; Majellaro, Maria; Velando, Carmen; Garcia-Mera, Xerardo; Caamano, Olga; Brea, Jose; Loza, Maria I.; Gutierrez-de-Teran, Hugo; Sotelo, EddyJournal of Medicinal Chemistry (2020), 63 (14), 7721-7739CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)A systematic exploration of bioisosteric replacements for furan and thiophene cores in a series of potent A2BAR antagonists was carried out using the nitrogen-walk approach. A collection of 42 novel alkyl 4-substituted-2-methyl-1,4-dihydrobenzo[4,5]imidazo[1,2-a]pyrimidine-3-carboxylates I [R = H, cyclopentyl, Ph, etc.; R1 = Et, i-Pr], which contain 18 different pentagonal heterocyclic frameworks at position 4, was synthesized and evaluated. This study enabled the identication of new ligands that combine remarkable affinity (Ki < 30 nM) and exquisite selectivity. The SAR trends identified were substantiated by a mol. modeling study, based on a receptor-driven docking model and including a systematic free energy perturbation (FEP) study. Preliminary evaluation of the CYP3A4 and CYP2D6 inhibitory activity in optimized ligands evidenced weak and negligible activity resp. The stereospecific interaction between hA2BAR and the eutomer of the most attractive novel antagonist (S)-II (Ki = 3.66 nM) was validated.
- 23Decherchi, S.; Cavalli, A. Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation. Chem. Rev. 2020, 120 (23), 12788– 12833, DOI: 10.1021/acs.chemrev.0c00534Google Scholar23https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhvFKltrzF&md5=75c83143290022930959bdc1abaab8e9Thermodynamics and Kinetics of Drug-Target Binding by Molecular SimulationDecherchi, Sergio; Cavalli, AndreaChemical Reviews (Washington, DC, United States) (2020), 120 (23), 12788-12833CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)A review. Computational studies play an increasingly important role in chem. and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compds. by estg. their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estg. the rate coeffs. of very slow processes of pharmacol. interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered mol. dynamics and metadynamics. We review recent applications to pharmacol. and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to exptl. and clin. approaches.
- 24Lagarias, P.; Barkan, K.; Tzortzini, E.; Stampelou, M.; Vrontaki, E.; Ladds, G.; Kolocouris, A. Insights to the Binding of a Selective Adenosine A3 Receptor Antagonist Using Molecular Dynamic Simulations, MM-PBSA and MM-GBSA Free Energy Calculations, and Mutagenesis. J. Chem. Inf. Model. 2019, 59 (12), 5183– 5197, DOI: 10.1021/acs.jcim.9b00751Google Scholar24https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXitFGrsL7I&md5=3ad8c6ddc392513238915acb47a00fa0Insights to the Binding of a Selective Adenosine A3 Receptor Antagonist Using Molecular Dynamic Simulations, MM-PBSA and MM-GBSA Free Energy Calculations, and MutagenesisLagarias, Panagiotis; Barkan, Kerry; Tzortzini, Eva; Stampelou, Margarita; Vrontaki, Eleni; Ladds, Graham; Kolocouris, AntoniosJournal of Chemical Information and Modeling (2019), 59 (12), 5183-5197CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)Adenosine A3 receptor (A3R) is a promising drug target cancer and for a no. of other conditions like inflammatory diseases, including asthma and rheumatoid arthritis, glaucoma, chronic obstructive pulmonary disease, and ischemic injury. Currently, there is no exptl. detd. structure of A3R. We explored the binding profile of O4-{[3-(2,6-dichlorophenyl)-5-methylisoxazol-4-yl]carbonyl}-2-methyl-1,3-thiazole-4-carbohydroximamide (K18), which is a new specific and competitive antagonist at the orthosteric binding site of A3R. MD simulations and MM-GBSA calcns. of the WT A3R in complex with K18 combined with in vitro mutagenic studies show that the most plausible binding conformation for the dichlorophenyl group of K18 is oriented toward trans-membrane helixes (TM) 5, 6 and reveal important residues for binding. Further, MM-GBSA calcns. distinguish mutations that reduce or maintain or increase antagonistic activity. Our studies show that selectivity of K18 toward A3R is defined not only by direct interactions with residues within the orthosteric binding area but also by remote residues playing a significant role. Although V1695.30 is considered to be a selectivity filter for A3R binders, when it was mutated to glutamic acid, K18 maintained antagonistic potency, in agreement with our previous results obtained for agonists binding profile investigation. Mutation of the direct interacting residue L903.32 in the low region and the remote L2647.35 in the middle/upper region to alanine increases antagonistic potency, suggesting an empty space in the orthosteric area available for increasing antagonist potency. These results approve the computational model for the description of K18 binding at A3R, which we previously performed for agonists binding to A3R, and the design of more effective antagonists based on K18.
- 25Barkan, K.; Lagarias, P.; Stampelou, M.; Stamatis, D.; Hoare, S.; Safitri, D.; Klotz, K. N.; Vrontaki, E.; Kolocouris, A.; Ladds, G. Pharmacological characterisation of novel adenosine A3 receptor antagonists. Sci. Rep. 2020, 10 (1), 20781, DOI: 10.1038/s41598-020-74521-yGoogle Scholar25https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXisFWltbrL&md5=3710ac8acc7ee7d2b072c0ee95fa3bffPharmacological characterization of novel adenosine A3 receptor antagonistsBarkan, Kerry; Lagarias, Panagiotis; Stampelou, Margarita; Stamatis, Dimitrios; Hoare, Sam; Safitri, Dewi; Klotz, Karl-Norbert; Vrontaki, Eleni; Kolocouris, Antonios; Ladds, GrahamScientific Reports (2020), 10 (1), 20781CODEN: SRCEC3; ISSN:2045-2322. (Nature Research)The adenosine A3 receptor (A3R) belongs to a family of four adenosine receptor (AR) subtypes which all play distinct roles throughout the body. A3R antagonists have been described as potential treatments for numerous diseases including asthma. Given the similarity between (adenosine receptors) orthosteric binding sites, obtaining highly selective antagonists is a challenging but crit. task. Here we screen 39 potential A3R, antagonists using agonist-induced inhibition of cAMP. Pos. hits were assessed for AR subtype selectivity through cAMP accumulation assays. The antagonist affinity was detd. using Schild anal. (pA2 values) and fluorescent ligand binding. Structure-activity relationship investigations revealed that loss of the 3-(dichlorophenyl)-isoxazolyl moiety or the arom. nitrogen heterocycle with nitrogen at α-position to the carbon of carboximidamide group significantly attenuated K18 antagonistic potency. Mutagenic studies supported by mol. dynamic simulations combined with Mol. Mechanics-Poisson Boltzmann Surface Area calcns. identified the residues important for binding in the A3R orthosteric site. We demonstrate that K18, which contains a 3-(dichlorophenyl)-isoxazole group connected through carbonyloxycarboximidamide fragment with a 1,3-thiazole ring, is a specific A3R (< 1 μM) competitive antagonist. Finally, we introduce a model that enables ests. of the equil. binding affinity for rapidly disassocg. compds. from real-time fluorescent ligand-binding studies. These results demonstrate the pharmacol. characterization of a selective competitive A3R antagonist and the description of its orthosteric binding mode. Our findings may provide new insights for drug discovery.
- 26Guo, D.; Heitman, L. H.; Ijzerman, A. P. Kinetic Aspects of the Interaction between Ligand and G Protein-Coupled Receptor: The Case of the Adenosine Receptors. Chem. Rev. 2017, 117 (1), 38– 66, DOI: 10.1021/acs.chemrev.6b00025Google Scholar26https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XmtVymsro%253D&md5=1b2b1dd2404cc1c64c27f9f0fab9a6abKinetic Aspects of the Interaction between Ligand and G Protein-Coupled Receptor: The Case of the Adenosine ReceptorsGuo, Dong; Heitman, Laura H.; Ijzerman, Adriaan P.Chemical Reviews (Washington, DC, United States) (2017), 117 (1), 38-66CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)Ligand-receptor binding kinetics is an emerging topic in the drug research community. Over the past years, medicinal chem. approaches from a kinetic perspective have been increasingly applied to G protein-coupled receptors including the adenosine receptors (AR), which are involved in a plethora of physiol. and pathol. conditions. The study of ligand-AR binding kinetics offers room for detailed structure-kinetics relationships next to more traditional structure-activity relationships. Their combination may facilitate the triage of candidate compds. in hit-to-lead campaigns. Furthermore, kinetic studies also help in understanding AR allosterism. Allosteric modulation may yield a change in the activity and conformation of a receptor resulting from the binding of a compd. at a site distinct from where the endogenous agonist adenosine binds. Hence, in this Review, we summarize available data and evidence for the binding kinetics of orthosteric and allosteric AR ligands. We hope this Review will raise awareness to consider the kinetic aspects of drug-target interactions on both ARs and other drug targets.
- 27Kirkwood, J. G. Statistical mechanics of fluid mixtures. J. Chem. Phys. 1935, 3, 300– 313, DOI: 10.1063/1.1749657Google Scholar27https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaA2MXjt1OrsA%253D%253D&md5=224deb235ab0d87ec9adf44f983dc686Statistical mechanics of fluid mixturesKirkwood, John G.Journal of Chemical Physics (1935), 3 (), 300-13CODEN: JCPSA6; ISSN:0021-9606.Math. Expressions for the chem. potentials of the components of gas mixts. and liquid solns. are derived.
- 28Kollman, P. Free Energy Calculations: Applications to Chemical and Biochemical Phenomena. Chem. Rev. 1993, 93 (7), 2395– 2417, DOI: 10.1021/cr00023a004Google Scholar28https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK3sXmt1Sktr0%253D&md5=f12326b24734ea092996cf22efb6ebd6Free energy calculations: Applications to chemical and biochemical phenomenaKollman, PeterChemical Reviews (Washington, DC, United States) (1993), 93 (7), 2395-417CODEN: CHREAY; ISSN:0009-2665.A review with 252 refs. about applications of free energy calcns. employing mol. dynamics or Monte Carlo methods to a variety of chem. and biochem. phenomena. The focus is on applications of such calcns. to mol. solvation, mol. assocn., macromol. stability, and enzyme catalysis. The mols. discussed range from monovalent ions and small mols. to proteins and nucleic acids.
- 29Lenselink, E. B.; Louvel, J.; Forti, A. F.; van Veldhoven, J. P. D.; de Vries, H.; Mulder-Krieger, T.; McRobb, F. M.; Negri, A.; Goose, J.; Abel, R. Predicting Binding Affinities for GPCR Ligands Using Free-Energy Perturbation. ACS Omega 2016, 1 (2), 293– 304, DOI: 10.1021/acsomega.6b00086Google Scholar29https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhsVWrsLbE&md5=dc8c6c56657c64608b4c66e65b630413Predicting Binding Affinities for GPCR Ligands Using Free-Energy PerturbationLenselink, Eelke B.; Louvel, Julien; Forti, Anna F.; van Veldhoven, Jacobus P. D.; de Vries, Henk; Mulder-Krieger, Thea; McRobb, Fiona M.; Negri, Ana; Goose, Joseph; Abel, Robert; van Vlijmen, Herman W. T.; Wang, Lingle; Harder, Edward; Sherman, Woody; IJzerman, Adriaan P.; Beuming, ThijsACS Omega (2016), 1 (2), 293-304CODEN: ACSODF; ISSN:2470-1343. (American Chemical Society)The rapid growth of structural information for G-protein-coupled receptors (GPCRs) has led to a greater understanding of their structure, function, selectivity, and ligand binding. Although novel ligands have been identified using methods such as virtual screening, computationally driven lead optimization has been possible only in isolated cases because of challenges assocd. with predicting binding free energies for related compds. Here, the authors provide a systematic characterization of the performance of free-energy perturbation (FEP) calcns. to predict relative binding free energies of congeneric ligands binding to GPCR targets using a consistent protocol and no adjustable parameters. Using the FEP+ package, first the authors validated the protocol, which includes a full lipid bilayer and explicit solvent, by predicting the binding affinity for a total of 45 different ligands across four different GPCRs (adenosine A2AAR, β1 adrenergic, CXCR4 chemokine, and δ opioid receptors). Comparison with exptl. binding affinity measurements revealed a highly predictive ranking correlation (av. spearman ρ = 0.55) and low root-mean-square error (0.80 kcal/mol). Next, the authors applied FEP+ in a prospective project, where the authors predicted the affinity of novel, potent adenosine A2A receptor (A2AR) antagonists. Four novel compds. were synthesized and tested in a radioligand displacement assay, yielding affinity values in the nanomolar range. The affinity of two out of the four novel ligands (plus three previously reported compds.) was correctly predicted (within 1 kcal/mol), including one compd. with approx. a 10-fold increase in affinity compared to the starting compd. Detailed analyses of the simulations underlying the predictions provided insights into the structural basis for the two cases where the affinity was overpredicted. Taken together, these results establish a protocol for systematically applying FEP+ to GPCRs and provide guidelines for identifying potent mols. in drug discovery lead optimization projects.
- 30Deflorian, F.; Perez-Benito, L.; Lenselink, E. B.; Congreve, M.; van Vlijmen, H. W. T.; Mason, J. S.; Graaf, C. d.; Tresadern, G. Accurate Prediction of GPCR Ligand Binding Affinity with Free Energy Perturbation. J. Chem. Inf. Model. 2020, 60 (11), 5563– 5579, DOI: 10.1021/acs.jcim.0c00449Google Scholar30https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhtFKhtbbK&md5=a6dfdd7d6258b3d539d9d51596a238f0Accurate Prediction of GPCR Ligand Binding Affinity with Free Energy PerturbationDeflorian, Francesca; Perez-Benito, Laura; Lenselink, Eelke B.; Congreve, Miles; van Vlijmen, Herman W. T.; Mason, Jonathan S.; Graaf, Chris de; Tresadern, GaryJournal of Chemical Information and Modeling (2020), 60 (11), 5563-5579CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)The computational prediction of relative binding free energies is a crucial goal for drug discovery, and G protein-coupled receptors (GPCRs) are arguably the most important drug target class. However, they present increased complexity to model compared to sol. globular proteins. Despite breakthroughs, exptl. X-ray crystal and cryo-EM structures are challenging to attain, meaning computational models of the receptor and ligand binding mode are sometimes necessary. This leads to uncertainty in understanding ligand-protein binding induced changes such as, water positioning and displacement, side chain positioning, hydrogen bond networks, and the overall structure of the hydration shell around the ligand and protein. In other words, the very elements that define structure activity relationships (SARs) and are crucial for accurate binding free energy calcns. are typically more uncertain for GPCRs. In this work we use free energy perturbation (FEP) to predict the relative binding free energies for ligands of two different GPCRs. We pinpoint the key aspects for success such as the important role of key water mols., amino acid ionization states, and the benefit of equilibration with specific ligands. Initial calcns. following typical FEP setup and execution protocols delivered no correlation with expt., but we show how results are improved in a logical and systematic way. This approach gave, in the best cases, a coeff. of detn. (R2) compared with expt. in the range of 0.6-0.9 and mean unsigned errors compared to expt. of 0.6-0.7 kcal/mol. We anticipate that our findings will be applicable to other difficult-to-model protein ligand data sets and be of wide interest for the community to continue improving FE binding energy predictions.
- 31Wan, S.; Potterton, A.; Husseini, F. S.; Wright, D. W.; Heifetz, A.; Malawski, M.; Townsend-Nicholson, A.; Coveney, P. V. Hit-to-lead and lead optimization binding free energy calculations for G protein-coupled receptors. Interfaces: Focus 2020, 10 (6), 20190128, DOI: 10.1098/rsfs.2019.0128Google ScholarThere is no corresponding record for this reference.
- 32Stampelou, M.; Suchankova, A.; Tzortzini, E.; Dhingra, L.; Barkan, K.; Lougiakis, N.; Marakos, P.; Pouli, N.; Ladds, G.; Kolocouris, A. Dual A1/A3 Adenosine Receptor Antagonists: Binding Kinetics and Structure-Activity Relationship Studies Using Mutagenesis and Alchemical Binding Free Energy Calculations. J. Med. Chem. 2022, 65 (19), 13305– 13327, DOI: 10.1021/acs.jmedchem.2c01123Google Scholar32https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XisFShsbfL&md5=fd5d06c03cd10369c9de0b6aeac6c05eDual A1/A3 Adenosine Receptor Antagonists: Binding Kinetics and Structure-Activity Relationship Studies Using Mutagenesis and Alchemical Binding Free Energy CalculationsStampelou, Margarita; Suchankova, Anna; Tzortzini, Efpraxia; Dhingra, Lakshiv; Barkan, Kerry; Lougiakis, Nikolaos; Marakos, Panagiotis; Pouli, Nicole; Ladds, Graham; Kolocouris, AntoniosJournal of Medicinal Chemistry (2022), 65 (19), 13305-13327CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)Drugs targeting adenosine receptors (AR) can provide treatment for diseases. We report the identification of 7-(phenylamino)-pyrazolo[3,4-c]pyridines L2-L10, A15, and A17 as low-micromolar to low-nanomolar A1R/A3R dual antagonists, with 3-phenyl-5-cyano-7-(trimethoxyphenylamino)-pyrazolo[3,4-c]pyridine A17 displaying the highest affinity at both receptors with a long residence time of binding, as detd. using a NanoBRET-based assay. Two binding orientations of A17 (I) produce stable complexes inside the orthosteric binding area of A1R in mol. dynamics (MD) simulations, and we selected the most plausible orientation based on the agreement with alanine mutagenesis supported by affinity expts. Interestingly, for drug design purposes, the mutation of L2506.51 to alanine increased the binding affinity of A17 at A1R. We explored the structure-activity relationships against A1R using alchem. binding free energy calcns. with the thermodn. integration coupled with the MD simulation (TI/MD) method, applied on the whole G-protein-coupled receptor-membrane system, which showed a good agreement (r = 0.73) between calcd. and exptl. relative binding free energies.
- 33Pohorille, A.; Jarzynski, C.; Chipot, C. Good practices in free-energy calculations. J. Phys. Chem. B 2010, 114 (32), 10235– 10253, DOI: 10.1021/jp102971xGoogle Scholar33https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXptFOmtrc%253D&md5=4beae4323556ec59ab57177b6670303bGood Practices in Free-Energy CalculationsPohorille, Andrew; Jarzynski, Christopher; Chipot, ChristopheJournal of Physical Chemistry B (2010), 114 (32), 10235-10253CODEN: JPCBFK; ISSN:1520-6106. (American Chemical Society)As access to computational resources continues to increase, free-energy calcns. have emerged as a powerful tool that can play a predictive role in a wide range of research areas. Yet, the reliability of these calcns. can often be improved significantly if a no. of precepts, or good practices, are followed. Although the theory upon which these good practices rely has largely been known for many years, it is often overlooked or simply ignored. In other cases, the theor. developments are too recent for their potential to be fully grasped and merged into popular platforms for the computation of free-energy differences. In this contribution, the current best practices for carrying out free-energy calcns. using free energy perturbation and nonequil. work methods are discussed, demonstrating that at little to no addnl. cost, free-energy ests. could be markedly improved and bounded by meaningful error ests. Monitoring the probability distributions that underlie the transformation between the states of interest, performing the calcn. bidirectionally, stratifying the reaction pathway, and choosing the most appropriate paradigms and algorithms for transforming between states offer significant gains in both accuracy and precision.
- 34Mazziotta, C.; Rotondo, J. C.; Lanzillotti, C.; Campione, G.; Martini, F.; Tognon, M. Cancer biology and molecular genetics of A3 adenosine receptor. Oncogene 2022, 41 (3), 301– 308, DOI: 10.1038/s41388-021-02090-zGoogle Scholar34https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXisVegsbnP&md5=be04ab19e047a7af23667a44d0225a8eCancer biology and molecular genetics of A3 adenosine receptorMazziotta, Chiara; Rotondo, John Charles; Lanzillotti, Carmen; Campione, Giulia; Martini, Fernanda; Tognon, MauroOncogene (2022), 41 (3), 301-308CODEN: ONCNES; ISSN:0950-9232. (Nature Portfolio)A review. A3 adenosine receptor (A3AR) is a cell membrane protein, which has been found to be overexpressed in a large no. of cancer types. This receptor plays an important role in cancer by interacting with adenosine. Specifically, A3AR has a dual nature in different pathophysiol. conditions, as it is expressed according to tissue type and stimulated by an adenosine dose-dependent manner. A3AR activation leads to tumor growth, cell proliferation and survival in some cases, while triggering cytostatic and apoptotic pathways in others. This aims to describe the most relevant aspects of A3AR activation and its ligands whereas it summarizes A3AR activities in cancer. Progress in the field of A3AR modulators, with a potential therapeutic role in cancer treatment are reported, as well.
- 35Kalash, L.; Winfield, I.; Safitri, D.; Bermudez, M.; Carvalho, S.; Glen, R.; Ladds, G.; Bender, A. Structure-based identification of dual ligands at the A2AR and PDE10A with anti-proliferative effects in lung cancer cell-lines. J. Cheminf. 2021, 13 (1), 17, DOI: 10.1186/s13321-021-00492-5Google ScholarThere is no corresponding record for this reference.
- 36Maier, J. A.; Martinez, C.; Kasavajhala, K.; Wickstrom, L.; Hauser, K. E.; Simmerling, C. ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. J. Chem. Theory Comput. 2015, 11 (8), 3696– 3713, DOI: 10.1021/acs.jctc.5b00255Google Scholar36https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtFequ7rN&md5=7b803577b3b6912cc6750cfbd356596eff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SBMaier, James A.; Martinez, Carmenza; Kasavajhala, Koushik; Wickstrom, Lauren; Hauser, Kevin E.; Simmerling, CarlosJournal of Chemical Theory and Computation (2015), 11 (8), 3696-3713CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)Mol. mechanics is powerful for its speed in atomistic simulations, but an accurate force field is required. The Amber ff99SB force field improved protein secondary structure balance and dynamics from earlier force fields like ff99, but weaknesses in side chain rotamer and backbone secondary structure preferences have been identified. Here, we performed a complete refit of all amino acid side chain dihedral parameters, which had been carried over from ff94. The training set of conformations included multidimensional dihedral scans designed to improve transferability of the parameters. Improvement in all amino acids was obtained as compared to ff99SB. Parameters were also generated for alternate protonation states of ionizable side chains. Av. errors in relative energies of pairs of conformations were under 1.0 kcal/mol as compared to QM, reduced 35% from ff99SB. We also took the opportunity to make empirical adjustments to the protein backbone dihedral parameters as compared to ff99SB. Multiple small adjustments of φ and ψ parameters were tested against NMR scalar coupling data and secondary structure content for short peptides. The best results were obtained from a phys. motivated adjustment to the φ rotational profile that compensates for lack of ff99SB QM training data in the β-ppII transition region. Together, these backbone and side chain modifications (hereafter called ff14SB) not only better reproduced their benchmarks, but also improved secondary structure content in small peptides and reprodn. of NMR χ1 scalar coupling measurements for proteins in soln. We also discuss the Amber ff12SB parameter set, a preliminary version of ff14SB that includes most of its improvements.
- 37Kaminski, G. A.; Friesner, R. A.; Tirado-Rives, J.; Jorgensen, W. L. Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate Quantum Chemical Calculations on Peptides. J. Phys. Chem. B 2001, 105 (28), 6474– 6487, DOI: 10.1021/jp003919dGoogle Scholar37https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3MXislKhsLk%253D&md5=3ff059626977ee7f6342466f5820f5b7Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate Quantum Chemical Calculations on PeptidesKaminski, George A.; Friesner, Richard A.; Tirado-Rives, Julian; Jorgensen, William L.Journal of Physical Chemistry B (2001), 105 (28), 6474-6487CODEN: JPCBFK; ISSN:1089-5647. (American Chemical Society)We present results of improving the OPLS-AA force field for peptides by means of refitting the key Fourier torsional coeffs. The fitting technique combines using accurate ab initio data as the target, choosing an efficient fitting subspace of the whole potential-energy surface, and detg. wts. for each of the fitting points based on magnitudes of the potential-energy gradient. The av. energy RMS deviation from the LMP2/cc-pVTZ(-f)//HF/6-31G** data is reduced by ∼40% from 0.81 to 0.47 kcal/mol as a result of the fitting for the electrostatically uncharged dipeptides. Transferability of the parameters is demonstrated by using the same alanine dipeptide-fitted backbone torsional parameters for all of the other dipeptides (with the appropriate side-chain refitting) and the alanine tetrapeptide. Parameters of nonbonded interactions have also been refitted for the sulfur-contg. dipeptides (cysteine and methionine), and the validity of the new Coulombic charges and the van der Waals σ's and ε's is proved through reproducing gas-phase energies of complex formation heats of vaporization and densities of pure model liqs. Moreover, a novel approach to fitting torsional parameters for electrostatically charged mol. systems has been presented and successfully tested on five dipeptides with charged side chains.
- 38Genheden, S.; Ryde, U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discovery 2015, 10 (5), 449– 461, DOI: 10.1517/17460441.2015.1032936Google Scholar38https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXntFGktr8%253D&md5=b123b88809f275564f95a2271ebd159fThe MM/PBSA and MM/GBSA methods to estimate ligand-binding affinitiesGenheden, Samuel; Ryde, UlfExpert Opinion on Drug Discovery (2015), 10 (5), 449-461CODEN: EODDBX; ISSN:1746-0441. (Informa Healthcare)Introduction: The mol. mechanics energies combined with the Poisson-Boltzmann or generalized Born and surface area continuum solvation (MM/PBSA and MM/GBSA) methods are popular approaches to est. the free energy of the binding of small ligands to biol. macromols. They are typically based on mol. dynamics simulations of the receptor-ligand complex and are therefore intermediate in both accuracy and computational effort between empirical scoring and strict alchem. perturbation methods. They have been applied to a large no. of systems with varying success. Areas covered: The authors review the use of MM/PBSA and MM/GBSA methods to calc. ligand-binding affinities, with an emphasis on calibration, testing and validation, as well as attempts to improve the methods, rather than on specific applications. Expert opinion: MM/PBSA and MM/GBSA are attractive approaches owing to their modular nature and that they do not require calcns. on a training set. They have been used successfully to reproduce and rationalize exptl. findings and to improve the results of virtual screening and docking. However, they contain several crude and questionable approxns., for example, the lack of conformational entropy and information about the no. and free energy of water mols. in the binding site. Moreover, there are many variants of the method and their performance varies strongly with the tested system. Likewise, most attempts to ameliorate the methods with more accurate approaches, for example, quantum-mech. calcns., polarizable force fields or improved solvation have deteriorated the results.
- 39Tian, C.; Kasavajhala, K.; Belfon, K. A. A.; Raguette, L.; Huang, H.; Migues, A. N.; Bickel, J.; Wang, Y.; Pincay, J.; Wu, Q. ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution. J. Chem. Theory Comput. 2020, 16 (1), 528– 552, DOI: 10.1021/acs.jctc.9b00591Google Scholar39https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB3MjnvFGisw%253D%253D&md5=7cbaecffea06e4bf7ccecb7f1d0a0f4dff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in SolutionTian Chuan; Kasavajhala Koushik; Belfon Kellon A A; Raguette Lauren; Huang He; Bickel John; Wang Yuzhang; Pincay Jorge; Simmerling Carlos; Tian Chuan; Kasavajhala Koushik; Belfon Kellon A A; Raguette Lauren; Huang He; Migues Angela N; Wang Yuzhang; Simmerling Carlos; Wu QinJournal of chemical theory and computation (2020), 16 (1), 528-552 ISSN:.Molecular dynamics (MD) simulations have become increasingly popular in studying the motions and functions of biomolecules. The accuracy of the simulation, however, is highly determined by the molecular mechanics (MM) force field (FF), a set of functions with adjustable parameters to compute the potential energies from atomic positions. However, the overall quality of the FF, such as our previously published ff99SB and ff14SB, can be limited by assumptions that were made years ago. In the updated model presented here (ff19SB), we have significantly improved the backbone profiles for all 20 amino acids. We fit coupled φ/ψ parameters using 2D φ/ψ conformational scans for multiple amino acids, using as reference data the entire 2D quantum mechanics (QM) energy surface. We address the polarization inconsistency during dihedral parameter fitting by using both QM and MM in aqueous solution. Finally, we examine possible dependency of the backbone fitting on side chain rotamer. To extensively validate ff19SB parameters, and to compare to results using other Amber models, we have performed a total of ∼5 ms MD simulations in explicit solvent. Our results show that after amino-acid-specific training against QM data with solvent polarization, ff19SB not only reproduces the differences in amino-acid-specific Protein Data Bank (PDB) Ramachandran maps better but also shows significantly improved capability to differentiate amino-acid-dependent properties such as helical propensities. We also conclude that an inherent underestimation of helicity is present in ff14SB, which is (inexactly) compensated for by an increase in helical content driven by the TIP3P bias toward overly compact structures. In summary, ff19SB, when combined with a more accurate water model such as OPC, should have better predictive power for modeling sequence-specific behavior, protein mutations, and also rational protein design. Of the explicit water models tested here, we recommend use of OPC with ff19SB.
- 40Stampelou, M.; Ladds, G.; Kolocouris, A. Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A3 Receptor. J. Phys. Chem. B 2024, 128, 914– 936, DOI: 10.1021/acs.jpcb.3c05986Google ScholarThere is no corresponding record for this reference.
- 41Heo, L.; Feig, M. Multi-state modeling of G-protein coupled receptors at experimental accuracy. Proteins: Struct., Funct., Bioinf. 2022, 90 (11), 1873– 1885, DOI: 10.1002/prot.26382Google Scholar41https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38Xht12ltr7F&md5=a75564614a8a1be233a0269ed620efdaMulti-state modeling of G-protein coupled receptors at experimental accuracyHeo, Lim; Feig, MichaelProteins: Structure, Function, and Bioinformatics (2022), 90 (11), 1873-1885CODEN: PSFBAF; ISSN:1097-0134. (Wiley-Blackwell)The family of G-protein coupled receptors (GPCRs) is one of the largest protein families in the human genome. GPCRs transduct chem. signals from extracellular to intracellular regions via a conformational switch between active and inactive states upon ligand binding. While exptl. structures of GPCRs remain limited, high-accuracy computational predictions are now possible with AlphaFold2. However, AlphaFold2 only predicts one state and is biased toward either the active or inactive conformation depending on the GPCR class. Here, a multi-state prediction protocol is introduced that extends AlphaFold2 to predict either active or inactive states at very high accuracy using state-annotated templated GPCR databases. The predicted models accurately capture the main structural changes upon activation of the GPCR at the at. level. For most of the benchmarked GPCRs (10 out of 15), models in the active and inactive states were closer to their corresponding activation state structures. Median RMSDs of the transmembrane regions were 1.12 S and 1.41 S for the active and inactive state models, resp. The models were more suitable for protein-ligand docking than the original AlphaFold2 models and template-based models. Finally, our prediction protocol predicted accurate GPCR structures and GPCR-peptide complex structures in GPCR Dock 2021, a blind GPCR-ligand complex modeling competition. We expect that high accuracy GPCR models in both activation states will promote understanding in GPCR activation mechanisms and drug discovery for GPCRs. At the time, the new protocol paves the way towards capturing the dynamics of proteins at high-accuracy via machine-learning methods.
- 42Sala, D.; Hildebrand, P. W.; Meiler, J. Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties. Front Mol. Biosci. 2023, 10, 10, DOI: 10.3389/fmolb.2023.1121962Google ScholarThere is no corresponding record for this reference.
- 43Pándy-Szekeres, G.; Munk, C.; Tsonkov, T. M.; Mordalski, S.; Harpsøe, K.; Hauser, A. S.; Bojarski, A. J.; Gloriam, D. E. GPCRdb in 2018: adding GPCR structure models and ligands. Nucleic Acids Res. 2018, 46 (D1), D440– D446, DOI: 10.1093/nar/gkx1109Google Scholar43https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXitlGisLbE&md5=0cf77dd5ea934548a2d3a0ca69991f60GPCRdb in 2018: adding GPCR structure models and ligandsPandy-Szekeres, Gaspar; Munk, Christian; Tsonkov, Tsonko M.; Mordalski, Stefan; Harpsoee, Kasper; Hauser, Alexander S.; Bojarski, Andrzej J.; Gloriam, David E.Nucleic Acids Research (2018), 46 (D1), D440-D446CODEN: NARHAD; ISSN:1362-4962. (Oxford University Press)G protein-coupled receptors are the most abundant mediators of both human signaling processes and therapeutic effects. Herein, we report GPCRomewide homol. models of unprecedented quality, and roughly 150 000 GPCR ligands with data on biol. activities and com. availability. Based on the strategy of 'Less model - more Xtal', each model exploits both a main template and alternative local templates. This achieved higher similarity to new structures than any of the existing resources, and refined crystal structures with missing or distorted regions. Models are provided for inactive, intermediate and active states-except for classes C and F that so far only have inactive templates. The ligand database has sep. browsers for: (i) target selection by receptor, family or class, (ii) ligand filtering based on cross-expt. activities (min, max and mean) or chem. properties, (iii) ligand source data and (iv) com. availability. SMILES structures and activity spreadsheets can be downloaded for further processing. Furthermore, three recent landmark publications on GPCR drugs, G protein selectivity and genetic variants have been accompanied with resources that now let readers view and analyze the findings themselves in GPCRdb. Altogether, this update will enable scientific investigation for the wider GPCR community.
- 44Zoltewicz, J. A.; Deady, L. W. Quaternization of Heteroaromatic Compounds: Quantitative Aspects. Adv. Heterocycl. Chem. 1978, 22 (C), 71– 121, DOI: 10.1016/S0065-2725(08)60103-8Google ScholarThere is no corresponding record for this reference.
- 45Lane, B. S.; Sames, D. Direct C-H Bond Arylation: Selective Palladium-Catalyzed C2-Arylation of N-Substituted Indoles. Org. Lett. 2004, 6 (17), 2897– 2900, DOI: 10.1021/ol0490072Google Scholar45https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXmtVOju7w%253D&md5=a180b90aa1339c90bb4e436aea737e2cDirect C-H Bond Arylation: Selective Palladium-Catalyzed C2-Arylation of N-Substituted IndolesLane, Benjamin S.; Sames, DaliborOrganic Letters (2004), 6 (17), 2897-2900CODEN: ORLEF7; ISSN:1523-7060. (American Chemical Society)The authors present a new, practical method by which N-substituted indoles may be selectively arylated in the C2-position with good yields, low catalyst loadings, and a high degree of functional group tolerance. E.g., Pd(OAc)2/PPh3 in the presence of base CsOAc catalyzed the arylation of 1-methylindole by PhI to give 1-methyl-2-phenylindole. The investigation found that two competitive processes, namely, the desired cross-coupling and biphenyl formation, were operative in this reaction. A simple kinetic model was formulated that proved to be instructive and provided useful guidelines for reaction optimization; the approach described within may prove to be useful in other catalytic cross-coupling processes.
- 46Stoddart, L. A.; Kilpatrick, L. E.; Hill, S. J. NanoBRET Approaches to Study Ligand Binding to GPCRs and RTKs. Trends Pharmacol. Sci. 2018, 39, 136– 147, DOI: 10.1016/j.tips.2017.10.006Google Scholar46https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhslGisLfJ&md5=8452d68dea813ba950a195e7c11faca5NanoBRET Approaches to Study Ligand Binding to GPCRs and RTKsStoddart, Leigh A.; Kilpatrick, Laura E.; Hill, Stephen J.Trends in Pharmacological Sciences (2018), 39 (2), 136-147CODEN: TPHSDY; ISSN:0165-6147. (Elsevier Ltd.)Recent advances in the development of fluorescent ligands for G-protein-coupled receptors (GPCRs) and receptor tyrosine kinase receptors (RTKs) have facilitated the study of these receptors in living cells. A limitation of these ligands is potential uptake into cells and increased nonspecific binding. However, this can largely be overcome by using proximity approaches, such as bioluminescence resonance energy transfer (BRET), which localize the signal (within 10 nm) to the specific receptor target. The recent engineering of NanoLuc has resulted in a luciferase variant that is smaller and significantly brighter (up to tenfold) than existing variants. Here, we review the use of BRET from N-terminal NanoLuc-tagged GPCRs or a RTK to a receptor-bound fluorescent ligand to provide quant. pharmacol. of ligand-receptor interactions in living cells in real time.
- 47Huang, H.; Si, P.; Wang, L.; Xu, Y.; Xu, X.; Zhu, J.; Jiang, H.; Li, W.; Chen, L.; Li, J. Design, Synthesis, and Biological Evaluation of Novel Nonsteroidal Farnesoid X Receptor (FXR) Antagonists: Molecular Basis of FXR Antagonism. ChemMedChem 2015, 10 (7), 1184– 1199, DOI: 10.1002/cmdc.201500136Google ScholarThere is no corresponding record for this reference.
- 48Salvatore, C. A.; Jacobson, M. A.; Taylor, H. E.; Linden, J.; Johnson, R. G. Molecular cloning and characterization of the human A3 adenosine receptor. Proc. Natl. Acad. Sci. U.S.A. 1993, 90 (21), 10365– 10369, DOI: 10.1073/pnas.90.21.10365Google Scholar48https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2cXps1Gqtg%253D%253D&md5=7f663d6084176c057a12a655b2b6f157Molecular cloning and characterization of the human A3 adenosine receptorSalvatore, Christopher A.; Jacobson, Marlene A.; Taylor, Heidi E.; Linden, Joel; Johnson, Robert G.Proceedings of the National Academy of Sciences of the United States of America (1993), 90 (21), 10365-9CODEN: PNASA6; ISSN:0027-8424.The human A3 adenosine receptor was cloned from a striatal cDNA library using a probe derived from the homologous rat sequence. The cDNA encodes a protein of 318 amino acids and exhibits 72% and 85% overall identity with the rat and sheep A3 adenosine receptor sequences, resp. Specific and saturable binding of the adenosine receptor agonist N6-(4-amino-3-[125I]iodobenzyl)adenosine [125I]ABA was measured on the human A3 receptor stably expressed in Chinese hamster ovary cells with a Kd = 10 nM. The potency order for adenosine receptor agonists was N-ethylcarboxamidoadenosine (NECA) ≥ (R)-N6-phenyl-2-propyladenosine [(R)-PIA] > N6-cyclopentyladenosine (CPA) > (S)-N6-phenyl-2-propyladenosine [(S)-PIA]. The human receptor was blocked by xanthine antagonists, most potently by 3-(3-iodo-4-aminobenzyl)-8-(4-oxyacetate)phenyl-1-propylxanthine (I-ABOPX) with a potency order of I-ABOPX > 1,3-dipropyl-8-(4-acrylate)phenylxanthine ≥ xanthine amino congener >> 1,3-dipropyl-8-cyclopentylxanthine. Adenosine, NECA, (R)- and (S)-PIA, and CPA inhibited forskolin-stimulated cAMP accumulation by 30-40% in stably transfected cells; I-ABA is a partial agonist. When measured in the presence of antagonists, the dose-response curves of NECA-induced inhibition of forskolin-stimulated cAMP accumulation were right-shifted. Antagonist potencies detd. by Schild analyses correlated well with those established by competition for radioligand binding. The A3 adenosine receptor transcript is widespread and, in contrast to the A1, A2a, and A2b transcripts, the most abundant expression is found in the lung and liver. The tissue distribution of A3 mRNA is more similar to the widespread profile found in sheep than to the restricted profile found in the rat. This raises the possibility that numerous physiol. effects of adenosine may be mediated by A3 adenosine receptors.
- 49Stampelou, M.; Ladds, G.; Kolocouris, A. Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A3 Receptor. J. Phys. Chem. B 2024, 128 (4), 914– 936, DOI: 10.1021/acs.jpcb.3c05986Google ScholarThere is no corresponding record for this reference.
- 50Bailey, S.; Harris, M.; Barkan, K.; Winfield, I.; Harper, M. T.; Simms, J.; Ladds, G.; Wheatley, M.; Poyner, D. Interactions between RAMP2 and CRF receptors: The effect of receptor subtypes, splice variants and cell context. Biochim. Biophys. Acta, Biomembr. 2019, 1861 (5), 997– 1003, DOI: 10.1016/j.bbamem.2019.02.008Google ScholarThere is no corresponding record for this reference.
- 51Mackie, D. I.; Nielsen, N. R.; Harris, M.; Singh, S.; Davis, R. B.; Dy, D.; Ladds, G.; Caron, K. M. RAMP3 determines rapid recycling of atypical chemokine receptor-3 for guided angiogenesis. Proc. Natl. Acad. Sci. U.S.A. 2019, 116 (48), 24093– 24099, DOI: 10.1073/pnas.1905561116Google Scholar51https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXit1OktrfO&md5=6442eeb7c9b5b3af292e9578babdb0caRAMP3 determines rapid recycling of atypical chemokine receptor-3 for guided angiogenesisMackie, Duncan I.; Nielsen, Natalie R.; Harris, Matthew; Singh, Smriti; Davis, Reema B.; Dy, Danica; Ladds, Graham; Caron, Kathleen M.Proceedings of the National Academy of Sciences of the United States of America (2019), 116 (48), 24093-24099CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)Receptor-activity-modifying proteins (RAMPs) are single transmembrane-spanning proteins which serve as mol. chaperones and allosteric modulators of G-protein-coupled receptors (GPCRs) and their signaling pathways. Although RAMPs have been previously studied in the context of their effects on Family B GPCRs, the coevolution of RAMPs with many GPCR families suggests an expanded repertoire of potential interactions. Using bioluminescence resonance energy transfer-based and cell-surface expression approaches, we comprehensively screen for RAMP interactions within the chemokine receptor family and identify robust interactions between RAMPs and nearly all chemokine receptors. Most notably, we identify robust RAMP interaction with atypical chemokine receptors (ACKRs), which function to establish chemotactic gradients for directed cell migration. Specifically, RAMP3 assocn. with atypical chemokine receptor 3 (ACKR3) diminishes adrenomedullin (AM) ligand availability without changing G-protein coupling. Instead, RAMP3 is required for the rapid recycling of ACKR3 to the plasma membrane through Rab4-pos. vesicles following either AM or SDF-1/CXCL12 binding, thereby enabling formation of dynamic spatiotemporal chemotactic gradients. Consequently, genetic deletion of either ACKR3 or RAMP3 in mice abolishes directed cell migration of retinal angiogenesis. Thus, RAMP assocn. with chemokine receptor family members represents a mol. interaction to control receptor signaling and trafficking properties.
- 52Stamatis, D.; Lagarias, P.; Barkan, K.; Vrontaki, E.; Ladds, G.; Kolocouris, A. Structural Characterization of Agonist Binding to an A 3 Adenosine Receptor through Biomolecular Simulations and Mutagenesis Experiments. J. Med. Chem. 2019, 62 (19), 8831– 8846, DOI: 10.1021/acs.jmedchem.9b01164Google ScholarThere is no corresponding record for this reference.
- 53Gero, A.; Markham, J. J. Studies on Pyridines: I. The Basicity of Pyridine Bases. J. Org. Chem. 1951, 16 (12), 1835– 1838, DOI: 10.1021/jo50006a001Google Scholar53https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaG38XlsVKisg%253D%253D&md5=dfc4770105c9426c9fdd560e1753df46Pyridines. I. The basicity of pyridine basesGero, Alexander; Markham, James J.Journal of Organic Chemistry (1951), 16 (), 1835-8CODEN: JOCEAH; ISSN:0022-3263.The basicity of methylpyridines has been detd. Detns. of the ionization consts. of C5H5N and some of its homologs, with special precautions to operate with pure bases and to eliminate the CO2 error, give the following pKA and KB values, for C5H5N, 5.23, 1.7 × 10-9; 2-picoline, 5.96, 9.1 × 10-9; 4-picoline, 6.05, 1.1 × 10-8; 2,6-lutidine, 6.62, 4.2 × 10-8; 2,4-lutidine, 6.79, 6.1 × 10-8; 2,4,6-collidine, 7.45, 2.8 × 10-7. Plotting the pKA values against the no. of Me groups gives a straight line. Deviations of individual points from this line indicate that each Me group in an α-position increases the pKA of C5H5N by 0.73, each Me group in a γ-position by 0.82, but 2 Me groups in α-positions increase it by only 2 × 0.73-0.06. These results are attributed to steric hindrance.
- 54Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169–409X(96)00423–1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25. 1. Adv. Drug Delivery Rev. 2001, 46 (1–3), 3– 26, DOI: 10.1016/S0169-409X(00)00129-0Google Scholar54https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3MXitVOhs7o%253D&md5=c60bb89da68f051c0ee7ac4c0468a0e4Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settingsLipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J.Advanced Drug Delivery Reviews (2001), 46 (1-3), 3-26CODEN: ADDREP; ISSN:0169-409X. (Elsevier Science Ireland Ltd.)A review with 50 refs. Exptl. and computational approaches to est. soly. and permeability in discovery and development settings are described. In the discovery setting 'the rule of 5' predicts that poor absorption or permeation is more likely when there are more than 5 H-bond donors, 10 H-bond acceptors, the mol. wt. (MWT) is greater than 500 and the calcd. Log P (CLogP) is greater than 5 (or MlogP >4.15). Computational methodol. for the rule-based Moriguchi Log P (MLogP) calcn. is described. Turbidimetric soly. measurement is described and applied to known drugs. High throughput screening (HTS) leads tend to have higher MWT and Log P and lower turbidimetric soly. than leads in the pre-HTS era. In the development setting, soly. calcns. focus on exact value prediction and are difficult because of polymorphism. Recent work on linear free energy relationships and Log P approaches are critically reviewed. Useful predictions are possible in closely related analog series when coupled with exptl. thermodn. soly. measurements.
- 55Banker, M. J.; Clark, T. H.; Williams, J. A. Development and validation of a 96-well equilibrium dialysis apparatus for measuring plasma protein binding. J. Pharm. Sci. 2003, 92 (5), 967– 974, DOI: 10.1002/jps.10332Google Scholar55https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3sXjs1ehsrs%253D&md5=0948f0a11373c57be06be9e0d604ad40Development and validation of a 96-well equilibrium dialysis apparatus for measuring plasma protein bindingBanker, Michael J.; Clark, Tracey H.; Williams, John A.Journal of Pharmaceutical Sciences (2003), 92 (5), 967-974CODEN: JPMSAE; ISSN:0022-3549. (Wiley-Liss, Inc.)A 96-well equil. dialysis block was designed and constructed that is compatible with most std. 96-well format lab. supplies and instruments. The unique design of the dialysis app. allows one to dispense and aspirate from either or both the sample and dialyzate sides from the top of the app., which is not possible with systems currently on the market. This feature permits the investigator to analyze a large no. of samples, time points, or replicates in the same expt. The novel alignment of the dialysis membrane vertically in the well maximizes the surface-to-vol. ratio, eliminates problems assocd. with trapped air pockets, and allows one to add or remove samples independently or all at once. Furthermore, the design of the app. allows both the sample and dialyzate sides of the dialysis well to be accessible by robotic systems, so assays can be readily automated. Teflon construction is used to minimize nonspecific binding of test samples to the app. The device is reusable, easily assembled, and can be shaken in controlled temp. environments to decrease the time required to reach equil. as well as facilitate dissoln. of test compds. Plasma protein binding values obtained for 10 diverse compds. using std. dialysis equipment and the 96-well dialysis block validates this method.
- 56Hidalgo, I. J.; Raub, T. J.; Borchardt, R. T. Characterization of the human colon carcinoma cell line (Caco-2) as a model system for intestinal epithelial permeability. Gastroenterology 1989, 96 (3), 736– 749, DOI: 10.1016/0016-5085(89)90897-4Google Scholar56https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADyaL1M7itFeqsg%253D%253D&md5=59ffccc1dd4dccb01400c05df95ac1ffCharacterization of the human colon carcinoma cell line (Caco-2) as a model system for intestinal epithelial permeabilityHidalgo I J; Raub T J; Borchardt R TGastroenterology (1989), 96 (3), 736-49 ISSN:0016-5085.Caco-2 cells develop morphologic characteristics of normal enterocytes when grown on plastic dishes or nitrocellulose filters. The purpose of this study was to determine whether Caco-2 cells undergo similar differentiation when grown on Transwell polycarbonate membranes, and to study the suitability of Caco-2 monolayers as an intestinal epithelial transport model system. Transepithelial electrical resistance values after confluence were 173.5 omega.cm2 and remained unchanged through day 17. Permeabilities to the water-soluble fluid-phase markers that do not permeate the membrane, Lucifer yellow CH, [14C]inulin, [14C]polyethylene glycol, and [3H] dextran were less than 0.25% of the administered amount per hour after day 10. Qualitative evaluation of uptake and permeability to horseradish peroxidase confirmed the similarity in uptake and barrier properties between this cell system and the small intestinal epithelial layer. We conclude that Caco-2 cells grown on collagen-coated polycarbonate membranes should represent a valuable transport model system for the small intestinal epithelium.
- 57Obach, R. S.; Baxter, J. G.; Liston, T. E. The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. J. Pharmacol. Exp. Ther. 1997, 283 (1), 46– 58Google Scholar57https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2sXmslCltr0%253D&md5=68d1910d925e26ec4a8ef23043ffb1edThe prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism dataObach, R. Scott; Baxter, James G.; Liston, Theodore E.; Silber, B. Michael; Jones, Barry C.; Macintyre, Flona; Rance, David J.; Wastall, PhilipJournal of Pharmacology and Experimental Therapeutics (1997), 283 (1), 46-58CODEN: JPETAB; ISSN:0022-3565. (Williams & Wilkins)A review with 35 refs. We describe a comprehensive retrospective anal. in which the abilities of several methods by which human pharmacokinetic parameters are predicted from preclin. pharmacokinetic data and/or in vitro metab. data were assessed. The prediction methods examd. included both methods from the scientific literature as well as some described in this report for the first time. Four methods were examd. for their ability to predict human vol. of distribution. Three were highly predictive, yielding, on av., predictions that were within 60% to 90% of actual values. Twelve methods were assessed for their utility in predicting clearance. The most successful allometric scaling method yielded clearance predictions that were, on av., within 80% of actual values. The best methods in which in vitro metab. data from human liver microsomes were scaled to in vivo clearance values yielded predicted clearance values that were, on av., within 70% to 80% of actual values. Human t1/2 was predicted by combining predictions of human vol. of distribution and clearance. The best t1/2 prediction methods successfully assigned compds. to appropriate dosing regimen categories (e.g., once daily, twice daily and so forth) 70% to 80% of the time. In addn., correlations between human t1/2 and t1/2 values from preclin. species were also generally successful (72-87%) when used to predict human dosing regimens. In summary, this retrospective anal. has identified several approaches by which human pharmacokinetic data can be predicted from preclin. data. Such approaches should find utility in the drug discovery and development processes in the identification and selection of compds. that will possess appropriate pharmacokinetic characteristics in humans for progression to clin. trials.
- 58Yung-Chi, C.; Prusoff, W. H. Relationship between the inhibition constant and the concentration of inhbitor which causes 50% inhibition of an enzymatic reaction. Biochem. Pharmacol. 1973, 22 (23), 3099– 3108, DOI: 10.1016/0006-2952(73)90196-2Google ScholarThere is no corresponding record for this reference.
- 59Motulsky, H. J.; Mahan, L. C. The kinetics of competitive radioligand binding predicted by the law of mass action. Mol. Pharmacol. 1984, 25 (1), 1– 9Google Scholar59https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADyaL2c7mslOmtQ%253D%253D&md5=f0c09a16d7f8947fe0d72c256e2e4914The kinetics of competitive radioligand binding predicted by the law of mass actionMotulsky H J; Mahan L CMolecular pharmacology (1984), 25 (1), 1-9 ISSN:0026-895X.Although equilibrium competitive radioligand binding studies are often used to characterize hormone and neurotransmitter receptors, the kinetics of such experiments have not been extensively explored. The interactions of the radioligand and competitor with the receptors can be described by two differential equations which can be solved to yield a single equation describing the binding of the radioligand as a function of time. This equation has several applications: First, it can be used to simulate competitive binding reactions under defined conditions. Second, fitting experimental data to this equation allows one to determine the association and dissociation rate constants of the competing ligand, parameters that cannot be derived from equilibrium experiments. Furthermore, this method can be used to determine the KI of the competing drug from data acquired before equilibrium is reached. Third, mathematical analysis of the binding equation allowed us to answer two specific questions regarding the kinetics of competitive radioligand binding: how long such an incubation takes to equilibrate, and how the IC50 varies over time. The answers to these questions depended, to a large extent, on the relative values of the dissociation rate constants of the radioligand and competitor, which can be determined as noted above. When the competitor dissociates from the receptors more rapidly than the radioligand, the IC50 first decreases and then increases, but never has a value less than the KI. At low radioligand concentrations, equilibrium is reached in the same amount of time required of the radioligand to dissociate completely from the receptors as determined in an "off-rate experiment." At higher concentrations of radioligand this time is halved. When the competitor dissociates from the receptor more slowly than does the radioligand, then the time required to equilibrate depends only on the dissociation rate constant of the competitor, and the IC50 decreases over time.
- 60Curtis, M. J.; Alexander, S.; Cirino, G.; Docherty, J. R.; George, C. H.; Giembycz, M. A.; Hoyer, D.; Insel, P. A.; Izzo, A. A.; Ji, Y. Experimental design and analysis and their reporting II: updated and simplified guidance for authors and peer reviewers. Br. J. Pharmacol. 2018, 175 (7), 987– 993, DOI: 10.1111/bph.14153Google Scholar60https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXktV2ntbs%253D&md5=4dee1eb4b76fe4aa368798bc9ec9087aExperimental design and analysis and their reporting II: updated and simplified guidance for authors and peer reviewersCurtis, Michael J.; Alexander, Steve; Cirino, Giuseppe; Docherty, James R.; George, Christopher H.; Giembycz, Mark A.; Hoyer, Daniel; Insel, Paul A.; Izzo, Angelo A.; Ji, Yong; MacEwan, David J.; Sobey, Christopher G.; Stanford, S. Clare; Teixeira, Mauro M.; Wonnacott, Sue; Ahluwalia, AmritaBritish Journal of Pharmacology (2018), 175 (7), 987-993CODEN: BJPCBM; ISSN:1476-5381. (Wiley-Blackwell)This article updates the guidance published in 2015 for authors submitting papers to British Journal of Pharmacol. (Curtis et al., 2015) and is intended to provide the rubric for peer review. Thus, it is directed towards authors, reviewers and editors. Explanations for many of the requirements were outlined previously and are not restated here. The new guidelines are intended to replace those published previously. The guidelines have been simplified for ease of understanding by authors, to make it more straightforward for peer reviewers to check compliance and to facilitate the curation of the journal's efforts to improve stds.
- 61Ballesteros, J. A.; Weinstein, H. Analysis and refinement of criteria for predicting the structure and relative orientations of transmembranal helical domains. Biophys. J. 1992, 62 (1), 107– 109, DOI: 10.1016/S0006-3495(92)81794-0Google Scholar61https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK38Xkt1arsbw%253D&md5=fc48e6b739613fb0b8afac61934d1e89Analysis and refinement of criteria for predicting the structure and relative orientations of transmembranal helical domainsBallesteros, Juan A.; Weinstein, HarelBiophysical Journal (1992), 62 (1), 107-9CODEN: BIOJAU; ISSN:0006-3495.Methods used currently in the construction of helical transmembrane domains could be misleading if used indiscriminately. These methods include the hydrophobicity profile, the hydrophobic moment, helix amphiphilicity, and charge neutralization. A refinement is proposed here, based on empirical observations, mol. modeling, and physicochem. considerations designed to overcome some of the shortcomings inherent in the use of the above mentioned methods. Here the anal. of two of the motifs identified in the study that led to the proposed refinements is presented: the distribution of acidic and basic residues in the transmembranal domains, and the kink induced by a proline residue in an α-helix.
- 62Yaziji, V.; Rodríguez, D.; Gutiérrez-De-Terán, H.; Coelho, A.; Caamaño, O.; García-Mera, X.; Brea, J.; Loza, M. I.; Cadavid, M. I.; Sotelo, E. Pyrimidine derivatives as potent and selective A3 adenosine receptor antagonists. J. Med. Chem. 2011, 54 (2), 457– 471, DOI: 10.1021/jm100843zGoogle Scholar62https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXhs1Wgt7nP&md5=9a904325839c3908780cde73487ba91bPyrimidine derivatives as potent and selective A3 adenosine receptor antagonistsYaziji, Vicente; Rodriguez, David; Gutierrez-de-Teran, Hugo; Coelho, Alberto; Caamano, Olga; Garcia-Mera, Xerardo; Brea, Jose; Loza, Maria Isabel; Cadavid, Maria Isabel; Sotelo, EddyJournal of Medicinal Chemistry (2011), 54 (2), 457-471CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)Two regioisomeric series of diaryl 2- or 4-amidopyrimidines e. g. I, II have been synthesized and their adenosine receptor affinities were detd. in radioligand binding assays at the four human adenosine receptors (hARs). Some of the ligands prepd. herein exhibit remarkable affinities (Ki < 10 nm) and, most noticeably, the absence of activity at the A1, A2A, and A2B receptors. The structural determinants that support the affinity and selectivity profiles of the series were highlighted through an integrated computational approach, combining a 3D-QSAR model built on the second generation of GRid Independent Descriptors (GRIND2) with a novel homol. model of the hA3 receptor. The robustness of the computational model was subsequently evaluated by the design of new derivs. exploring the alkyl substituent of the exocyclic amide group. The synthesis and evaluation of the novel compds. validated the predictive power of the model, exhibiting excellent agreement between predicted and exptl. activities.
- 63Jaakola, V. P.; Griffith, M. T.; Hanson, M. A.; Cherezov, V.; Chien, E. Y. T.; Lane, J. R.; Ijzerman, A. P.; Stevens, R. C. The 2.6 Angstrom Crystal Structure of a Human A2A Adenosine Receptor Bound to an Antagonist. Science 2008, 322 (5905), 1211– 1217, DOI: 10.1126/science.1164772Google Scholar63https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXhtlyqtbfN&md5=5bdb862b41f345c244f3c162e058206bThe 2.6 Angstrom Crystal Structure of a Human A2A Adenosine Receptor Bound to an AntagonistJaakola, Veli-Pekka; Griffith, Mark T.; Hanson, Michael A.; Cherezov, Vadim; Chien, Ellen Y. T.; Lane, J. Robert; IJzerman, Adriaan P.; Stevens, Raymond C.Science (Washington, DC, United States) (2008), 322 (5905), 1211-1217CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)The adenosine class of heterotrimeric guanine nucleotide-binding protein (G protein)-coupled receptors (GPCRs) mediates the important role of extracellular adenosine in many physiol. processes and is antagonized by caffeine. The authors have detd. the crystal structure of the human A2A adenosine receptor, in complex with a high-affinity subtype-selective antagonist, ZM241385, to 2.6 angstrom resoln. Four disulfide bridges in the extracellular domain, combined with a subtle repacking of the transmembrane helixes relative to the adrenergic and rhodopsin receptor structures, define a pocket distinct from that of other structurally detd. GPCRs. The arrangement allows for the binding of the antagonist in an extended conformation, perpendicular to the membrane plane. The binding site highlights an integral role for the extracellular loops, together with the helical core, in ligand recognition by this class of GPCRs and suggests a role for ZM241385 in restricting the movement of a tryptophan residue important in the activation mechanism of the class A receptors.
- 64Ballesteros, J. A.; Weinstein, H. [19] Integrated methods for the construction of three-dimensional models and computational probing of structure-function relations in G protein-coupled receptors. Receptor Molecular Biol. 1995, 25, 366– 428, DOI: 10.1016/S1043-9471(05)80049-7Google ScholarThere is no corresponding record for this reference.
- 65Sastry, G. M.; Adzhigirey, M.; Day, T.; Annabhimoju, R.; Sherman, W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des. 2013, 27 (3), 221– 234, DOI: 10.1007/s10822-013-9644-8Google Scholar65https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3srksFekug%253D%253D&md5=9eb1a820e121aca2742ed53f24481aceProtein and ligand preparation: parameters, protocols, and influence on virtual screening enrichmentsSastry G Madhavi; Adzhigirey Matvey; Day Tyler; Annabhimoju Ramakrishna; Sherman WoodyJournal of computer-aided molecular design (2013), 27 (3), 221-34 ISSN:.Structure-based virtual screening plays an important role in drug discovery and complements other screening approaches. In general, protein crystal structures are prepared prior to docking in order to add hydrogen atoms, optimize hydrogen bonds, remove atomic clashes, and perform other operations that are not part of the x-ray crystal structure refinement process. In addition, ligands must be prepared to create 3-dimensional geometries, assign proper bond orders, and generate accessible tautomer and ionization states prior to virtual screening. While the prerequisite for proper system preparation is generally accepted in the field, an extensive study of the preparation steps and their effect on virtual screening enrichments has not been performed. In this work, we systematically explore each of the steps involved in preparing a system for virtual screening. We first explore a large number of parameters using the Glide validation set of 36 crystal structures and 1,000 decoys. We then apply a subset of protocols to the DUD database. We show that database enrichment is improved with proper preparation and that neglecting certain steps of the preparation process produces a systematic degradation in enrichments, which can be large for some targets. We provide examples illustrating the structural changes introduced by the preparation that impact database enrichment. While the work presented here was performed with the Protein Preparation Wizard and Glide, the insights and guidance are expected to be generalizable to structure-based virtual screening with other docking methods.
- 66Lomize, M. A.; Pogozheva, I. D.; Joo, H.; Mosberg, H. I.; Lomize, A. L. OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Res. 2012, 40 (D1), D370– D376, DOI: 10.1093/nar/gkr703Google Scholar66https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhs12hurzJ&md5=b931126c56d227ab0eb6c5762c1dae6bOPM database and PPM web server: resources for positioning of proteins in membranesLomize, Mikhail A.; Pogozheva, Irina D.; Joo, Hyeon; Mosberg, Henry I.; Lomize, Andrei L.Nucleic Acids Research (2012), 40 (D1), D370-D376CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)The Orientations of Proteins in Membranes (OPM) database is a curated web resource that provides spatial positions of membrane-bound peptides and proteins of known three-dimensional structure in the lipid bilayer, together with their structural classification, topol. and intracellular localization. OPM currently contains more than 1200 transmembrane and peripheral proteins and peptides from approx. 350 organisms that represent approx. 3800 Protein Data Bank entries. Proteins are classified into classes, superfamilies and families and assigned to 21 distinct membrane types. Spatial positions of proteins with respect to the lipid bilayer are optimized by the PPM 2.0 method that accounts for the hydrophobic, hydrogen bonding and electrostatic interactions of the proteins with the anisotropic water-lipid environment described by the dielec. const. and hydrogen-bonding profiles. The OPM database is freely accessible at http://opm.phar.umich.edu. Data can be sorted, searched or retrieved using the hierarchical classification, source organism, localization in different types of membranes. The database offers downloadable coordinates of proteins and peptides with membrane boundaries. A gallery of protein images and several visualization tools are provided. The database is supplemented by the PPM server (http://opm.phar.umich.edu/server.php) which can be used for calcg. spatial positions in membranes of newly detd. proteins structures or theor. models.
- 67Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 1983, 79 (2), 926– 935, DOI: 10.1063/1.445869Google Scholar67https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL3sXksF2htL4%253D&md5=a1161334e381746be8c9b15a5e56f704Comparison of simple potential functions for simulating liquid waterJorgensen, William L.; Chandrasekhar, Jayaraman; Madura, Jeffry D.; Impey, Roger W.; Klein, Michael L.Journal of Chemical Physics (1983), 79 (2), 926-35CODEN: JCPSA6; ISSN:0021-9606.Classical Monte Carlo simulations were carried out for liq. H2O in the NPT ensemble at 25° and 1 atm using 6 of the simpler intermol. potential functions for the dimer. Comparisons were made with exptl. thermodn. and structural data including the neutron diffraction results of Thiessen and Narten (1982). The computed densities and potential energies agree with expt. except for the original Bernal-Fowler model, which yields an 18% overest. of the d. and poor structural results. The discrepancy may be due to the correction terms needed in processing the neutron data or to an effect uniformly neglected in the computations. Comparisons were made for the self-diffusion coeffs. obtained from mol. dynamics simulations.
- 68Dickson, C. J.; Walker, R. C.; Gould, I. R. Lipid21: Complex Lipid Membrane Simulations with AMBER. J. Chem. Theory Comput. 2022, 18 (3), 1726– 1736, DOI: 10.1021/acs.jctc.1c01217Google Scholar68https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38Xis1ymtLg%253D&md5=d6d7877157017f4853fc6dbe8d8987b5Lipid21: Complex Lipid Membrane Simulations with AMBERDickson, Callum J.; Walker, Ross C.; Gould, Ian R.Journal of Chemical Theory and Computation (2022), 18 (3), 1726-1736CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)We extend the modular AMBER lipid force field to include anionic lipids, polyunsatd. fatty acid (PUFA) lipids, and sphingomyelin, allowing the simulation of realistic cell membrane lipid compns., including raft-like domains. Head group torsion parameters are revised, resulting in improved agreement with NMR order parameters, and hydrocarbon chain parameters are updated, providing a better match with phase transition temp. Extensive validation runs (0.9μs per lipid type) show good agreement with exptl. measurements. Furthermore, the simulation of raft-like bilayers demonstrates the perturbing effect of increasing PUFA concns. on cholesterol mols. The force field derivation is consistent with the AMBER philosophy, meaning it can be easily mixed with protein, small mol., nucleic acid, and carbohydrate force fields.
- 69He, X.; Man, V. H.; Yang, W.; Lee, T. S.; Wang, J. A fast and high-quality charge model for the next generation general AMBER force field. J. Chem. Phys. 2020, 153(11). DOI: 10.1063/5.0019056 .Google ScholarThere is no corresponding record for this reference.
- 70Joung, I. S.; Cheatham, T. E. Determination of Alkali and Halide Monovalent Ion Parameters for Use in Explicitly Solvated Biomolecular Simulations. J. Phys. Chem. B 2008, 112 (30), 9020– 9041, DOI: 10.1021/jp8001614Google Scholar70https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXnvFGqtL4%253D&md5=aa489470ae1c7479bf0911710217bd28Determination of Alkali and Halide Monovalent Ion Parameters for Use in Explicitly Solvated Biomolecular SimulationsJoung, In Suk; Cheatham, Thomas E.Journal of Physical Chemistry B (2008), 112 (30), 9020-9041CODEN: JPCBFK; ISSN:1520-6106. (American Chemical Society)Alkali (Li+, Na+, K+, Rb+, and Cs+) and halide (F-, Cl-, Br-, and I-) ions play an important role in many biol. phenomena, roles that range from stabilization of biomol. structure, to influence on biomol. dynamics, to key physiol. influence on homeostasis and signaling. To properly model ionic interaction and stability in atomistic simulations of biomol. structure, dynamics, folding, catalysis, and function, an accurate model or representation of the monovalent ions is critically necessary. A good model needs to simultaneously reproduce many properties of ions, including their structure, dynamics, solvation, and moreover both the interactions of these ions with each other in the crystal and in soln. and the interactions of ions with other mols. At present, the best force fields for biomols. employ a simple additive, nonpolarizable, and pairwise potential for at. interaction. In this work, the authors describe their efforts to build better models of the monovalent ions within the pairwise Coulombic and 6-12 Lennard-Jones framework, where the models are tuned to balance crystal and soln. properties in Ewald simulations with specific choices of well-known water models. Although it has been clearly demonstrated that truly accurate treatments of ions will require inclusion of nonadditivity and polarizability (particularly with the anions) and ultimately even a quantum mech. treatment, the authors' goal was to simply push the limits of the additive treatments to see if a balanced model could be created. The applied methodol. is general and can be extended to other ions and to polarizable force-field models. The authors' starting point centered on observations from long simulations of biomols. in salt soln. with the AMBER force fields where salt crystals formed well below their soly. limit. The likely cause of the artifact in the AMBER parameters relates to the naive mixing of the Smith and Dang chloride parameters with AMBER-adapted Aqvist cation parameters. To provide a more appropriate balance, the authors reoptimized the parameters of the Lennard-Jones potential for the ions and specific choices of water models. To validate and optimize the parameters, the authors calcd. hydration free energies of the solvated ions and also lattice energies (LE) and lattice consts. (LC) of alkali halide salt crystals. This is the first effort that systematically scans across the Lennard-Jones space (well depth and radius) while balancing ion properties like LE and LC across all pair combinations of the alkali ions and halide ions. The optimization across the entire monovalent series avoids systematic deviations. The ion parameters developed, optimized, and characterized were targeted for use with some of the most commonly used rigid and nonpolarizable water models, specifically TIP3P, TIP4PEW, and SPC/E. In addn. to well reproducing the soln. and crystal properties, the new ion parameters well reproduce binding energies of the ions to water and the radii of the first hydration shells.
- 71Sengupta, A.; Li, Z.; Song, L. F.; Li, P.; Merz, K. M. Parameterization of Monovalent Ions for the OPC3, OPC, TIP3P-FB, and TIP4P-FB Water Models. J. Chem. Inf. Model. 2021, 61 (2), 869– 880, DOI: 10.1021/acs.jcim.0c01390Google Scholar71https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXivVejur4%253D&md5=91eafbb4a3a16b5034e3c6cf3fb3873bParameterization of Monovalent Ions for the OPC3, OPC, TIP3P-FB, and TIP4P-FB Water ModelsSengupta, Arkajyoti; Li, Zhen; Song, Lin Frank; Li, Pengfei; Merz Jr., Kenneth M.Journal of Chemical Information and Modeling (2021), 61 (2), 869-880CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)Monovalent ions play significant roles in various biol. and material systems. Recently, four new water models (OPC3, OPC, TIP3P-FB, and TIP4P-FB), with significantly improved descriptions of condensed phase water, have been developed. The pairwise interaction between the metal ion and water necessitates the development of ion parameters specifically for these water models. Herein, we parameterized the 12-6 and the 12-6-4 nonbonded models for 12 monovalent ions with the resp. four new water models. These monovalent ions contain eight cations including alkali metal ions (Li+, Na+, K+, Rb+, Cs+), transition-metal ions (Cu+ and Ag+), and Tl+ from the boron family, along with four halide anions (F-, Cl-, Br-, I-). Our parameters were designed to reproduce the target hydration free energies (the 12-6 hydration free energy (HFE) set), the ion-oxygen distances (the 12-6 ion-oxygen distance (IOD) set), or both of them (the 12-6-4 set). The 12-6-4 parameter set provides highly accurate structural features overcoming the limitations of the routinely used 12-6 nonbonded model for ions. Specifically, we note that the 12-6-4 parameter set is able to reproduce exptl. hydration free energies within 1 kcal/mol and exptl. ion-oxygen distances within 0.01 Å simultaneously. We further reproduced the exptl. detd. activity derivs. for salt solns., validating the ion parameters for simulations of ion pairs. The improved performance of the present water models over our previous parameter sets for the TIP3P, TIP4P, and SPC/E water models (P. Li et al., J. Chem. Theor. Comput., 2015, 11, 1645 1657) highlights the importance of the choice of water model in conjunction with the metal ion parameter set.
- 72Li, P.; Song, L. F.; Merz, K. M. Systematic Parameterization of Monovalent Ions Employing the Nonbonded Model. J. Chem. Theory Comput. 2015, 11 (4), 1645– 1657, DOI: 10.1021/ct500918tGoogle Scholar72https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXitlKmurg%253D&md5=df0dabeb998e9de862776acd3970e299Systematic Parameterization of Monovalent Ions Employing the Nonbonded ModelLi, Pengfei; Song, Lin Frank; Merz, Kenneth M.Journal of Chemical Theory and Computation (2015), 11 (4), 1645-1657CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)Monovalent ions play fundamental roles in many biol. processes in organisms. Modeling these ions in mol. simulations continues to be a challenging problem. The 12-6 Lennard-Jones (LJ) nonbonded model is widely used to model monovalent ions in classical mol. dynamics simulations. A lot of parameterization efforts have been reported for these ions with a no. of exptl. end points. However, some reported parameter sets do not have a good balance between the two Lennard-Jones parameters (the van der Waals (VDW) radius and potential well depth), which affects their transferability. In the present work, via the use of a noble gas curve we fitted in former work, we reoptimized the 12-6 LJ parameters for 15 monovalent ions (11 pos. and 4 neg. ions) for three extensively used water models (TIP3P, SPC/E, and TIP4PEW). Since the 12-6 LJ nonbonded model performs poorly in some instances for these ions, we have also parameterized the 12-6-4 LJ-type nonbonded model using the same three water models. The three derived parameter sets focused on reproducing the hydration free energies (the HFE set) and the ion-oxygen distance (the IOD set) using the 12-6 LJ nonbonded model and the 12-6-4 LJ-type nonbonded model (the 12-6-4 set) overall give improved results. In particular, the final parameter sets showed better agreement with quantum mech. calcd. VDW radii and improved transferability to ion-pair solns. when compared to previous parameter sets.
- 73Bayly, C. I.; Cieplak, P.; Cornell, W. D.; Kollman, P. A. A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: The RESP model. J. Phys. Chem. 1993, 97, 10269– 10280, DOI: 10.1021/j100142a004Google Scholar73https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK3sXlvVyqsLs%253D&md5=e65c6a556ffc174df4f327687912a0bdA well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the RESP modelBayly, Christopher I.; Cieplak, Piotr; Cornell, Wendy; Kollman, Peter A.Journal of Physical Chemistry (1993), 97 (40), 10269-80CODEN: JPCHAX; ISSN:0022-3654.The authors present a new approach to generating electrostatic potential (ESP) derived charges for mols. The major strength of electrostatic potential derived charges is that they optimally reproduce the intermol. interaction properties of mols. with a simple two-body additive potential, provided, of course, that a suitably accurate level of quantum mech. calcn. is used to derive the ESP around the mol. Previously, the major weaknesses of these charges have been that they were not easily transferably between common functional groups in related mols., they have often been conformationally dependent, and the large charges that frequently occur can be problematic for simulating intramol. interactions. Introducing restraints in the form of a penalty function into the fitting process considerably reduces the above problems, with only a minor decrease in the quality of the fit to the quantum mech. ESP. Several other refinements in addn. to the restrained electrostatic potential (RESP) fit yield a general and algorithmic charge fitting procedure for generating atom-centered point charges. This approach can thus be recommended for general use in mol. mechanics, mol. dynamics, and free energy calcns. for any org. or bioorg. system.
- 74risch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Montgomery, J. A., Jr.; Vreven, T.; Kudin, K. N.; Burant, J. C.; Millam, J. M.; Iyengar, S. S.; Tomasi, J.; Barone, V.; Mennucci, B.; Cossi, M.; Scalmani, G.; Re, J. A. Gaussian 03 , 2007.Google ScholarThere is no corresponding record for this reference.
- 75Davidson, E. R.; Feller, D. Basis Set Selection for Molecular Calculations. Chem. Rev. 1986, 86 (4), 681– 696, DOI: 10.1021/cr00074a002Google Scholar75https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL28Xks1Kmt7c%253D&md5=1ebd5f870aed9777a8f993b6b602a10fBasis set selection for molecular calculationsDavidson, Ernest R.; Feller, DavidChemical Reviews (Washington, DC, United States) (1986), 86 (4), 681-96CODEN: CHREAY; ISSN:0009-2665.Contracted Cartesian Gaussian basis sets are reviewed with 131 refs.
- 76Case, D. A.; Aktulga, H. M.; Belfon, K.; Amber23 , 2023.Google ScholarThere is no corresponding record for this reference.
- 77Izaguirre, J. A.; Reich, S.; Skeel, R. D. Longer time steps for molecular dynamics. J. Chem. Phys. 1999, 110 (20), 9853– 9864, DOI: 10.1063/1.478995Google Scholar77https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK1MXivV2mtLc%253D&md5=d8a592f8139ff1e052869415dc5ca7b7Longer time steps for molecular dynamicsIzaguirre, Jesus A.; Reich, Sebastian; Skeel, Robert D.Journal of Chemical Physics (1999), 110 (20), 9853-9864CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)Simulations of the dynamics of biomols. have been greatly accelerated by the use of multiple time-stepping methods, such as the Verlet-I/r-RESPA (reversible ref. system propagator algorithms) method, which is based on approximating "slow" forces as widely sepd. impulses. Indeed, numerical expts. have shown that time steps of 4 fs are possible for these slow forces but unfortunately have also shown that a long time step of 5 fs results in a dramatic energy drift. To overcome this instability, a symplectic modification of the impulsive Verlet-I/r-RESPA method has been proposed, called the mollified impulse method. The idea is that one modifies the slow part of the potential energy so that it is evaluated at "time averaged" values of the positions, and one uses the gradient of this modified potential for the slow part of the force. By filtering out excitations to the fastest motions, these averagings allow the use of longer time steps than does the impulse method. We introduce a new mollified method, Equil., that avoids instability in a more effective manner than previous averaging mollified methods. Our expts. show that Equil. with a time step of 6 fs is as stable as the impulsive Verlet-I/r-RESPA method with a time step of 4 fs. We show that it may be necessary to include the effect of nonbonded forces in the averaging to make yet longer time steps possible. We also show that the slight modification of the potential has little effect on accuracy. For this purpose we compare self-diffusion coeffs. and radial distribution functions against the Leapfrog method with a short time step (0.5 fs).
- 78Case, D. A.; Ben-Shalom, I. Y.; Brozell, S. R.; AMBER 2018; University of California, 2018.Google ScholarThere is no corresponding record for this reference.
- 79Berendsen, H. J. C.; Postma, J. P. M.; Van Gunsteren, W. F.; Dinola, A.; Haak, J. R. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 1984, 81 (8), 3684– 3690, DOI: 10.1063/1.448118Google Scholar79https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL2cXmtlGksbY%253D&md5=5510dc00297d63b91ee3a7a4ae5aacb1Molecular dynamics with coupling to an external bathBerendsen, H. J. C.; Postma, J. P. M.; Van Gunsteren, W. F.; DiNola, A.; Haak, J. R.Journal of Chemical Physics (1984), 81 (8), 3684-90CODEN: JCPSA6; ISSN:0021-9606.In mol. dynamics (MD) simulations, the need often arises to maintain such parameters as temp. or pressure rather than energy and vol., or to impose gradients for studying transport properties in nonequil. MD. A method is described to realize coupling to an external bath with const. temp. or pressure with adjustable time consts. for the coupling. The method is easily extendable to other variables and to gradients, and can be applied also to polyat. mols. involving internal constraints. The influence of coupling time consts. on dynamical variables is evaluated. A leap-frog algorithm is presented for the general case involving constraints with coupling to both a const. temp. and a const. pressure bath.
- 80Koynova, R.; Caffrey, M. Phases and phase transitions of the phosphatidylcholines. Biochim. Biophys. Acta, Biomembr. 1998, 1376 (1), 91– 145, DOI: 10.1016/S0304-4157(98)00006-9Google Scholar80https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK1cXktFGnt7g%253D&md5=80f11f060ae55bc1193a047532b290f6Phases and phase transitions of the phosphatidylcholinesKoynova, Rumiana; Caffrey, MartinBiochimica et Biophysica Acta, Reviews on Biomembranes (1998), 1376 (1), 91-145CODEN: BRBMC5; ISSN:0304-4157. (Elsevier B.V.)A review with 651 refs. LIPIDAT (http://www.lipidat.chem.ohio-state.edu) is an Internet accessible, computerized relational database providing access to the wealth of information scattered throughout the literature concerning synthetic and biol. derived polar lipid polymorphic and mesomorphic phase behavior and mol. structures. Here, a review of the data subset referring to phosphatidylcholines is presented together with an anal. of these data. This subset represents ca. 60% of all LIPIDAT records. It includes data collected over a 43-yr period and consists of 12,208 records obtained from 1573 articles in 106 different journals. An anal. of the data in the subset identifies trends in phosphatidylcholine phase behavior reflecting changes in lipid chain length, unsatn. (no., isomeric type and position of double bonds), asymmetry and branching, type of chain-glycerol linkage (ester, ether, amide), position of chain attachment to the glycerol backbone (1,2- vs. 1,3-) and head group modification. Also included is a summary of the data concerning the effect of pressure, pH, stereochem. purity, and different additives such as salts, saccharides, amino acids and alcs., on phosphatidylcholine phase behavior. Information on the phase behavior of biol. derived phosphatidylcholines is also presented. This review includes 651 refs.
- 81Ryckaert, J. P.; Ciccotti, G.; Berendsen, H. J. C. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput. Phys. 1977, 23 (3), 327– 341, DOI: 10.1016/0021-9991(77)90098-5Google Scholar81https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaE2sXktVGhsL4%253D&md5=b4aecddfde149117813a5ea4f5353ce2Numerical integration of the Cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanesRyckaert, Jean Paul; Ciccotti, Giovanni; Berendsen, Herman J. C.Journal of Computational Physics (1977), 23 (3), 327-41CODEN: JCTPAH; ISSN:0021-9991.A numerical algorithm integrating the 3N Cartesian equation of motion of a system of N points subject to holonomic constraints is applied to mol. dynamics simulation of a liq. of 64 butane mols.
- 82Humphrey, W.; Dalke, A.; Schulten, K. VMD: Visual molecular dynamics. J. Mol. Graphics 1996, 14 (1), 33– 38, DOI: 10.1016/0263-7855(96)00018-5Google Scholar82https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK28Xis12nsrg%253D&md5=1e3094ec3151fb85c5ff05f8505c78d5VDM: visual molecular dynamicsHumphrey, William; Dalke, Andrew; Schulten, KlausJournal of Molecular Graphics (1996), 14 (1), 33-8, plates, 27-28CODEN: JMGRDV; ISSN:0263-7855. (Elsevier)VMD is a mol. graphics program designed for the display and anal. of mol. assemblies, in particular, biopolymers such as proteins and nucleic acids. VMD can simultaneously display any no. of structures using a wide variety of rendering styles and coloring methods. Mols. are displayed as one or more "representations," in which each representation embodies a particular rendering method and coloring scheme for a selected subset of atoms. The atoms displayed in each representation are chosen using an extensive atom selection syntax, which includes Boolean operators and regular expressions. VMD provides a complete graphical user interface for program control, as well as a text interface using the Tcl embeddable parser to allow for complex scripts with variable substitution, control loops, and function calls. Full session logging is supported, which produces a VMD command script for later playback. High-resoln. raster images of displayed mols. may be produced by generating input scripts for use by a no. of photorealistic image-rendering applications. VMD has also been expressly designed with the ability to animate mol. dynamics (MD) simulation trajectories, imported either from files or from a direct connection to a running MD simulation. VMD is the visualization component of MDScope, a set of tools for interactive problem solving in structural biol., which also includes the parallel MD program NAMD, and the MDCOMM software used to connect the visualization and simulation programs, VMD is written in C++, using an object-oriented design; the program, including source code and extensive documentation, is freely available via anonymous ftp and through the World Wide Web.
- 83Roe, D. R.; Cheatham, T. E. PTRAJ and CPPTRAJ: Software for Processing and Analysis of Molecular Dynamics Trajectory Data. J. Chem. Theory Comput. 2013, 9 (7), 3084– 3095, DOI: 10.1021/ct400341pGoogle Scholar83https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXptFehtr8%253D&md5=6f1bee934f13f180bd7e1feb6b78036dPTRAJ and CPPTRAJ: Software for Processing and Analysis of Molecular Dynamics Trajectory DataRoe, Daniel R.; Cheatham, Thomas E.Journal of Chemical Theory and Computation (2013), 9 (7), 3084-3095CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)We describe PTRAJ and its successor CPPTRAJ, two complementary, portable, and freely available computer programs for the anal. and processing of time series of three-dimensional at. positions (i.e., coordinate trajectories) and the data therein derived. Common tools include the ability to manipulate the data to convert among trajectory formats, process groups of trajectories generated with ensemble methods (e.g., replica exchange mol. dynamics), image with periodic boundary conditions, create av. structures, strip subsets of the system, and perform calcns. such as RMS fitting, measuring distances, B-factors, radii of gyration, radial distribution functions, and time correlations, among other actions and analyses. Both the PTRAJ and CPPTRAJ programs and source code are freely available under the GNU General Public License version 3 and are currently distributed within the AmberTools 12 suite of support programs that make up part of the Amber package of computer programs (see http://ambermd.org). This overview describes the general design, features, and history of these two programs, as well as algorithmic improvements and new features available in CPPTRAJ.
- 84Michaud-Agrawal, N.; Denning, E. J.; Woolf, T. B.; Beckstein, O. MDAnalysis: A toolkit for the analysis of molecular dynamics simulations. J. Comput. Chem. 2011, 32 (10), 2319– 2327, DOI: 10.1002/jcc.21787Google Scholar84https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXnvFalsr8%253D&md5=d567042c65cfdc1c81336a29137654bfMDAnalysis: A toolkit for the analysis of molecular dynamics simulationsMichaud-Agrawal, Naveen; Denning, Elizabeth J.; Woolf, Thomas B.; Beckstein, OliverJournal of Computational Chemistry (2011), 32 (10), 2319-2327CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)MDAnal. is an object-oriented library for structural and temporal anal. of mol. dynamics (MD) simulation trajectories and individual protein structures. It is written in the Python language with some performance-crit. code in C. It uses the powerful NumPy package to expose trajectory data as fast and efficient NumPy arrays. It has been tested on systems of millions of particles. Many common file formats of simulation packages including CHARMM, Gromacs, Amber, and NAMD and the Protein Data Bank format can be read and written. Atoms can be selected with a syntax similar to CHARMM's powerful selection commands. MDAnal. enables both novice and experienced programmers to rapidly write their own anal. tools and access data stored in trajectories in an easily accessible manner that facilitates interactive explorative anal. MDAnal. has been tested on and works for most Unix-based platforms such as Linux and Mac OS X. It is freely available under the GNU General Public License from http://mdanal.googlecode.com. © 2011 Wiley Periodicals, Inc. J Comput Chem 2011.
- 85Gowers, R.; Linke, M.; Barnoud, J., Reddy, T., Melo, M., Seyler, S., Domański, J., Dotson, D., Buchoux, S., Kenney, I., MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations. In Proceedings of the 15th Python in Science Conference; Los Alamos National Laboratory, 2016; pp 98– 105. doi: DOI: 10.25080/Majora-629e541a-00e .Google ScholarThere is no corresponding record for this reference.
- 86Hunter, J. D. Matplotlib: A 2D Graphics Environment. Comput. Sci. Eng. 2007, 9 (3), 90– 95, DOI: 10.1109/MCSE.2007.55Google ScholarThere is no corresponding record for this reference.
- 87Bouysset, C.; Fiorucci, S. ProLIF: a library to encode molecular interactions as fingerprints. J. Cheminf. 2021, 13 (1), 72, DOI: 10.1186/s13321-021-00548-6Google ScholarThere is no corresponding record for this reference.
- 88Harris, C. R.; Millman, K. J.; van der Walt, S. J.; Gommers, R.; Virtanen, P.; Cournapeau, D.; Wieser, E.; Taylor, J.; Berg, S.; Smith, N. J. Array programming with NumPy. Nature 2020, 585 (7825), 357– 362, DOI: 10.1038/s41586-020-2649-2Google Scholar88https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXitlWmsbbN&md5=a9e32986e9cc14fa31afe3e524e95882Array programming with NumPyHarris, Charles R.; Millman, K. Jarrod; van der Walt, Stefan J.; Gommers, Ralf; Virtanen, Pauli; Cournapeau, David; Wieser, Eric; Taylor, Julian; Berg, Sebastian; Smith, Nathaniel J.; Kern, Robert; Picus, Matti; Hoyer, Stephan; van Kerkwijk, Marten H.; Brett, Matthew; Haldane, Allan; del Rio, Jaime Fernandez; Wiebe, Mark; Peterson, Pearu; Gerard-Marchant, Pierre; Sheppard, Kevin; Reddy, Tyler; Weckesser, Warren; Abbasi, Hameer; Gohlke, Christoph; Oliphant, Travis E.Nature (London, United Kingdom) (2020), 585 (7825), 357-362CODEN: NATUAS; ISSN:0028-0836. (Nature Research)Abstr.: Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrixes and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research anal. pipelines in fields as diverse as physics, chem., astronomy, geoscience, biol., psychol., materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analyzing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial anal.
- 89Procacci, P. Multiple Bennett acceptance ratio made easy for replica exchange simulations. J. Chem. Phys. 2013, 139 (12), 124105, DOI: 10.1063/1.4821814Google Scholar89https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhsV2ksLjN&md5=61c1df918dd644f14fad4c884e69e81bMultiple Bennett acceptance ratio made easy for replica exchange simulationsProcacci, PieroJournal of Chemical Physics (2013), 139 (12), 124105/1-124105/6CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)In this paper, we illustrate a practical technique to improve the efficiency of the so-called multiple Bennett acceptance ratio (MBAR) estimator for computing thermodn. expectations of phys. quantities, from samples drawn from Hamiltonian or temp. replica exchange simulations. The methods exploit the Crooks fluctuation theorem for accurately evaluating the partition functions ratios of neighboring replicas, thus providing an excellent initial guess for the MBAR iterative procedure. (c) 2013 American Institute of Physics.
- 90He, X.; Liu, S.; Lee, T. S.; Ji, B.; Man, V. H.; York, D. M.; Wang, J. Fast, Accurate, and Reliable Protocols for Routine Calculations of Protein-Ligand Binding Affinities in Drug Design Projects Using AMBER GPU-TI with ff14SB/GAFF. ACS Omega 2020, 5 (9), 4611– 4619, DOI: 10.1021/acsomega.9b04233Google Scholar90https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXjs1arsLk%253D&md5=1e7b3bc44a0cb73a122538af2c04434cFast, Accurate, and Reliable Protocols for Routine Calculations of Protein-Ligand Binding Affinities in Drug Design Projects Using AMBER GPU-TI with ff14SB/GAFFHe, Xibing; Liu, Shuhan; Lee, Tai-Sung; Ji, Beihong; Man, Viet H.; York, Darrin M.; Wang, JunmeiACS Omega (2020), 5 (9), 4611-4619CODEN: ACSODF; ISSN:2470-1343. (American Chemical Society)Accurate prediction of the abs. or relative protein-ligand binding affinity is one of the major tasks in computer-aided drug design projects, esp. in the stage of lead optimization. In principle, the alchem. free energy (AFE) methods such as thermodn. integration (TI) or free-energy perturbation (FEP) can fulfill this task, but in practice, a lot of hurdles prevent them from being routinely applied in daily drug design projects, such as the demanding computing resources, slow computing processes, unavailable or inaccurate force field parameters, and difficult and unfriendly setting up and post-anal. procedures. In this study, we have exploited practical protocols of applying the CPU (central processing unit)-TI and newly developed GPU (graphic processing unit)-TI modules and other tools in the AMBER software package, combined with ff14SB/GAFF1.8 force fields, to conduct efficient and accurate AFE calcns. on protein-ligand binding free energies. We have tested 134 protein-ligand complexes in total for four target proteins (BACE, CDK2, MCL1, and PTP1B) and obtained overall comparable performance with the com. Schrodinger FEP+ program (). The achieved accuracy fits within the requirements for computations to generate effective guidance for exptl. work in drug lead optimization, and the needed wall time is short enough for practical application. Our verified protocol provides a practical soln. for routine AFE calcns. in real drug design projects.
- 91Song, L. F.; Lee, T. S.; Zhu, C.; York, D. M.; Merz, K. M. Using AMBER18 for Relative Free Energy Calculations. J. Chem. Inf. Model. 2019, 59 (7), 3128– 3135, DOI: 10.1021/acs.jcim.9b00105Google Scholar91https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhtFals73N&md5=fff93b6518d5771717d50db133ff9fe1Using AMBER18 for Relative Free Energy CalculationsSong, Lin Frank; Lee, Tai-Sung; Zhu, Chun; York, Darrin M.; Merz, Kenneth M.Journal of Chemical Information and Modeling (2019), 59 (7), 3128-3135CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)With renewed interest in free energy methods in contemporary structure-based need to validate against multiple targets and force fields to assess the overall ability of these methods to accurately predict relative binding free energies. We computed relative binding free energies using GPU accelerated Thermodn. Integration (GPU-TI) on a dataset originally assembled by Schrodinger, Inc.. Using their GPU free energy code (FEP+) and the OPLS2.1 force field combined with the REST2 enhanced sampling approach, these authors obtained an overall MUE of 0.9 kcal/mol and an overall RMSD of 1.14 kcal/mol. In our study using GPU-TI from AMBER with the AMBER14SB/GAFF1.8 force field but without enhanced sampling, we obtained an overall MUE of 1.17 kcal/mol and an overall RMSD of 1.50 kcal/mol for the 330 perturbations contained in this data set. A more detailed analyses of our results suggested that the obsd. differences between the two studies arise from differences in sampling protocols along with differences in the force fields employed. Future work should address the problem of establishing benchmark quality results with robust statistical error bars obtained through multiple independent runs and enhanced sampling, which is possible with the GPU-accelerated features in AMBER.
- 92Genheden, S.; Nilsson, I.; Ryde, U. Binding Affinities of Factor Xa Inhibitors Estimated by Thermodynamic Integration and MM/GBSA. J. Chem. Inf. Model. 2011, 51 (4), 947– 958, DOI: 10.1021/ci100458fGoogle Scholar92https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXjsVCrsrk%253D&md5=372ec20bd9e70fd6b55fb0eae44eb491Binding Affinities of Factor Xa Inhibitors Estimated by Thermodynamic Integration and MM/GBSAGenheden, Samuel; Nilsson, Ingemar; Ryde, UlfJournal of Chemical Information and Modeling (2011), 51 (4), 947-958CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)We present free energy ests. of nine 3-amidinobenzyl-1H-indole-2-carboxamide inhibitors of factor Xa. Using alchem. thermodn. integration (TI) calcns., we est. the difference in binding free energies with high accuracy and precision, except for mutations involving one of the amidinobenzyl rings. Crystal studies show that the inhibitors may bind in two distinct conformations, and using TI, we show that the two conformations give a similar binding affinity. Furthermore, we show that we can reduce the computational demand, while still retaining a high accuracy and precision, by using fewer integration points and shorter protein-ligand simulations. Finally, we have compared the TI results to those obtained with the simpler MM/GBSA method (mol.-mechanics with generalized Born surface-area solvation). MM/GBSA gives better results for the mutations that involve a change of net charge, but if a precision similar to that of the TI method is required, the MM/GBSA method is actually slightly more expensive. Thus, we have shown that TI could be a valuable tool in drug design.
- 93Shirts, M. R.; Pande, V. S. Comparison of efficiency and bias of free energies computed by exponential averaging, the Bennett acceptance ratio, and thermodynamic integration. J. Chem. Phys. 2005, 122 (14), 144107, DOI: 10.1063/1.1873592Google Scholar93https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXjsFGhsb0%253D&md5=a1f11573f5e3b81847135bb8a2e2b790Comparison of efficiency and bias of free energies computed by exponential averaging, the Bennett acceptance ratio, and thermodynamic integrationShirts, Michael R.; Pande, Vijay S.Journal of Chemical Physics (2005), 122 (14), 144107/1-144107/16CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)Recent work has demonstrated the Bennett acceptance ratio method is the best asymptotically unbiased method for detg. the equil. free energy between two end states given work distributions collected from either equil. or nonequil. data. However, it is still not clear what the practical advantage of this acceptance ratio method is over other common methods in atomistic simulations. In this study, we first review theor. ests. of the bias and variance of exponential averaging (EXP), thermodn. integration (TI), and the Bennett acceptance ratio (BAR). In the process, we present a new simple scheme for computing the variance and bias of many estimators, and demonstrate the connections between BAR and the weighted histogram anal. method. Next, a series of anal. solvable toy problems is examd. to shed more light on the relative performance in terms of the bias and efficiency of these three methods. Interestingly, it is impossible to conclusively identify a "best" method for calcg. the free energy, as each of the three methods performs more efficiently than the others in at least one situation examd. in these toy problems. Finally, sample problems of the insertion/deletion of both a Lennard-Jones particle and a much larger mol. in TIP3P water are examd. by these three methods. In all tests of atomistic systems, free energies obtained with BAR have significantly lower bias and smaller variance than when using EXP or TI, esp. when the overlap in phase space between end states is small. For example, BAR can ext. as much information from multiple fast, far-from-equil. simulations as from fewer simulations near equil., which EXP cannot. Although TI and sometimes even EXP can be somewhat more efficient in idealized toy problems, in the realistic atomistic situations tested in this paper, BAR is significantly more efficient than all other methods.
- 94Paliwal, H.; Shirts, M. R. A benchmark test set for alchemical free energy transformations and its use to quantify error in common free energy methods. J. Chem. Theory Comput. 2011, 7, 4115– 4134, DOI: 10.1021/ct2003995Google Scholar94https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhtlaqtrvM&md5=41383373300216165a7f1724ad535451A Benchmark Test Set for Alchemical Free Energy Transformations and Its Use to Quantify Error in Common Free Energy MethodsPaliwal, Himanshu; Shirts, Michael R.Journal of Chemical Theory and Computation (2011), 7 (12), 4115-4134CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)There is a significant need for improved tools to validate thermophys. quantities computed via mol. simulation. In this paper we present the initial version of a benchmark set of testing methods for calcg. free energies of mol. transformation in soln. This set is based on mol. changes common to many mol. design problems, such as insertion and deletion of at. sites and changing at. partial charges. We use this benchmark set to compare the statistical efficiency, reliability, and quality of uncertainty ests. for a no. of published free energy methods, including thermodn. integration, free energy perturbation, the Bennett acceptance ratio (BAR) and its multistate equiv. MBAR. We identify MBAR as the consistently best performing method, though other methods are frequently comparable in reliability and accuracy in many cases. We demonstrate that assumptions of Gaussian distributed errors in free energies are usually valid for most methods studied. We demonstrate that bootstrap error estn. is a robust and useful technique for estg. statistical variance for all free energy methods studied. This benchmark set is provided in a no. of different file formats with the hope of becoming a useful and general tool for method comparisons.
- 95Tan, Z.; Gallicchio, E.; Lapelosa, M.; Levy, R. M. Theory of binless multi-state free energy estimation with applications to protein-ligand binding. J. Chem. Phys. 2012, 136. DOI: 10.1063/1.3701175 .Google ScholarThere is no corresponding record for this reference.
- 96Vinuesa, A.; Viñas, M.; Jahani, D.; Ginard, J.; Mur, N.; Pujol, M. D. Regioselective alkylation reaction of purines under microwave irradiation. J. Heterocycl. Chem. 2022, 59 (3), 597– 602, DOI: 10.1002/jhet.4407Google ScholarThere is no corresponding record for this reference.
- 97Dyer, E.; Reitz, J. M.; Farris, R. E. Carbamates Derived from Aminopurines. J. Med. Chem. 1963, 6 (3), 289– 291, DOI: 10.1021/jm00339a015Google ScholarThere is no corresponding record for this reference.
- 98Da Costa Leite, L. F. C.; Srivastava, R. M.; Cavalcanti, A. P. Thermal Reactions of Arylamidoximes. Bull. Soc. Chim. Belg. 1989, 98 (3), 203– 210, DOI: 10.1002/bscb.19890980307Google ScholarThere is no corresponding record for this reference.
- 99Ismail, M. A.; Brun, R.; Easterbrook, J. D.; Tanious, F. A.; Wilson, W. D.; Boykin, D. W. Synthesis and Antiprotozoal Activity of Aza-Analogues of Furamidine. J. Med. Chem. 2003, 46 (22), 4761– 4769, DOI: 10.1021/jm0302602Google Scholar99https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3sXnsFyqsbw%253D&md5=80cb286d23d7946e403505a57e532b05Synthesis and Antiprotozoal Activity of Aza-Analogues of FuramidineIsmail, Mohamed A.; Brun, Reto; Easterbrook, Judy D.; Tanious, Farial A.; Wilson, W. David; Boykin, David W.Journal of Medicinal Chemistry (2003), 46 (22), 4761-4769CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)6-[5-(4-Amidinophenyl)furan-2-yl]nicotinamidine (I; X = O, R = H) was synthesized from 6-[5-(4-cyanophenyl)furan-2-yl]nicotinonitrile (II), through the bis-O-acetoxyamidoxime followed by hydrogenation. Compd. II was prepd. via selective bromination of 6-(furan-2-yl)nicotinonitrile with N-bromosuccinimide, followed by Suzuki coupling with 4-cyanophenylboronic acid. In a similar way, diamidines III and IV (R = H) were prepd. from the corresponding dicyano derivs. N-Methoxy-6-{5-[4-(N-methoxyamidino)phenyl]-furan-2-yl}-nicotinamidine (I; X = O, R = OMe) was prepd. via methylation of the resp. diamidoxime with dimethylsulfate. Prodrugs I (X = S, R = OMe) and IV (R = OMe) were also prepd. by methylation of the resp. diamidoximes. The sym. diamidines V and VI were synthesized through the corresponding bis-O-acetoxyamidoxime followed by hydrogenation. The corresponding dicyano precursors were conveniently obtained by Stille coupling between 2,5-bis(tri-n-butylstannyl)furan and the corresponding heteroaryl halides. These compds. have been evaluated in vitro for activity against Trypanosoma b. rhodesiense (T. b. r.) and P. falciparum (P. f.). The diamidines I (X = O, R = H) and IV (R = H), and VI gave IC50 values vs. T. b. r. of less than 10 nM. Against P. f. I (X = O, R = H) and III, and VI exhibited IC50 values less than 10 nM. In an in vivo mouse model for T. b. r. compds. I (X = O, R = OMe, OEt, and H) and IV (R = OMe) were curative. I (X = O, R = OMe) produced cures at an oral dosage of 5 mg/kg.
- 100Xu, L. L.; Wu, Y. F.; Wang, L.; Li, C. C.; Li, L.; Di, B.; You, Q. D.; Jiang, Z. Y. Structure-activity and structure-property relationships of novel Nrf2 activators with a 1,2,4-oxadiazole core and their therapeutic effects on acetaminophen (APAP)-induced acute liver injury. Eur. J. Med. Chem. 2018, 157, 1376– 1394, DOI: 10.1016/j.ejmech.2018.08.071Google Scholar100https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhs1OmurrP&md5=68a622002b4ce75203120db53c93e5b3Structure-activity and structure-property relationships of novel Nrf2 activators with a 1,2,4-oxadiazole core and their therapeutic effects on acetaminophen (APAP)-induced acute liver injuryXu, Li-Li; Wu, Yu-Feng; Wang, Lei; Li, Cui-Cui; Li, Li; Di, Bin; You, Qi-Dong; Jiang, Zheng-YuEuropean Journal of Medicinal Chemistry (2018), 157 (), 1376-1394CODEN: EJMCA5; ISSN:0223-5234. (Elsevier Masson SAS)The antioxidant function induced by Nrf2 protects the liver from damage. We found a novel Nrf2 activator named compd. 25 via structural modification of compd. 1 we previously reported. In vitro, compd. 25 induced Nrf2 transport into the nucleus and protected hepatocyte L02 cells from APAP-induced cytotoxicity via activating the Nrf2-ARE signaling pathway. In vivo, 25 exhibited therapeutic effects in a mouse model of acute liver injury induced by acetaminophen (APAP) by up-regulating Nrf2-dependent antioxidases and down-regulating liver injury markers in serum. Together, these results indicated that 25 is a potent Nrf2/ARE activator both in vitro and in vivo. The drug-like properties of compd. 25 further revealed its potential for development as a therapeutic drug against acute liver injury.
- 101Lin, C. C.; Hsieh, T. H.; Liao, P. Y.; Liao, Z. Y.; Chang, C. W.; Shih, Y. C.; Yeh, W. H.; Chien, T. C. Practical Synthesis of N -Substituted Cyanamides via Tiemann Rearrangement of Amidoximes. Org. Lett. 2014, 16 (3), 892– 895, DOI: 10.1021/ol403645yGoogle ScholarThere is no corresponding record for this reference.
- 102Koryakova, A. G.; Ivanenkov, Y. A.; Ryzhova, E. A.; Bulanova, E. A.; Karapetian, R. N.; Mikitas, O. V.; Katrukha, E. A.; Kazey, V. I.; Okun, I.; Kravchenko, D. V. Novel aryl and heteroaryl substituted N-[3-(4-phenylpiperazin-1-yl)propyl]-1,2,4-oxadiazole-5-carboxamides as selective GSK-3 inhibitors. Bioorg. Med. Chem. Lett. 2008, 18 (12), 3661– 3666, DOI: 10.1016/j.bmcl.2007.11.121Google ScholarThere is no corresponding record for this reference.
- 103Camp, J. E.; Shabalin, D. A.; Dunsford, J. J.; Ngwerume, S.; Saunders, A. R.; Gill, D. M. Synthesis of 2,4-Disubstituted Imidazoles via Nucleophilic Catalysis. Synlett 2020, 31 (08), 797– 800, DOI: 10.1055/s-0039-1690832Google ScholarThere is no corresponding record for this reference.
- 104Suzue, S.; Hirobe, M.; Okamoto, T. Synthetic Antimicrobials. II. Synthesis of Pyrazolo [1, 5-a] pyridine Derivatives. Chem. Pharm. Bull. 1973, 21 (10), 2146– 2160, DOI: 10.1248/cpb.21.2146Google ScholarThere is no corresponding record for this reference.
- 105Lessel, J.; Herfs, G. Synthesis of 4,5-dihydro-1,2,4-oxadiazoles from N-unsubstituted amidoximes. Pharmazie 2000, 55 (1), 22– 26Google ScholarThere is no corresponding record for this reference.
- 106Gao, J.; Liu, X.; Zhang, B.; Mao, Q.; Zhang, Z.; Zou, Q.; Dai, X.; Wang, S. Design, synthesis and biological evaluation of 1-alkyl-5/6-(5-oxo-4,5-dihydro-1,2,4-oxadiazol-3-yl)-1H-indole-3-carbonitriles as novel xanthine oxidase inhibitors. Eur. J. Med. Chem. 2020, 190, 112077, DOI: 10.1016/j.ejmech.2020.112077Google Scholar106https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXisFOgtrs%253D&md5=c5f3c2deb1cc0249eff200dcae09eae0Design, synthesis and biological evaluation of 1-alkyl-5/6-(5-oxo-4,5-dihydro-1,2,4-oxadiazol-3-yl)-1H-indole-3-carbonitriles as novel xanthine oxidase inhibitorsGao, Jun; Liu, Xuegui; Zhang, Bing; Mao, Qing; Zhang, Zhuo; Zou, Qian; Dai, Xiwen; Wang, ShaojieEuropean Journal of Medicinal Chemistry (2020), 190 (), 112077pp.CODEN: EJMCA5; ISSN:0223-5234. (Elsevier Masson SAS)Xanthine oxidase (XO) has emerged as an important target for the treatment of hyperuricemia and gout. In this study, to obtain novel nonpurine XO inhibitors, a series of 1-alkyl-5/6-(5-oxo-4,5-dihydro-1,2,4-oxadiazol-3-yl)-1H-indole-3-carbonitriles I (R = H, iso-Bu, cyclopentyl, benzyl, etc.; R1 = R2 = H, 5-oxo-4,5-dihydro-1,2,4-oxadiazol-3-yl) was designed using a bioisosteric replacement strategy and synthesized through a five-step procedure with good yields. Thereafter, the in vitro XO inhibitory potencies of these compds. were evaluated by spectrophotometry, showing inhibitory profiles in the micromolar/submicromolar range. Particularly, compd. I (R = cyclopentyl; R1 = 5-oxo-4,5-dihydro-1,2,4-oxadiazol-3-yl) (A) emerged as the strongest XO inhibitor, with an IC50 value of 0.36μM, which was approx. 21-fold more potent than the pos. control allopurinol. Addnl., the structure-activity relationships revealed that the 5-oxo-4,5-dihydro-1,2,4-oxadiazole moiety linked at the 5-position of the indole scaffold was more preferable than the 6-position for the XO inhibitory potency. Enzyme kinetic studies indicated that compd. A acted as a mixed-type XO inhibitor. Moreover, mol. modeling studies were performed on compd. A to gain insights into its binding modes with XO. The results showed that the 5-oxo-4,5-dihydro-1,2,4-oxadiazole moiety could interact with Arg880 and Thr1010 in the innermost part of the active pocket through hydrogen bonds, while the cyano group could form hydrogen bonds with Asn768 and Lys771 in the subpocket. Furthermore, the in vivo hypouricemic effect of compd. I (R = cyclopentyl) was further investigated in a hyperuricemia rat model induced by potassium oxonate. The results suggested that compd. I (R = cyclopentyl) could effectively reduce serum uric acid levels at an oral dose of 10 mg/kg. Therefore, compd. I (R = cyclopentyl) could be a promising lead compd. for the treatment of hyperuricemia and gout.
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- 8Katritch, V.; Jaakola, V. P.; Lane, J. R.; Lin, J.; Ijzerman, A. P.; Yeager, M.; Kufareva, I.; Stevens, R. C.; Abagyan, R. Structure-based discovery of novel chemotypes for adenosine A2A receptor antagonists. J. Med. Chem. 2010, 53 (4), 1799– 1809, DOI: 10.1021/jm901647p8https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXptlKqsw%253D%253D&md5=5a54df43f6edd20d83e7e5942e2f9811Structure-Based Discovery of Novel Chemotypes for Adenosine A2A Receptor AntagonistsKatritch, Vsevolod; Jaakola, Veli-Pekka; Lane, J. Robert; Lin, Judy; IJzerman, Adriaan P.; Yeager, Mark; Kufareva, Irina; Stevens, Raymond C.; Abagyan, RubenJournal of Medicinal Chemistry (2010), 53 (4), 1799-1809CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)The recent progress in crystallog. of G-protein coupled receptors opens an unprecedented venue for structure-based GPCR drug discovery. To test efficiency of the structure-based approach, we performed mol. docking and virtual ligand screening (VLS) of more than 4 million com. available "drug-like" and "lead-like" compds. against the A2AAR 2.6 Å resoln. crystal structure. Out of 56 high ranking compds. tested in A2AAR binding assays, 23 showed affinities under 10 μM, 11 of those had sub-μM affinities and two compds. had affinities under 60 nM. The identified hits represent at least 9 different chem. scaffolds and are characterized by very high ligand efficiency (0.3-0.5 kcal/mol per heavy atom). Significant A2AAR antagonist activities were confirmed for 10 out of 13 ligands tested in functional assays. High success rate, novelty, and diversity of the chem. scaffolds and strong ligand efficiency of the A2AAR antagonists identified in this study suggest practical applicability of receptor-based VLS in GPCR drug discovery.
- 9Carlsson, J.; Yoo, L.; Gao, Z. G.; Irwin, J. J.; Shoichet, B. K.; Jacobson, K. A. Structure-based discovery of A2Aadenosine receptor ligands. J. Med. Chem. 2010, 53 (9), 3748– 3755, DOI: 10.1021/jm100240h9https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXkvFaqsL8%253D&md5=c36c941d52d2cec06387d79c4c423d46Structure-Based Discovery of A2A Adenosine Receptor LigandsCarlsson, Jens; Yoo, Lena; Gao, Zhan-Guo; Irwin, John J.; Shoichet, Brian K.; Jacobson, Kenneth A.Journal of Medicinal Chemistry (2010), 53 (9), 3748-3755CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)The recent detn. of X-ray structures of pharmacol. relevant GPCRs has made these targets accessible to structure-based ligand discovery. Here we explore whether novel chemotypes may be discovered for the A2A adenosine receptor, based on complementarity to its recently detd. structure. The A2A adenosine receptor signals in the periphery and the CNS, with agonists explored as anti-inflammatory drugs and antagonists explored for neurodegenerative diseases. We used mol. docking to screen a 1.4 million compd. database against the X-ray structure computationally and tested 20 high-ranking, previously unknown mols. exptl. Of these 35% showed substantial activity with affinities between 200 nM and 9 μM. For the most potent of these new inhibitors, over 50-fold specificity was obsd. for the A2A vs. the related A1 and A3 subtypes. These high hit rates and affinities at least partly reflect the bias of com. libraries toward GPCR-like chemotypes, an issue that we attempt to investigate quant. Despite this bias, many of the most potent new ligands were novel, dissimilar from known ligands, providing new lead structures for modulation of this medically important target.
- 10Lenselink, E. B.; Beuming, T.; van Veen, C.; Massink, A.; Sherman, W.; van Vlijmen, H. W. T.; Ijzerman, A. P. In search of novel ligands using a structure-based approach: a case study on the adenosine A2A receptor. J. Comput. Aided Mol. Des. 2016, 30 (10), 863– 874, DOI: 10.1007/s10822-016-9963-7There is no corresponding record for this reference.
- 11Cescon, E.; Bolcato, G.; Federico, S.; Bissaro, M.; Valentini, A.; Ferlin, M. G.; Spalluto, G.; Sturlese, M.; Moro, S. Scaffold Repurposing of in-House Chemical Library toward the Identification of New Casein Kinase 1 Inhibitors. ACS Med. Chem. Lett. 2020, 11 (6), 1168– 1174, DOI: 10.1021/acsmedchemlett.0c0002811https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXot1Wms7c%253D&md5=191136a578c976cacb0219cf4612d219Scaffold Repurposing of in-House Chemical Library toward the Identification of New Casein Kinase 1 δ InhibitorsCescon, Eleonora; Bolcato, Giovanni; Federico, Stephanie; Bissaro, Maicol; Valentini, Alice; Ferlin, Maria Grazia; Spalluto, Gianpiero; Sturlese, Mattia; Moro, StefanoACS Medicinal Chemistry Letters (2020), 11 (6), 1168-1174CODEN: AMCLCT; ISSN:1948-5875. (American Chemical Society)Recent studies have highlighted the key role of Casein kinase 1 δ (CK1δ) in the development of several neurodegenerative pathologies, such as Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS). So far, CK1δ inhibitors are noncovalent ATP competitive ligands and no drugs are currently available for this mol. target, hence the interest in developing new CK1δ inhibitors. The study aims to identify new inhibitors able to bind the enzyme; by a dual approach in silico/in vitro, the virtual screening has been performed on an inhouse chem. library, which was previously designed and synthesized for other targets. The work can, therefore, be seen in the scaffold repurposing logic. The proposed strategy has led to the identification of two hits, having a novel scaffold in the landscape of CK1δ inhibitors and with an activity in the micromolar range.
- 12Langmead, C. J.; Andrews, S. P.; Congreve, M.; Errey, J. C.; Hurrell, E.; Marshall, F. H.; Mason, J. S.; Richardson, C. M.; Robertson, N.; Zhukov, A. Identification of novel adenosine A2A receptor antagonists by virtual screening. J. Med. Chem. 2012, 55 (5), 1904– 1909, DOI: 10.1021/jm201455yThere is no corresponding record for this reference.
- 13Jazayeri, A.; Andrews, S. P.; Marshall, F. H. Structurally enabled discovery of adenosine a2a receptor antagonists. Chem. Rev. 2017, 117 (1), 21– 37, DOI: 10.1021/acs.chemrev.6b0011913https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhtVWkur3L&md5=5e0a332b9f43eae4854f1681a79ff184Structurally Enabled Discovery of Adenosine A2A Receptor AntagonistsJazayeri, Ali; Andrews, Stephen P.; Marshall, Fiona H.Chemical Reviews (Washington, DC, United States) (2017), 117 (1), 21-37CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)A review. Over the past decade there has been a revolution in the field of G protein-coupled receptor (GPCR) structural biol. Many years of innovative research from different areas have come together to fuel this significant change in the fortunes of this field, which for many years was characterized by the paucity of high-resoln. structures. The detn. to succeed has been in part due to the recognized importance of these proteins as drug targets, and although the pharmaceutical industry has been focusing on these receptors, it can be justifiably argued and demonstrated that many of the approved and com. successful GPCR drugs can be significantly improved to increase efficacy and/or reduce undesired side effects. In addn., many validated targets in this class remain to be drugged. It is widely recognized that application of structure-based drug design approaches can help medicinal chemists a long way toward discovering better drugs. The achievement of structural biologists in providing high-resoln. insight is beginning to transform drug discovery efforts, and there are a no. of GPCR drugs that have been discovered by use of structural information that are in clin. development. This review aims to highlight the key developments that have brought success to GPCR structure resoln. efforts and exemplify the practical application of structural information for the discovery of adenosine A2A receptor antagonists that have potential to treat multiple conditions.
- 14Tian, S.; Wang, X.; Li, L.; Zhang, X.; Li, Y.; Zhu, F.; Hou, T.; Zhen, X. Discovery of Novel and Selective Adenosine A2A Receptor Antagonists for Treating Parkinson’s Disease through Comparative Structure-Based Virtual Screening. J. Chem. Inf. Model. 2017, 57 (6), 1474– 1487, DOI: 10.1021/acs.jcim.7b0018814https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXmvFamtb4%253D&md5=1d81fbc9dde5327024eaee18e944b926Discovery of Novel and Selective Adenosine A2A Receptor Antagonists for Treating Parkinson's Disease through Comparative Structure-Based Virtual ScreeningTian, Sheng; Wang, Xu; Li, Linlang; Zhang, Xiaohu; Li, Youyong; Zhu, Feng; Hou, Tingjun; Zhen, XuechuJournal of Chemical Information and Modeling (2017), 57 (6), 1474-1487CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)Among non-dopaminergic strategies for combating Parkinson's disease (PD), antagonism of the A2A adenosine receptor (AR) has emerged to show great potential. In this study, on the basis of two crystal structures of the A2A AR with the best capability to distinguish known antagonists from decoys, docking-based virtual screening (VS) was conducted to identify novel A2A AR antagonists. A total of 63 structurally diverse compds. identified by VS were submitted to exptl. testing, and 11 of them exhibited substantial activity against the A2A AR (Ki < 10 μM), including two compds. with Ki below 1 μM (compd. 43, 0.42 μM; compd. 51, 0.27 μM) and good A2A/A1 selectivity (fold < 0.1). Compds. 43 and 51 demonstrated antagonistic activity according to the results of cAMP measurements (cAMP IC50 = 1.67 and 1.80 μM, resp.) and showed good efficacy in the haloperidol-induced catalepsy (HIC) rat model for PD at doses of up to 30 mg/kg. Further lead optimization based on a substructure searching strategy led to the discovery of compd. 84 as an excellent A2A AR antagonist (A2AKi = 54 nM, A2A/A1 fold < 0.1, cAMP IC50 = 0.3 μM) that exhibited significant improvement in anti-PD efficacy in the HIC rat model.
- 15Lagarias, P.; Vrontaki, E.; Lambrinidis, G.; Stamatis, D.; Convertino, M.; Ortore, G.; Mavromoustakos, T.; Klotz, K. N.; Kolocouris, A. Discovery of Novel Adenosine Receptor Antagonists through a Combined Structure- and Ligand-Based Approach Followed by Molecular Dynamics Investigation of Ligand Binding Mode. J. Chem. Inf. Model. 2018, 58 (4), 794– 815, DOI: 10.1021/acs.jcim.7b0045515https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXjtlOktbw%253D&md5=c6e5de5973264c7dcab867f801e8bfa2Discovery of Novel Adenosine Receptor Antagonists through a Combined Structure- and Ligand-Based Approach Followed by Molecular Dynamics Investigation of Ligand Binding ModeLagarias, Panagiotis; Vrontaki, Eleni; Lambrinidis, George; Stamatis, Dimitrios; Convertino, Marino; Ortore, Gabriella; Mavromoustakos, Thomas; Klotz, Karl-Norbert; Kolocouris, AntoniosJournal of Chemical Information and Modeling (2018), 58 (4), 794-815CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)An intense effort is made by pharmaceutical and academic research labs. to identify and develop selective antagonists for each adenosine receptor (AR) subtype as potential clin. candidates for "soft" treatment of various diseases. Crystal structures of subtypes A2A and A1ARs offer exciting opportunities for structure-based drug design. In the first part of the present work, Maybridge HitFinder library of 14400 compds. was utilized to apply a combination of structure-based against the crystal structure of A2AAR and ligand-based methodologies. The docking poses were rescored by CHARMM energy minimization and calcn. of the desolvation energy using Poisson-Boltzmann equation electrostatics. Out of the eight selected and tested compds., five were found pos. hits (63% success). Although the project was initially focused on targeting A2AAR, the identified antagonists exhibited low micromolar or micromolar affinity against A2A/A3, ARs, or A3AR, resp. Based on these results, 19 compds. characterized by novel chemotypes were purchased and tested. Sixteen of them were identified as AR antagonists with affinity toward combinations of the AR family isoforms (A2A/A3, A1/A3, A1/A2A/A3, and A3). The second part of this work involves the performance of hundreds of mol. dynamics (MD) simulations of complexes between the ARs and a total of 27 ligands to resolve the binding interactions of the active compds., which were not achieved by docking calcns. alone. This computational work allowed the prediction of stable and unstable complexes which agree with the exptl. results of potent and inactive compds., resp. Of particular interest is that the 2-amino-thiophene-3-carboxamides, 3-acylamino-5-aryl-thiophene-2-carboxamides, and carbonyloxycarboximidamide derivs. were found to be selective and possess a micromolar to low micromolar affinity for the A3 receptor.
- 16Zwanzig, R. W. High-Temperature Equation of State by a Perturbation Method. I. Nonpolar Gases. J. Chem. Phys. 1954, 22 (8), 1420– 1426, DOI: 10.1063/1.174040916https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaG2cXnsFCgsw%253D%253D&md5=8b0bce75afc2097b1c178dab250389a9High-temperature equation of state by a perturbation method. I. Nonpolar gasesZwanzig, Robert W.Journal of Chemical Physics (1954), 22 (), 1420-6CODEN: JCPSA6; ISSN:0021-9606.A theoretical study was made of the equations of state of A and N at high temp. and ds. The intermol. potential was of the Lennard-Jones form, with an adjustable rigid sphere cutoff. A perturbation theory was developed, by which the thermodynamic properties of 1 system could be related to those of a slightly different system and to the difference in the intermol. potentials of the 2 systems. The unperturbed system was a rigid-sphere fluid, and the Lennard-Jones potential was the perturbation. The results were in fair agreement with expt. and can be used as an exptl. test of the theoretical rigid-sphere equation of state.
- 17Kollman, P. Free Energy Calculations: Applications to Chemical and Biochemical Phenomena. Chem. Rev. 1993, 93 (7), 2395– 2417, DOI: 10.1021/cr00023a00417https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK3sXmt1Sktr0%253D&md5=f12326b24734ea092996cf22efb6ebd6Free energy calculations: Applications to chemical and biochemical phenomenaKollman, PeterChemical Reviews (Washington, DC, United States) (1993), 93 (7), 2395-417CODEN: CHREAY; ISSN:0009-2665.A review with 252 refs. about applications of free energy calcns. employing mol. dynamics or Monte Carlo methods to a variety of chem. and biochem. phenomena. The focus is on applications of such calcns. to mol. solvation, mol. assocn., macromol. stability, and enzyme catalysis. The mols. discussed range from monovalent ions and small mols. to proteins and nucleic acids.
- 18Chen, D.; Ranganathan, A.; Ijzerman, A. P.; Siegal, G.; Carlsson, J. Complementarity between in silico and biophysical screening approaches in fragment-based lead discovery against the A2A adenosine receptor. J. Chem. Inf. Model. 2013, 53 (10), 2701– 2714, DOI: 10.1021/ci400315618https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtlWqsr%252FN&md5=9000cbd70a73169e869ac789fdea88adComplementarity between in Silico and Biophysical Screening Approaches in Fragment-Based Lead Discovery against the A2A Adenosine ReceptorChen, Dan; Ranganathan, Anirudh; Ijzerman, Adriaan P.; Siegal, Gregg; Carlsson, JensJournal of Chemical Information and Modeling (2013), 53 (10), 2701-2714CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)Fragment-based lead discovery (FBLD) is becoming an increasingly important method in drug development. We have explored the potential to complement NMR-based biophys. screening of chem. libraries with mol. docking in FBLD against the A2A adenosine receptor (A2AAR), a drug target for inflammation and Parkinson's disease. Prior to an NMR-based screen of a fragment library against the A2AAR, mol. docking against a crystal structure was used to rank the same set of mols. by their predicted affinities. Mol. docking was able to predict four out of the five orthosteric ligands discovered by NMR among the top 5% of the ranked library, suggesting that structure-based methods could be used to prioritize among primary hits from biophys. screens. In addn., three fragments that were top-ranked by mol. docking, but had not been picked up by the NMR-based method, were demonstrated to be A2AAR ligands. While biophys. approaches for fragment screening are typically limited to a few thousand compds., the docking screen was extended to include 328,000 com. available fragments. Twenty-two top-ranked compds. were tested in radioligand binding assays, and 14 of these were A2AAR ligands with Ki values ranging from 2 to 240 μM. Optimization of fragments was guided by mol. dynamics simulations and free energy calcns. The results illuminate strengths and weaknesses of mol. docking and demonstrate that this method can serve as a valuable complementary tool to biophys. screening in FBLD.
- 19Matricon, P.; Ranganathan, A.; Warnick, E.; Gao, Z. G.; Rudling, A.; Lambertucci, C.; Marucci, G.; Ezzati, A.; Jaiteh, M.; Dal Ben, D. Fragment optimization for GPCRs by molecular dynamics free energy calculations: Probing druggable subpockets of the A 2A adenosine receptor binding site. Sci. Rep. 2017, 7 (1), 6398, DOI: 10.1038/s41598-017-04905-019https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC1cfhtFKktQ%253D%253D&md5=05f0210d0cbe5f4cd58677b8747dffa0Fragment optimization for GPCRs by molecular dynamics free energy calculations: Probing druggable subpockets of the A 2A adenosine receptor binding siteMatricon Pierre; Jaiteh Mariama; Carlsson Jens; Ranganathan Anirudh; Rudling Axel; Ezzati Aitakin; Warnick Eugene; Gao Zhan-Guo; Jacobson Kenneth A; Lambertucci Catia; Marucci Gabriella; Dal Ben DiegoScientific reports (2017), 7 (1), 6398 ISSN:.Fragment-based lead discovery is becoming an increasingly popular strategy for drug discovery. Fragment screening identifies weakly binding compounds that require optimization to become high-affinity leads. As design of leads from fragments is challenging, reliable computational methods to guide optimization would be invaluable. We evaluated using molecular dynamics simulations and the free energy perturbation method (MD/FEP) in fragment optimization for the A2A adenosine receptor, a pharmaceutically relevant G protein-coupled receptor. Optimization of fragments exploring two binding site subpockets was probed by calculating relative binding affinities for 23 adenine derivatives, resulting in strong agreement with experimental data (R(2) = 0.78). The predictive power of MD/FEP was significantly better than that of an empirical scoring function. We also demonstrated the potential of the MD/FEP to assess multiple binding modes and to tailor the thermodynamic profile of ligands during optimization. Finally, MD/FEP was applied prospectively to optimize three nonpurine fragments, and predictions for 12 compounds were evaluated experimentally. The direction of the change in binding affinity was correctly predicted in a majority of the cases, and agreement with experiment could be improved with rigorous parameter derivation. The results suggest that MD/FEP will become a powerful tool in structure-driven optimization of fragments to lead candidates.
- 20Matricon, P.; Vo, D. D.; Gao, Z. G.; Kihlberg, J.; Jacobson, K. A.; Carlsson, J. Fragment-based design of selective GPCR ligands guided by free energy simulations. Chem. Commun. 2021, 57 (92), 12305– 12308, DOI: 10.1039/D1CC03202J20https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXitlWgtbfK&md5=fb089f3548bf70c040f7e5a2370cc817Fragment-based design of selective GPCR ligands guided by free energy simulationsMatricon, Pierre; Vo, Duc Duy; Gao, Zhan-Guo; Kihlberg, Jan; Jacobson, Kenneth A.; Carlsson, JensChemical Communications (Cambridge, United Kingdom) (2021), 57 (92), 12305-12308CODEN: CHCOFS; ISSN:1359-7345. (Royal Society of Chemistry)Fragment-based drug discovery relies on successful optimization of weakly binding ligands for affinity and selectivity. Herein, we explored strategies for structure-based evolution of fragments binding to a G protein-coupled receptor. Mol. dynamics simulations combined with rigorous free energy calcns. guided synthesis of nanomolar ligands with up to >1000-fold improvements of binding affinity and close to 40-fold subtype selectivity.
- 21Jespers, W.; Oliveira, A.; Prieto-Díaz, R.; Majellaro, M.; Åqvist, J.; Sotelo, E.; Gutiérrez-de-Terán, H. Structure-Based Design of Potent and Selective Ligands at the Four Adenosine Receptors. Molecules 2017, 22 (11), 1945, DOI: 10.3390/molecules2211194521https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXisVGjur8%253D&md5=b22386d14fb1d7d5d20bfa8346f8c67bStructure-based design of potent and selective ligands at the four adenosine receptorsJespers, Willem; Oliveira, Ana; Prieto-Diaz, Ruben; Majellaro, Maria; Aqvist, Johan; Sotelo, Eddy; Gutierrez-de-Teran, HugoMolecules (2017), 22 (11), 1945/1-1945/17CODEN: MOLEFW; ISSN:1420-3049. (MDPI AG)The four receptors that signal for adenosine, A1, A2A, A2B and A3 ARs, belong to the superfamily of G protein-coupled receptors (GPCRs). They mediate a no. of (patho)physiol. functions and have attracted the interest of the biopharmaceutical sector for decades as potential drug targets. The many crystal structures of the A2A, and lately the A1 ARs, allow for the use of advanced computational, structure-based ligand design methodologies. Over the last decade, we have assessed the efficient synthesis of novel ligands specifically addressed to each of the four ARs. We herein review and update the results of this program with particular focus on mol. dynamics (MD) and free energy perturbation (FEP) protocols. The first in silico mutagenesis on the A1AR here reported allows understanding the specificity and high affinity of the xanthine-antagonist 8-Cyclopentyl-1,3-dipropylxanthine (DPCPX). On the A2AAR, we demonstrate how FEP simulations can distinguish the conformational selectivity of a recent series of partial agonists. These novel results are complemented with the revision of the first series of enantiospecific antagonists on the A2BAR, and the use of FEP as a tool for bioisosteric design on the A3AR.
- 22Mallo-Abreu, A.; Prieto-Díaz, R.; Jespers, W.; Azuaje, J.; Majellaro, M.; Velando, C.; García-Mera, X.; Caamaño, O.; Brea, J.; Loza, M. I. Nitrogen-Walk Approach to Explore Bioisosteric Replacements in a Series of Potent A 2B Adenosine Receptor Antagonists. J. Med. Chem. 2020, 63 (14), 7721– 7739, DOI: 10.1021/acs.jmedchem.0c0056422https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXht1amu73E&md5=33fad82df198f133dad6b4d866d36b42A Nitrogen-Walk Approach to Explore Bioisosteric Replacements in a Series of Potent A2B Adenosine Receptor AntagonistsMallo-Abreu, Ana; Prieto-Diaz, Ruben; Jespers, Willem; Azuaje, Jhonny; Majellaro, Maria; Velando, Carmen; Garcia-Mera, Xerardo; Caamano, Olga; Brea, Jose; Loza, Maria I.; Gutierrez-de-Teran, Hugo; Sotelo, EddyJournal of Medicinal Chemistry (2020), 63 (14), 7721-7739CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)A systematic exploration of bioisosteric replacements for furan and thiophene cores in a series of potent A2BAR antagonists was carried out using the nitrogen-walk approach. A collection of 42 novel alkyl 4-substituted-2-methyl-1,4-dihydrobenzo[4,5]imidazo[1,2-a]pyrimidine-3-carboxylates I [R = H, cyclopentyl, Ph, etc.; R1 = Et, i-Pr], which contain 18 different pentagonal heterocyclic frameworks at position 4, was synthesized and evaluated. This study enabled the identication of new ligands that combine remarkable affinity (Ki < 30 nM) and exquisite selectivity. The SAR trends identified were substantiated by a mol. modeling study, based on a receptor-driven docking model and including a systematic free energy perturbation (FEP) study. Preliminary evaluation of the CYP3A4 and CYP2D6 inhibitory activity in optimized ligands evidenced weak and negligible activity resp. The stereospecific interaction between hA2BAR and the eutomer of the most attractive novel antagonist (S)-II (Ki = 3.66 nM) was validated.
- 23Decherchi, S.; Cavalli, A. Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation. Chem. Rev. 2020, 120 (23), 12788– 12833, DOI: 10.1021/acs.chemrev.0c0053423https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhvFKltrzF&md5=75c83143290022930959bdc1abaab8e9Thermodynamics and Kinetics of Drug-Target Binding by Molecular SimulationDecherchi, Sergio; Cavalli, AndreaChemical Reviews (Washington, DC, United States) (2020), 120 (23), 12788-12833CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)A review. Computational studies play an increasingly important role in chem. and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compds. by estg. their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estg. the rate coeffs. of very slow processes of pharmacol. interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered mol. dynamics and metadynamics. We review recent applications to pharmacol. and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to exptl. and clin. approaches.
- 24Lagarias, P.; Barkan, K.; Tzortzini, E.; Stampelou, M.; Vrontaki, E.; Ladds, G.; Kolocouris, A. Insights to the Binding of a Selective Adenosine A3 Receptor Antagonist Using Molecular Dynamic Simulations, MM-PBSA and MM-GBSA Free Energy Calculations, and Mutagenesis. J. Chem. Inf. Model. 2019, 59 (12), 5183– 5197, DOI: 10.1021/acs.jcim.9b0075124https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXitFGrsL7I&md5=3ad8c6ddc392513238915acb47a00fa0Insights to the Binding of a Selective Adenosine A3 Receptor Antagonist Using Molecular Dynamic Simulations, MM-PBSA and MM-GBSA Free Energy Calculations, and MutagenesisLagarias, Panagiotis; Barkan, Kerry; Tzortzini, Eva; Stampelou, Margarita; Vrontaki, Eleni; Ladds, Graham; Kolocouris, AntoniosJournal of Chemical Information and Modeling (2019), 59 (12), 5183-5197CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)Adenosine A3 receptor (A3R) is a promising drug target cancer and for a no. of other conditions like inflammatory diseases, including asthma and rheumatoid arthritis, glaucoma, chronic obstructive pulmonary disease, and ischemic injury. Currently, there is no exptl. detd. structure of A3R. We explored the binding profile of O4-{[3-(2,6-dichlorophenyl)-5-methylisoxazol-4-yl]carbonyl}-2-methyl-1,3-thiazole-4-carbohydroximamide (K18), which is a new specific and competitive antagonist at the orthosteric binding site of A3R. MD simulations and MM-GBSA calcns. of the WT A3R in complex with K18 combined with in vitro mutagenic studies show that the most plausible binding conformation for the dichlorophenyl group of K18 is oriented toward trans-membrane helixes (TM) 5, 6 and reveal important residues for binding. Further, MM-GBSA calcns. distinguish mutations that reduce or maintain or increase antagonistic activity. Our studies show that selectivity of K18 toward A3R is defined not only by direct interactions with residues within the orthosteric binding area but also by remote residues playing a significant role. Although V1695.30 is considered to be a selectivity filter for A3R binders, when it was mutated to glutamic acid, K18 maintained antagonistic potency, in agreement with our previous results obtained for agonists binding profile investigation. Mutation of the direct interacting residue L903.32 in the low region and the remote L2647.35 in the middle/upper region to alanine increases antagonistic potency, suggesting an empty space in the orthosteric area available for increasing antagonist potency. These results approve the computational model for the description of K18 binding at A3R, which we previously performed for agonists binding to A3R, and the design of more effective antagonists based on K18.
- 25Barkan, K.; Lagarias, P.; Stampelou, M.; Stamatis, D.; Hoare, S.; Safitri, D.; Klotz, K. N.; Vrontaki, E.; Kolocouris, A.; Ladds, G. Pharmacological characterisation of novel adenosine A3 receptor antagonists. Sci. Rep. 2020, 10 (1), 20781, DOI: 10.1038/s41598-020-74521-y25https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXisFWltbrL&md5=3710ac8acc7ee7d2b072c0ee95fa3bffPharmacological characterization of novel adenosine A3 receptor antagonistsBarkan, Kerry; Lagarias, Panagiotis; Stampelou, Margarita; Stamatis, Dimitrios; Hoare, Sam; Safitri, Dewi; Klotz, Karl-Norbert; Vrontaki, Eleni; Kolocouris, Antonios; Ladds, GrahamScientific Reports (2020), 10 (1), 20781CODEN: SRCEC3; ISSN:2045-2322. (Nature Research)The adenosine A3 receptor (A3R) belongs to a family of four adenosine receptor (AR) subtypes which all play distinct roles throughout the body. A3R antagonists have been described as potential treatments for numerous diseases including asthma. Given the similarity between (adenosine receptors) orthosteric binding sites, obtaining highly selective antagonists is a challenging but crit. task. Here we screen 39 potential A3R, antagonists using agonist-induced inhibition of cAMP. Pos. hits were assessed for AR subtype selectivity through cAMP accumulation assays. The antagonist affinity was detd. using Schild anal. (pA2 values) and fluorescent ligand binding. Structure-activity relationship investigations revealed that loss of the 3-(dichlorophenyl)-isoxazolyl moiety or the arom. nitrogen heterocycle with nitrogen at α-position to the carbon of carboximidamide group significantly attenuated K18 antagonistic potency. Mutagenic studies supported by mol. dynamic simulations combined with Mol. Mechanics-Poisson Boltzmann Surface Area calcns. identified the residues important for binding in the A3R orthosteric site. We demonstrate that K18, which contains a 3-(dichlorophenyl)-isoxazole group connected through carbonyloxycarboximidamide fragment with a 1,3-thiazole ring, is a specific A3R (< 1 μM) competitive antagonist. Finally, we introduce a model that enables ests. of the equil. binding affinity for rapidly disassocg. compds. from real-time fluorescent ligand-binding studies. These results demonstrate the pharmacol. characterization of a selective competitive A3R antagonist and the description of its orthosteric binding mode. Our findings may provide new insights for drug discovery.
- 26Guo, D.; Heitman, L. H.; Ijzerman, A. P. Kinetic Aspects of the Interaction between Ligand and G Protein-Coupled Receptor: The Case of the Adenosine Receptors. Chem. Rev. 2017, 117 (1), 38– 66, DOI: 10.1021/acs.chemrev.6b0002526https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XmtVymsro%253D&md5=1b2b1dd2404cc1c64c27f9f0fab9a6abKinetic Aspects of the Interaction between Ligand and G Protein-Coupled Receptor: The Case of the Adenosine ReceptorsGuo, Dong; Heitman, Laura H.; Ijzerman, Adriaan P.Chemical Reviews (Washington, DC, United States) (2017), 117 (1), 38-66CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)Ligand-receptor binding kinetics is an emerging topic in the drug research community. Over the past years, medicinal chem. approaches from a kinetic perspective have been increasingly applied to G protein-coupled receptors including the adenosine receptors (AR), which are involved in a plethora of physiol. and pathol. conditions. The study of ligand-AR binding kinetics offers room for detailed structure-kinetics relationships next to more traditional structure-activity relationships. Their combination may facilitate the triage of candidate compds. in hit-to-lead campaigns. Furthermore, kinetic studies also help in understanding AR allosterism. Allosteric modulation may yield a change in the activity and conformation of a receptor resulting from the binding of a compd. at a site distinct from where the endogenous agonist adenosine binds. Hence, in this Review, we summarize available data and evidence for the binding kinetics of orthosteric and allosteric AR ligands. We hope this Review will raise awareness to consider the kinetic aspects of drug-target interactions on both ARs and other drug targets.
- 27Kirkwood, J. G. Statistical mechanics of fluid mixtures. J. Chem. Phys. 1935, 3, 300– 313, DOI: 10.1063/1.174965727https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaA2MXjt1OrsA%253D%253D&md5=224deb235ab0d87ec9adf44f983dc686Statistical mechanics of fluid mixturesKirkwood, John G.Journal of Chemical Physics (1935), 3 (), 300-13CODEN: JCPSA6; ISSN:0021-9606.Math. Expressions for the chem. potentials of the components of gas mixts. and liquid solns. are derived.
- 28Kollman, P. Free Energy Calculations: Applications to Chemical and Biochemical Phenomena. Chem. Rev. 1993, 93 (7), 2395– 2417, DOI: 10.1021/cr00023a00428https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK3sXmt1Sktr0%253D&md5=f12326b24734ea092996cf22efb6ebd6Free energy calculations: Applications to chemical and biochemical phenomenaKollman, PeterChemical Reviews (Washington, DC, United States) (1993), 93 (7), 2395-417CODEN: CHREAY; ISSN:0009-2665.A review with 252 refs. about applications of free energy calcns. employing mol. dynamics or Monte Carlo methods to a variety of chem. and biochem. phenomena. The focus is on applications of such calcns. to mol. solvation, mol. assocn., macromol. stability, and enzyme catalysis. The mols. discussed range from monovalent ions and small mols. to proteins and nucleic acids.
- 29Lenselink, E. B.; Louvel, J.; Forti, A. F.; van Veldhoven, J. P. D.; de Vries, H.; Mulder-Krieger, T.; McRobb, F. M.; Negri, A.; Goose, J.; Abel, R. Predicting Binding Affinities for GPCR Ligands Using Free-Energy Perturbation. ACS Omega 2016, 1 (2), 293– 304, DOI: 10.1021/acsomega.6b0008629https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhsVWrsLbE&md5=dc8c6c56657c64608b4c66e65b630413Predicting Binding Affinities for GPCR Ligands Using Free-Energy PerturbationLenselink, Eelke B.; Louvel, Julien; Forti, Anna F.; van Veldhoven, Jacobus P. D.; de Vries, Henk; Mulder-Krieger, Thea; McRobb, Fiona M.; Negri, Ana; Goose, Joseph; Abel, Robert; van Vlijmen, Herman W. T.; Wang, Lingle; Harder, Edward; Sherman, Woody; IJzerman, Adriaan P.; Beuming, ThijsACS Omega (2016), 1 (2), 293-304CODEN: ACSODF; ISSN:2470-1343. (American Chemical Society)The rapid growth of structural information for G-protein-coupled receptors (GPCRs) has led to a greater understanding of their structure, function, selectivity, and ligand binding. Although novel ligands have been identified using methods such as virtual screening, computationally driven lead optimization has been possible only in isolated cases because of challenges assocd. with predicting binding free energies for related compds. Here, the authors provide a systematic characterization of the performance of free-energy perturbation (FEP) calcns. to predict relative binding free energies of congeneric ligands binding to GPCR targets using a consistent protocol and no adjustable parameters. Using the FEP+ package, first the authors validated the protocol, which includes a full lipid bilayer and explicit solvent, by predicting the binding affinity for a total of 45 different ligands across four different GPCRs (adenosine A2AAR, β1 adrenergic, CXCR4 chemokine, and δ opioid receptors). Comparison with exptl. binding affinity measurements revealed a highly predictive ranking correlation (av. spearman ρ = 0.55) and low root-mean-square error (0.80 kcal/mol). Next, the authors applied FEP+ in a prospective project, where the authors predicted the affinity of novel, potent adenosine A2A receptor (A2AR) antagonists. Four novel compds. were synthesized and tested in a radioligand displacement assay, yielding affinity values in the nanomolar range. The affinity of two out of the four novel ligands (plus three previously reported compds.) was correctly predicted (within 1 kcal/mol), including one compd. with approx. a 10-fold increase in affinity compared to the starting compd. Detailed analyses of the simulations underlying the predictions provided insights into the structural basis for the two cases where the affinity was overpredicted. Taken together, these results establish a protocol for systematically applying FEP+ to GPCRs and provide guidelines for identifying potent mols. in drug discovery lead optimization projects.
- 30Deflorian, F.; Perez-Benito, L.; Lenselink, E. B.; Congreve, M.; van Vlijmen, H. W. T.; Mason, J. S.; Graaf, C. d.; Tresadern, G. Accurate Prediction of GPCR Ligand Binding Affinity with Free Energy Perturbation. J. Chem. Inf. Model. 2020, 60 (11), 5563– 5579, DOI: 10.1021/acs.jcim.0c0044930https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhtFKhtbbK&md5=a6dfdd7d6258b3d539d9d51596a238f0Accurate Prediction of GPCR Ligand Binding Affinity with Free Energy PerturbationDeflorian, Francesca; Perez-Benito, Laura; Lenselink, Eelke B.; Congreve, Miles; van Vlijmen, Herman W. T.; Mason, Jonathan S.; Graaf, Chris de; Tresadern, GaryJournal of Chemical Information and Modeling (2020), 60 (11), 5563-5579CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)The computational prediction of relative binding free energies is a crucial goal for drug discovery, and G protein-coupled receptors (GPCRs) are arguably the most important drug target class. However, they present increased complexity to model compared to sol. globular proteins. Despite breakthroughs, exptl. X-ray crystal and cryo-EM structures are challenging to attain, meaning computational models of the receptor and ligand binding mode are sometimes necessary. This leads to uncertainty in understanding ligand-protein binding induced changes such as, water positioning and displacement, side chain positioning, hydrogen bond networks, and the overall structure of the hydration shell around the ligand and protein. In other words, the very elements that define structure activity relationships (SARs) and are crucial for accurate binding free energy calcns. are typically more uncertain for GPCRs. In this work we use free energy perturbation (FEP) to predict the relative binding free energies for ligands of two different GPCRs. We pinpoint the key aspects for success such as the important role of key water mols., amino acid ionization states, and the benefit of equilibration with specific ligands. Initial calcns. following typical FEP setup and execution protocols delivered no correlation with expt., but we show how results are improved in a logical and systematic way. This approach gave, in the best cases, a coeff. of detn. (R2) compared with expt. in the range of 0.6-0.9 and mean unsigned errors compared to expt. of 0.6-0.7 kcal/mol. We anticipate that our findings will be applicable to other difficult-to-model protein ligand data sets and be of wide interest for the community to continue improving FE binding energy predictions.
- 31Wan, S.; Potterton, A.; Husseini, F. S.; Wright, D. W.; Heifetz, A.; Malawski, M.; Townsend-Nicholson, A.; Coveney, P. V. Hit-to-lead and lead optimization binding free energy calculations for G protein-coupled receptors. Interfaces: Focus 2020, 10 (6), 20190128, DOI: 10.1098/rsfs.2019.0128There is no corresponding record for this reference.
- 32Stampelou, M.; Suchankova, A.; Tzortzini, E.; Dhingra, L.; Barkan, K.; Lougiakis, N.; Marakos, P.; Pouli, N.; Ladds, G.; Kolocouris, A. Dual A1/A3 Adenosine Receptor Antagonists: Binding Kinetics and Structure-Activity Relationship Studies Using Mutagenesis and Alchemical Binding Free Energy Calculations. J. Med. Chem. 2022, 65 (19), 13305– 13327, DOI: 10.1021/acs.jmedchem.2c0112332https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XisFShsbfL&md5=fd5d06c03cd10369c9de0b6aeac6c05eDual A1/A3 Adenosine Receptor Antagonists: Binding Kinetics and Structure-Activity Relationship Studies Using Mutagenesis and Alchemical Binding Free Energy CalculationsStampelou, Margarita; Suchankova, Anna; Tzortzini, Efpraxia; Dhingra, Lakshiv; Barkan, Kerry; Lougiakis, Nikolaos; Marakos, Panagiotis; Pouli, Nicole; Ladds, Graham; Kolocouris, AntoniosJournal of Medicinal Chemistry (2022), 65 (19), 13305-13327CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)Drugs targeting adenosine receptors (AR) can provide treatment for diseases. We report the identification of 7-(phenylamino)-pyrazolo[3,4-c]pyridines L2-L10, A15, and A17 as low-micromolar to low-nanomolar A1R/A3R dual antagonists, with 3-phenyl-5-cyano-7-(trimethoxyphenylamino)-pyrazolo[3,4-c]pyridine A17 displaying the highest affinity at both receptors with a long residence time of binding, as detd. using a NanoBRET-based assay. Two binding orientations of A17 (I) produce stable complexes inside the orthosteric binding area of A1R in mol. dynamics (MD) simulations, and we selected the most plausible orientation based on the agreement with alanine mutagenesis supported by affinity expts. Interestingly, for drug design purposes, the mutation of L2506.51 to alanine increased the binding affinity of A17 at A1R. We explored the structure-activity relationships against A1R using alchem. binding free energy calcns. with the thermodn. integration coupled with the MD simulation (TI/MD) method, applied on the whole G-protein-coupled receptor-membrane system, which showed a good agreement (r = 0.73) between calcd. and exptl. relative binding free energies.
- 33Pohorille, A.; Jarzynski, C.; Chipot, C. Good practices in free-energy calculations. J. Phys. Chem. B 2010, 114 (32), 10235– 10253, DOI: 10.1021/jp102971x33https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXptFOmtrc%253D&md5=4beae4323556ec59ab57177b6670303bGood Practices in Free-Energy CalculationsPohorille, Andrew; Jarzynski, Christopher; Chipot, ChristopheJournal of Physical Chemistry B (2010), 114 (32), 10235-10253CODEN: JPCBFK; ISSN:1520-6106. (American Chemical Society)As access to computational resources continues to increase, free-energy calcns. have emerged as a powerful tool that can play a predictive role in a wide range of research areas. Yet, the reliability of these calcns. can often be improved significantly if a no. of precepts, or good practices, are followed. Although the theory upon which these good practices rely has largely been known for many years, it is often overlooked or simply ignored. In other cases, the theor. developments are too recent for their potential to be fully grasped and merged into popular platforms for the computation of free-energy differences. In this contribution, the current best practices for carrying out free-energy calcns. using free energy perturbation and nonequil. work methods are discussed, demonstrating that at little to no addnl. cost, free-energy ests. could be markedly improved and bounded by meaningful error ests. Monitoring the probability distributions that underlie the transformation between the states of interest, performing the calcn. bidirectionally, stratifying the reaction pathway, and choosing the most appropriate paradigms and algorithms for transforming between states offer significant gains in both accuracy and precision.
- 34Mazziotta, C.; Rotondo, J. C.; Lanzillotti, C.; Campione, G.; Martini, F.; Tognon, M. Cancer biology and molecular genetics of A3 adenosine receptor. Oncogene 2022, 41 (3), 301– 308, DOI: 10.1038/s41388-021-02090-z34https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXisVegsbnP&md5=be04ab19e047a7af23667a44d0225a8eCancer biology and molecular genetics of A3 adenosine receptorMazziotta, Chiara; Rotondo, John Charles; Lanzillotti, Carmen; Campione, Giulia; Martini, Fernanda; Tognon, MauroOncogene (2022), 41 (3), 301-308CODEN: ONCNES; ISSN:0950-9232. (Nature Portfolio)A review. A3 adenosine receptor (A3AR) is a cell membrane protein, which has been found to be overexpressed in a large no. of cancer types. This receptor plays an important role in cancer by interacting with adenosine. Specifically, A3AR has a dual nature in different pathophysiol. conditions, as it is expressed according to tissue type and stimulated by an adenosine dose-dependent manner. A3AR activation leads to tumor growth, cell proliferation and survival in some cases, while triggering cytostatic and apoptotic pathways in others. This aims to describe the most relevant aspects of A3AR activation and its ligands whereas it summarizes A3AR activities in cancer. Progress in the field of A3AR modulators, with a potential therapeutic role in cancer treatment are reported, as well.
- 35Kalash, L.; Winfield, I.; Safitri, D.; Bermudez, M.; Carvalho, S.; Glen, R.; Ladds, G.; Bender, A. Structure-based identification of dual ligands at the A2AR and PDE10A with anti-proliferative effects in lung cancer cell-lines. J. Cheminf. 2021, 13 (1), 17, DOI: 10.1186/s13321-021-00492-5There is no corresponding record for this reference.
- 36Maier, J. A.; Martinez, C.; Kasavajhala, K.; Wickstrom, L.; Hauser, K. E.; Simmerling, C. ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. J. Chem. Theory Comput. 2015, 11 (8), 3696– 3713, DOI: 10.1021/acs.jctc.5b0025536https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtFequ7rN&md5=7b803577b3b6912cc6750cfbd356596eff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SBMaier, James A.; Martinez, Carmenza; Kasavajhala, Koushik; Wickstrom, Lauren; Hauser, Kevin E.; Simmerling, CarlosJournal of Chemical Theory and Computation (2015), 11 (8), 3696-3713CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)Mol. mechanics is powerful for its speed in atomistic simulations, but an accurate force field is required. The Amber ff99SB force field improved protein secondary structure balance and dynamics from earlier force fields like ff99, but weaknesses in side chain rotamer and backbone secondary structure preferences have been identified. Here, we performed a complete refit of all amino acid side chain dihedral parameters, which had been carried over from ff94. The training set of conformations included multidimensional dihedral scans designed to improve transferability of the parameters. Improvement in all amino acids was obtained as compared to ff99SB. Parameters were also generated for alternate protonation states of ionizable side chains. Av. errors in relative energies of pairs of conformations were under 1.0 kcal/mol as compared to QM, reduced 35% from ff99SB. We also took the opportunity to make empirical adjustments to the protein backbone dihedral parameters as compared to ff99SB. Multiple small adjustments of φ and ψ parameters were tested against NMR scalar coupling data and secondary structure content for short peptides. The best results were obtained from a phys. motivated adjustment to the φ rotational profile that compensates for lack of ff99SB QM training data in the β-ppII transition region. Together, these backbone and side chain modifications (hereafter called ff14SB) not only better reproduced their benchmarks, but also improved secondary structure content in small peptides and reprodn. of NMR χ1 scalar coupling measurements for proteins in soln. We also discuss the Amber ff12SB parameter set, a preliminary version of ff14SB that includes most of its improvements.
- 37Kaminski, G. A.; Friesner, R. A.; Tirado-Rives, J.; Jorgensen, W. L. Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate Quantum Chemical Calculations on Peptides. J. Phys. Chem. B 2001, 105 (28), 6474– 6487, DOI: 10.1021/jp003919d37https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3MXislKhsLk%253D&md5=3ff059626977ee7f6342466f5820f5b7Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate Quantum Chemical Calculations on PeptidesKaminski, George A.; Friesner, Richard A.; Tirado-Rives, Julian; Jorgensen, William L.Journal of Physical Chemistry B (2001), 105 (28), 6474-6487CODEN: JPCBFK; ISSN:1089-5647. (American Chemical Society)We present results of improving the OPLS-AA force field for peptides by means of refitting the key Fourier torsional coeffs. The fitting technique combines using accurate ab initio data as the target, choosing an efficient fitting subspace of the whole potential-energy surface, and detg. wts. for each of the fitting points based on magnitudes of the potential-energy gradient. The av. energy RMS deviation from the LMP2/cc-pVTZ(-f)//HF/6-31G** data is reduced by ∼40% from 0.81 to 0.47 kcal/mol as a result of the fitting for the electrostatically uncharged dipeptides. Transferability of the parameters is demonstrated by using the same alanine dipeptide-fitted backbone torsional parameters for all of the other dipeptides (with the appropriate side-chain refitting) and the alanine tetrapeptide. Parameters of nonbonded interactions have also been refitted for the sulfur-contg. dipeptides (cysteine and methionine), and the validity of the new Coulombic charges and the van der Waals σ's and ε's is proved through reproducing gas-phase energies of complex formation heats of vaporization and densities of pure model liqs. Moreover, a novel approach to fitting torsional parameters for electrostatically charged mol. systems has been presented and successfully tested on five dipeptides with charged side chains.
- 38Genheden, S.; Ryde, U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discovery 2015, 10 (5), 449– 461, DOI: 10.1517/17460441.2015.103293638https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXntFGktr8%253D&md5=b123b88809f275564f95a2271ebd159fThe MM/PBSA and MM/GBSA methods to estimate ligand-binding affinitiesGenheden, Samuel; Ryde, UlfExpert Opinion on Drug Discovery (2015), 10 (5), 449-461CODEN: EODDBX; ISSN:1746-0441. (Informa Healthcare)Introduction: The mol. mechanics energies combined with the Poisson-Boltzmann or generalized Born and surface area continuum solvation (MM/PBSA and MM/GBSA) methods are popular approaches to est. the free energy of the binding of small ligands to biol. macromols. They are typically based on mol. dynamics simulations of the receptor-ligand complex and are therefore intermediate in both accuracy and computational effort between empirical scoring and strict alchem. perturbation methods. They have been applied to a large no. of systems with varying success. Areas covered: The authors review the use of MM/PBSA and MM/GBSA methods to calc. ligand-binding affinities, with an emphasis on calibration, testing and validation, as well as attempts to improve the methods, rather than on specific applications. Expert opinion: MM/PBSA and MM/GBSA are attractive approaches owing to their modular nature and that they do not require calcns. on a training set. They have been used successfully to reproduce and rationalize exptl. findings and to improve the results of virtual screening and docking. However, they contain several crude and questionable approxns., for example, the lack of conformational entropy and information about the no. and free energy of water mols. in the binding site. Moreover, there are many variants of the method and their performance varies strongly with the tested system. Likewise, most attempts to ameliorate the methods with more accurate approaches, for example, quantum-mech. calcns., polarizable force fields or improved solvation have deteriorated the results.
- 39Tian, C.; Kasavajhala, K.; Belfon, K. A. A.; Raguette, L.; Huang, H.; Migues, A. N.; Bickel, J.; Wang, Y.; Pincay, J.; Wu, Q. ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution. J. Chem. Theory Comput. 2020, 16 (1), 528– 552, DOI: 10.1021/acs.jctc.9b0059139https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB3MjnvFGisw%253D%253D&md5=7cbaecffea06e4bf7ccecb7f1d0a0f4dff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in SolutionTian Chuan; Kasavajhala Koushik; Belfon Kellon A A; Raguette Lauren; Huang He; Bickel John; Wang Yuzhang; Pincay Jorge; Simmerling Carlos; Tian Chuan; Kasavajhala Koushik; Belfon Kellon A A; Raguette Lauren; Huang He; Migues Angela N; Wang Yuzhang; Simmerling Carlos; Wu QinJournal of chemical theory and computation (2020), 16 (1), 528-552 ISSN:.Molecular dynamics (MD) simulations have become increasingly popular in studying the motions and functions of biomolecules. The accuracy of the simulation, however, is highly determined by the molecular mechanics (MM) force field (FF), a set of functions with adjustable parameters to compute the potential energies from atomic positions. However, the overall quality of the FF, such as our previously published ff99SB and ff14SB, can be limited by assumptions that were made years ago. In the updated model presented here (ff19SB), we have significantly improved the backbone profiles for all 20 amino acids. We fit coupled φ/ψ parameters using 2D φ/ψ conformational scans for multiple amino acids, using as reference data the entire 2D quantum mechanics (QM) energy surface. We address the polarization inconsistency during dihedral parameter fitting by using both QM and MM in aqueous solution. Finally, we examine possible dependency of the backbone fitting on side chain rotamer. To extensively validate ff19SB parameters, and to compare to results using other Amber models, we have performed a total of ∼5 ms MD simulations in explicit solvent. Our results show that after amino-acid-specific training against QM data with solvent polarization, ff19SB not only reproduces the differences in amino-acid-specific Protein Data Bank (PDB) Ramachandran maps better but also shows significantly improved capability to differentiate amino-acid-dependent properties such as helical propensities. We also conclude that an inherent underestimation of helicity is present in ff14SB, which is (inexactly) compensated for by an increase in helical content driven by the TIP3P bias toward overly compact structures. In summary, ff19SB, when combined with a more accurate water model such as OPC, should have better predictive power for modeling sequence-specific behavior, protein mutations, and also rational protein design. Of the explicit water models tested here, we recommend use of OPC with ff19SB.
- 40Stampelou, M.; Ladds, G.; Kolocouris, A. Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A3 Receptor. J. Phys. Chem. B 2024, 128, 914– 936, DOI: 10.1021/acs.jpcb.3c05986There is no corresponding record for this reference.
- 41Heo, L.; Feig, M. Multi-state modeling of G-protein coupled receptors at experimental accuracy. Proteins: Struct., Funct., Bioinf. 2022, 90 (11), 1873– 1885, DOI: 10.1002/prot.2638241https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38Xht12ltr7F&md5=a75564614a8a1be233a0269ed620efdaMulti-state modeling of G-protein coupled receptors at experimental accuracyHeo, Lim; Feig, MichaelProteins: Structure, Function, and Bioinformatics (2022), 90 (11), 1873-1885CODEN: PSFBAF; ISSN:1097-0134. (Wiley-Blackwell)The family of G-protein coupled receptors (GPCRs) is one of the largest protein families in the human genome. GPCRs transduct chem. signals from extracellular to intracellular regions via a conformational switch between active and inactive states upon ligand binding. While exptl. structures of GPCRs remain limited, high-accuracy computational predictions are now possible with AlphaFold2. However, AlphaFold2 only predicts one state and is biased toward either the active or inactive conformation depending on the GPCR class. Here, a multi-state prediction protocol is introduced that extends AlphaFold2 to predict either active or inactive states at very high accuracy using state-annotated templated GPCR databases. The predicted models accurately capture the main structural changes upon activation of the GPCR at the at. level. For most of the benchmarked GPCRs (10 out of 15), models in the active and inactive states were closer to their corresponding activation state structures. Median RMSDs of the transmembrane regions were 1.12 S and 1.41 S for the active and inactive state models, resp. The models were more suitable for protein-ligand docking than the original AlphaFold2 models and template-based models. Finally, our prediction protocol predicted accurate GPCR structures and GPCR-peptide complex structures in GPCR Dock 2021, a blind GPCR-ligand complex modeling competition. We expect that high accuracy GPCR models in both activation states will promote understanding in GPCR activation mechanisms and drug discovery for GPCRs. At the time, the new protocol paves the way towards capturing the dynamics of proteins at high-accuracy via machine-learning methods.
- 42Sala, D.; Hildebrand, P. W.; Meiler, J. Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties. Front Mol. Biosci. 2023, 10, 10, DOI: 10.3389/fmolb.2023.1121962There is no corresponding record for this reference.
- 43Pándy-Szekeres, G.; Munk, C.; Tsonkov, T. M.; Mordalski, S.; Harpsøe, K.; Hauser, A. S.; Bojarski, A. J.; Gloriam, D. E. GPCRdb in 2018: adding GPCR structure models and ligands. Nucleic Acids Res. 2018, 46 (D1), D440– D446, DOI: 10.1093/nar/gkx110943https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXitlGisLbE&md5=0cf77dd5ea934548a2d3a0ca69991f60GPCRdb in 2018: adding GPCR structure models and ligandsPandy-Szekeres, Gaspar; Munk, Christian; Tsonkov, Tsonko M.; Mordalski, Stefan; Harpsoee, Kasper; Hauser, Alexander S.; Bojarski, Andrzej J.; Gloriam, David E.Nucleic Acids Research (2018), 46 (D1), D440-D446CODEN: NARHAD; ISSN:1362-4962. (Oxford University Press)G protein-coupled receptors are the most abundant mediators of both human signaling processes and therapeutic effects. Herein, we report GPCRomewide homol. models of unprecedented quality, and roughly 150 000 GPCR ligands with data on biol. activities and com. availability. Based on the strategy of 'Less model - more Xtal', each model exploits both a main template and alternative local templates. This achieved higher similarity to new structures than any of the existing resources, and refined crystal structures with missing or distorted regions. Models are provided for inactive, intermediate and active states-except for classes C and F that so far only have inactive templates. The ligand database has sep. browsers for: (i) target selection by receptor, family or class, (ii) ligand filtering based on cross-expt. activities (min, max and mean) or chem. properties, (iii) ligand source data and (iv) com. availability. SMILES structures and activity spreadsheets can be downloaded for further processing. Furthermore, three recent landmark publications on GPCR drugs, G protein selectivity and genetic variants have been accompanied with resources that now let readers view and analyze the findings themselves in GPCRdb. Altogether, this update will enable scientific investigation for the wider GPCR community.
- 44Zoltewicz, J. A.; Deady, L. W. Quaternization of Heteroaromatic Compounds: Quantitative Aspects. Adv. Heterocycl. Chem. 1978, 22 (C), 71– 121, DOI: 10.1016/S0065-2725(08)60103-8There is no corresponding record for this reference.
- 45Lane, B. S.; Sames, D. Direct C-H Bond Arylation: Selective Palladium-Catalyzed C2-Arylation of N-Substituted Indoles. Org. Lett. 2004, 6 (17), 2897– 2900, DOI: 10.1021/ol049007245https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXmtVOju7w%253D&md5=a180b90aa1339c90bb4e436aea737e2cDirect C-H Bond Arylation: Selective Palladium-Catalyzed C2-Arylation of N-Substituted IndolesLane, Benjamin S.; Sames, DaliborOrganic Letters (2004), 6 (17), 2897-2900CODEN: ORLEF7; ISSN:1523-7060. (American Chemical Society)The authors present a new, practical method by which N-substituted indoles may be selectively arylated in the C2-position with good yields, low catalyst loadings, and a high degree of functional group tolerance. E.g., Pd(OAc)2/PPh3 in the presence of base CsOAc catalyzed the arylation of 1-methylindole by PhI to give 1-methyl-2-phenylindole. The investigation found that two competitive processes, namely, the desired cross-coupling and biphenyl formation, were operative in this reaction. A simple kinetic model was formulated that proved to be instructive and provided useful guidelines for reaction optimization; the approach described within may prove to be useful in other catalytic cross-coupling processes.
- 46Stoddart, L. A.; Kilpatrick, L. E.; Hill, S. J. NanoBRET Approaches to Study Ligand Binding to GPCRs and RTKs. Trends Pharmacol. Sci. 2018, 39, 136– 147, DOI: 10.1016/j.tips.2017.10.00646https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhslGisLfJ&md5=8452d68dea813ba950a195e7c11faca5NanoBRET Approaches to Study Ligand Binding to GPCRs and RTKsStoddart, Leigh A.; Kilpatrick, Laura E.; Hill, Stephen J.Trends in Pharmacological Sciences (2018), 39 (2), 136-147CODEN: TPHSDY; ISSN:0165-6147. (Elsevier Ltd.)Recent advances in the development of fluorescent ligands for G-protein-coupled receptors (GPCRs) and receptor tyrosine kinase receptors (RTKs) have facilitated the study of these receptors in living cells. A limitation of these ligands is potential uptake into cells and increased nonspecific binding. However, this can largely be overcome by using proximity approaches, such as bioluminescence resonance energy transfer (BRET), which localize the signal (within 10 nm) to the specific receptor target. The recent engineering of NanoLuc has resulted in a luciferase variant that is smaller and significantly brighter (up to tenfold) than existing variants. Here, we review the use of BRET from N-terminal NanoLuc-tagged GPCRs or a RTK to a receptor-bound fluorescent ligand to provide quant. pharmacol. of ligand-receptor interactions in living cells in real time.
- 47Huang, H.; Si, P.; Wang, L.; Xu, Y.; Xu, X.; Zhu, J.; Jiang, H.; Li, W.; Chen, L.; Li, J. Design, Synthesis, and Biological Evaluation of Novel Nonsteroidal Farnesoid X Receptor (FXR) Antagonists: Molecular Basis of FXR Antagonism. ChemMedChem 2015, 10 (7), 1184– 1199, DOI: 10.1002/cmdc.201500136There is no corresponding record for this reference.
- 48Salvatore, C. A.; Jacobson, M. A.; Taylor, H. E.; Linden, J.; Johnson, R. G. Molecular cloning and characterization of the human A3 adenosine receptor. Proc. Natl. Acad. Sci. U.S.A. 1993, 90 (21), 10365– 10369, DOI: 10.1073/pnas.90.21.1036548https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2cXps1Gqtg%253D%253D&md5=7f663d6084176c057a12a655b2b6f157Molecular cloning and characterization of the human A3 adenosine receptorSalvatore, Christopher A.; Jacobson, Marlene A.; Taylor, Heidi E.; Linden, Joel; Johnson, Robert G.Proceedings of the National Academy of Sciences of the United States of America (1993), 90 (21), 10365-9CODEN: PNASA6; ISSN:0027-8424.The human A3 adenosine receptor was cloned from a striatal cDNA library using a probe derived from the homologous rat sequence. The cDNA encodes a protein of 318 amino acids and exhibits 72% and 85% overall identity with the rat and sheep A3 adenosine receptor sequences, resp. Specific and saturable binding of the adenosine receptor agonist N6-(4-amino-3-[125I]iodobenzyl)adenosine [125I]ABA was measured on the human A3 receptor stably expressed in Chinese hamster ovary cells with a Kd = 10 nM. The potency order for adenosine receptor agonists was N-ethylcarboxamidoadenosine (NECA) ≥ (R)-N6-phenyl-2-propyladenosine [(R)-PIA] > N6-cyclopentyladenosine (CPA) > (S)-N6-phenyl-2-propyladenosine [(S)-PIA]. The human receptor was blocked by xanthine antagonists, most potently by 3-(3-iodo-4-aminobenzyl)-8-(4-oxyacetate)phenyl-1-propylxanthine (I-ABOPX) with a potency order of I-ABOPX > 1,3-dipropyl-8-(4-acrylate)phenylxanthine ≥ xanthine amino congener >> 1,3-dipropyl-8-cyclopentylxanthine. Adenosine, NECA, (R)- and (S)-PIA, and CPA inhibited forskolin-stimulated cAMP accumulation by 30-40% in stably transfected cells; I-ABA is a partial agonist. When measured in the presence of antagonists, the dose-response curves of NECA-induced inhibition of forskolin-stimulated cAMP accumulation were right-shifted. Antagonist potencies detd. by Schild analyses correlated well with those established by competition for radioligand binding. The A3 adenosine receptor transcript is widespread and, in contrast to the A1, A2a, and A2b transcripts, the most abundant expression is found in the lung and liver. The tissue distribution of A3 mRNA is more similar to the widespread profile found in sheep than to the restricted profile found in the rat. This raises the possibility that numerous physiol. effects of adenosine may be mediated by A3 adenosine receptors.
- 49Stampelou, M.; Ladds, G.; Kolocouris, A. Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A3 Receptor. J. Phys. Chem. B 2024, 128 (4), 914– 936, DOI: 10.1021/acs.jpcb.3c05986There is no corresponding record for this reference.
- 50Bailey, S.; Harris, M.; Barkan, K.; Winfield, I.; Harper, M. T.; Simms, J.; Ladds, G.; Wheatley, M.; Poyner, D. Interactions between RAMP2 and CRF receptors: The effect of receptor subtypes, splice variants and cell context. Biochim. Biophys. Acta, Biomembr. 2019, 1861 (5), 997– 1003, DOI: 10.1016/j.bbamem.2019.02.008There is no corresponding record for this reference.
- 51Mackie, D. I.; Nielsen, N. R.; Harris, M.; Singh, S.; Davis, R. B.; Dy, D.; Ladds, G.; Caron, K. M. RAMP3 determines rapid recycling of atypical chemokine receptor-3 for guided angiogenesis. Proc. Natl. Acad. Sci. U.S.A. 2019, 116 (48), 24093– 24099, DOI: 10.1073/pnas.190556111651https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXit1OktrfO&md5=6442eeb7c9b5b3af292e9578babdb0caRAMP3 determines rapid recycling of atypical chemokine receptor-3 for guided angiogenesisMackie, Duncan I.; Nielsen, Natalie R.; Harris, Matthew; Singh, Smriti; Davis, Reema B.; Dy, Danica; Ladds, Graham; Caron, Kathleen M.Proceedings of the National Academy of Sciences of the United States of America (2019), 116 (48), 24093-24099CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)Receptor-activity-modifying proteins (RAMPs) are single transmembrane-spanning proteins which serve as mol. chaperones and allosteric modulators of G-protein-coupled receptors (GPCRs) and their signaling pathways. Although RAMPs have been previously studied in the context of their effects on Family B GPCRs, the coevolution of RAMPs with many GPCR families suggests an expanded repertoire of potential interactions. Using bioluminescence resonance energy transfer-based and cell-surface expression approaches, we comprehensively screen for RAMP interactions within the chemokine receptor family and identify robust interactions between RAMPs and nearly all chemokine receptors. Most notably, we identify robust RAMP interaction with atypical chemokine receptors (ACKRs), which function to establish chemotactic gradients for directed cell migration. Specifically, RAMP3 assocn. with atypical chemokine receptor 3 (ACKR3) diminishes adrenomedullin (AM) ligand availability without changing G-protein coupling. Instead, RAMP3 is required for the rapid recycling of ACKR3 to the plasma membrane through Rab4-pos. vesicles following either AM or SDF-1/CXCL12 binding, thereby enabling formation of dynamic spatiotemporal chemotactic gradients. Consequently, genetic deletion of either ACKR3 or RAMP3 in mice abolishes directed cell migration of retinal angiogenesis. Thus, RAMP assocn. with chemokine receptor family members represents a mol. interaction to control receptor signaling and trafficking properties.
- 52Stamatis, D.; Lagarias, P.; Barkan, K.; Vrontaki, E.; Ladds, G.; Kolocouris, A. Structural Characterization of Agonist Binding to an A 3 Adenosine Receptor through Biomolecular Simulations and Mutagenesis Experiments. J. Med. Chem. 2019, 62 (19), 8831– 8846, DOI: 10.1021/acs.jmedchem.9b01164There is no corresponding record for this reference.
- 53Gero, A.; Markham, J. J. Studies on Pyridines: I. The Basicity of Pyridine Bases. J. Org. Chem. 1951, 16 (12), 1835– 1838, DOI: 10.1021/jo50006a00153https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaG38XlsVKisg%253D%253D&md5=dfc4770105c9426c9fdd560e1753df46Pyridines. I. The basicity of pyridine basesGero, Alexander; Markham, James J.Journal of Organic Chemistry (1951), 16 (), 1835-8CODEN: JOCEAH; ISSN:0022-3263.The basicity of methylpyridines has been detd. Detns. of the ionization consts. of C5H5N and some of its homologs, with special precautions to operate with pure bases and to eliminate the CO2 error, give the following pKA and KB values, for C5H5N, 5.23, 1.7 × 10-9; 2-picoline, 5.96, 9.1 × 10-9; 4-picoline, 6.05, 1.1 × 10-8; 2,6-lutidine, 6.62, 4.2 × 10-8; 2,4-lutidine, 6.79, 6.1 × 10-8; 2,4,6-collidine, 7.45, 2.8 × 10-7. Plotting the pKA values against the no. of Me groups gives a straight line. Deviations of individual points from this line indicate that each Me group in an α-position increases the pKA of C5H5N by 0.73, each Me group in a γ-position by 0.82, but 2 Me groups in α-positions increase it by only 2 × 0.73-0.06. These results are attributed to steric hindrance.
- 54Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169–409X(96)00423–1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25. 1. Adv. Drug Delivery Rev. 2001, 46 (1–3), 3– 26, DOI: 10.1016/S0169-409X(00)00129-054https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3MXitVOhs7o%253D&md5=c60bb89da68f051c0ee7ac4c0468a0e4Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settingsLipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J.Advanced Drug Delivery Reviews (2001), 46 (1-3), 3-26CODEN: ADDREP; ISSN:0169-409X. (Elsevier Science Ireland Ltd.)A review with 50 refs. Exptl. and computational approaches to est. soly. and permeability in discovery and development settings are described. In the discovery setting 'the rule of 5' predicts that poor absorption or permeation is more likely when there are more than 5 H-bond donors, 10 H-bond acceptors, the mol. wt. (MWT) is greater than 500 and the calcd. Log P (CLogP) is greater than 5 (or MlogP >4.15). Computational methodol. for the rule-based Moriguchi Log P (MLogP) calcn. is described. Turbidimetric soly. measurement is described and applied to known drugs. High throughput screening (HTS) leads tend to have higher MWT and Log P and lower turbidimetric soly. than leads in the pre-HTS era. In the development setting, soly. calcns. focus on exact value prediction and are difficult because of polymorphism. Recent work on linear free energy relationships and Log P approaches are critically reviewed. Useful predictions are possible in closely related analog series when coupled with exptl. thermodn. soly. measurements.
- 55Banker, M. J.; Clark, T. H.; Williams, J. A. Development and validation of a 96-well equilibrium dialysis apparatus for measuring plasma protein binding. J. Pharm. Sci. 2003, 92 (5), 967– 974, DOI: 10.1002/jps.1033255https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3sXjs1ehsrs%253D&md5=0948f0a11373c57be06be9e0d604ad40Development and validation of a 96-well equilibrium dialysis apparatus for measuring plasma protein bindingBanker, Michael J.; Clark, Tracey H.; Williams, John A.Journal of Pharmaceutical Sciences (2003), 92 (5), 967-974CODEN: JPMSAE; ISSN:0022-3549. (Wiley-Liss, Inc.)A 96-well equil. dialysis block was designed and constructed that is compatible with most std. 96-well format lab. supplies and instruments. The unique design of the dialysis app. allows one to dispense and aspirate from either or both the sample and dialyzate sides from the top of the app., which is not possible with systems currently on the market. This feature permits the investigator to analyze a large no. of samples, time points, or replicates in the same expt. The novel alignment of the dialysis membrane vertically in the well maximizes the surface-to-vol. ratio, eliminates problems assocd. with trapped air pockets, and allows one to add or remove samples independently or all at once. Furthermore, the design of the app. allows both the sample and dialyzate sides of the dialysis well to be accessible by robotic systems, so assays can be readily automated. Teflon construction is used to minimize nonspecific binding of test samples to the app. The device is reusable, easily assembled, and can be shaken in controlled temp. environments to decrease the time required to reach equil. as well as facilitate dissoln. of test compds. Plasma protein binding values obtained for 10 diverse compds. using std. dialysis equipment and the 96-well dialysis block validates this method.
- 56Hidalgo, I. J.; Raub, T. J.; Borchardt, R. T. Characterization of the human colon carcinoma cell line (Caco-2) as a model system for intestinal epithelial permeability. Gastroenterology 1989, 96 (3), 736– 749, DOI: 10.1016/0016-5085(89)90897-456https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADyaL1M7itFeqsg%253D%253D&md5=59ffccc1dd4dccb01400c05df95ac1ffCharacterization of the human colon carcinoma cell line (Caco-2) as a model system for intestinal epithelial permeabilityHidalgo I J; Raub T J; Borchardt R TGastroenterology (1989), 96 (3), 736-49 ISSN:0016-5085.Caco-2 cells develop morphologic characteristics of normal enterocytes when grown on plastic dishes or nitrocellulose filters. The purpose of this study was to determine whether Caco-2 cells undergo similar differentiation when grown on Transwell polycarbonate membranes, and to study the suitability of Caco-2 monolayers as an intestinal epithelial transport model system. Transepithelial electrical resistance values after confluence were 173.5 omega.cm2 and remained unchanged through day 17. Permeabilities to the water-soluble fluid-phase markers that do not permeate the membrane, Lucifer yellow CH, [14C]inulin, [14C]polyethylene glycol, and [3H] dextran were less than 0.25% of the administered amount per hour after day 10. Qualitative evaluation of uptake and permeability to horseradish peroxidase confirmed the similarity in uptake and barrier properties between this cell system and the small intestinal epithelial layer. We conclude that Caco-2 cells grown on collagen-coated polycarbonate membranes should represent a valuable transport model system for the small intestinal epithelium.
- 57Obach, R. S.; Baxter, J. G.; Liston, T. E. The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. J. Pharmacol. Exp. Ther. 1997, 283 (1), 46– 5857https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2sXmslCltr0%253D&md5=68d1910d925e26ec4a8ef23043ffb1edThe prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism dataObach, R. Scott; Baxter, James G.; Liston, Theodore E.; Silber, B. Michael; Jones, Barry C.; Macintyre, Flona; Rance, David J.; Wastall, PhilipJournal of Pharmacology and Experimental Therapeutics (1997), 283 (1), 46-58CODEN: JPETAB; ISSN:0022-3565. (Williams & Wilkins)A review with 35 refs. We describe a comprehensive retrospective anal. in which the abilities of several methods by which human pharmacokinetic parameters are predicted from preclin. pharmacokinetic data and/or in vitro metab. data were assessed. The prediction methods examd. included both methods from the scientific literature as well as some described in this report for the first time. Four methods were examd. for their ability to predict human vol. of distribution. Three were highly predictive, yielding, on av., predictions that were within 60% to 90% of actual values. Twelve methods were assessed for their utility in predicting clearance. The most successful allometric scaling method yielded clearance predictions that were, on av., within 80% of actual values. The best methods in which in vitro metab. data from human liver microsomes were scaled to in vivo clearance values yielded predicted clearance values that were, on av., within 70% to 80% of actual values. Human t1/2 was predicted by combining predictions of human vol. of distribution and clearance. The best t1/2 prediction methods successfully assigned compds. to appropriate dosing regimen categories (e.g., once daily, twice daily and so forth) 70% to 80% of the time. In addn., correlations between human t1/2 and t1/2 values from preclin. species were also generally successful (72-87%) when used to predict human dosing regimens. In summary, this retrospective anal. has identified several approaches by which human pharmacokinetic data can be predicted from preclin. data. Such approaches should find utility in the drug discovery and development processes in the identification and selection of compds. that will possess appropriate pharmacokinetic characteristics in humans for progression to clin. trials.
- 58Yung-Chi, C.; Prusoff, W. H. Relationship between the inhibition constant and the concentration of inhbitor which causes 50% inhibition of an enzymatic reaction. Biochem. Pharmacol. 1973, 22 (23), 3099– 3108, DOI: 10.1016/0006-2952(73)90196-2There is no corresponding record for this reference.
- 59Motulsky, H. J.; Mahan, L. C. The kinetics of competitive radioligand binding predicted by the law of mass action. Mol. Pharmacol. 1984, 25 (1), 1– 959https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADyaL2c7mslOmtQ%253D%253D&md5=f0c09a16d7f8947fe0d72c256e2e4914The kinetics of competitive radioligand binding predicted by the law of mass actionMotulsky H J; Mahan L CMolecular pharmacology (1984), 25 (1), 1-9 ISSN:0026-895X.Although equilibrium competitive radioligand binding studies are often used to characterize hormone and neurotransmitter receptors, the kinetics of such experiments have not been extensively explored. The interactions of the radioligand and competitor with the receptors can be described by two differential equations which can be solved to yield a single equation describing the binding of the radioligand as a function of time. This equation has several applications: First, it can be used to simulate competitive binding reactions under defined conditions. Second, fitting experimental data to this equation allows one to determine the association and dissociation rate constants of the competing ligand, parameters that cannot be derived from equilibrium experiments. Furthermore, this method can be used to determine the KI of the competing drug from data acquired before equilibrium is reached. Third, mathematical analysis of the binding equation allowed us to answer two specific questions regarding the kinetics of competitive radioligand binding: how long such an incubation takes to equilibrate, and how the IC50 varies over time. The answers to these questions depended, to a large extent, on the relative values of the dissociation rate constants of the radioligand and competitor, which can be determined as noted above. When the competitor dissociates from the receptors more rapidly than the radioligand, the IC50 first decreases and then increases, but never has a value less than the KI. At low radioligand concentrations, equilibrium is reached in the same amount of time required of the radioligand to dissociate completely from the receptors as determined in an "off-rate experiment." At higher concentrations of radioligand this time is halved. When the competitor dissociates from the receptor more slowly than does the radioligand, then the time required to equilibrate depends only on the dissociation rate constant of the competitor, and the IC50 decreases over time.
- 60Curtis, M. J.; Alexander, S.; Cirino, G.; Docherty, J. R.; George, C. H.; Giembycz, M. A.; Hoyer, D.; Insel, P. A.; Izzo, A. A.; Ji, Y. Experimental design and analysis and their reporting II: updated and simplified guidance for authors and peer reviewers. Br. J. Pharmacol. 2018, 175 (7), 987– 993, DOI: 10.1111/bph.1415360https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXktV2ntbs%253D&md5=4dee1eb4b76fe4aa368798bc9ec9087aExperimental design and analysis and their reporting II: updated and simplified guidance for authors and peer reviewersCurtis, Michael J.; Alexander, Steve; Cirino, Giuseppe; Docherty, James R.; George, Christopher H.; Giembycz, Mark A.; Hoyer, Daniel; Insel, Paul A.; Izzo, Angelo A.; Ji, Yong; MacEwan, David J.; Sobey, Christopher G.; Stanford, S. Clare; Teixeira, Mauro M.; Wonnacott, Sue; Ahluwalia, AmritaBritish Journal of Pharmacology (2018), 175 (7), 987-993CODEN: BJPCBM; ISSN:1476-5381. (Wiley-Blackwell)This article updates the guidance published in 2015 for authors submitting papers to British Journal of Pharmacol. (Curtis et al., 2015) and is intended to provide the rubric for peer review. Thus, it is directed towards authors, reviewers and editors. Explanations for many of the requirements were outlined previously and are not restated here. The new guidelines are intended to replace those published previously. The guidelines have been simplified for ease of understanding by authors, to make it more straightforward for peer reviewers to check compliance and to facilitate the curation of the journal's efforts to improve stds.
- 61Ballesteros, J. A.; Weinstein, H. Analysis and refinement of criteria for predicting the structure and relative orientations of transmembranal helical domains. Biophys. J. 1992, 62 (1), 107– 109, DOI: 10.1016/S0006-3495(92)81794-061https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK38Xkt1arsbw%253D&md5=fc48e6b739613fb0b8afac61934d1e89Analysis and refinement of criteria for predicting the structure and relative orientations of transmembranal helical domainsBallesteros, Juan A.; Weinstein, HarelBiophysical Journal (1992), 62 (1), 107-9CODEN: BIOJAU; ISSN:0006-3495.Methods used currently in the construction of helical transmembrane domains could be misleading if used indiscriminately. These methods include the hydrophobicity profile, the hydrophobic moment, helix amphiphilicity, and charge neutralization. A refinement is proposed here, based on empirical observations, mol. modeling, and physicochem. considerations designed to overcome some of the shortcomings inherent in the use of the above mentioned methods. Here the anal. of two of the motifs identified in the study that led to the proposed refinements is presented: the distribution of acidic and basic residues in the transmembranal domains, and the kink induced by a proline residue in an α-helix.
- 62Yaziji, V.; Rodríguez, D.; Gutiérrez-De-Terán, H.; Coelho, A.; Caamaño, O.; García-Mera, X.; Brea, J.; Loza, M. I.; Cadavid, M. I.; Sotelo, E. Pyrimidine derivatives as potent and selective A3 adenosine receptor antagonists. J. Med. Chem. 2011, 54 (2), 457– 471, DOI: 10.1021/jm100843z62https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXhs1Wgt7nP&md5=9a904325839c3908780cde73487ba91bPyrimidine derivatives as potent and selective A3 adenosine receptor antagonistsYaziji, Vicente; Rodriguez, David; Gutierrez-de-Teran, Hugo; Coelho, Alberto; Caamano, Olga; Garcia-Mera, Xerardo; Brea, Jose; Loza, Maria Isabel; Cadavid, Maria Isabel; Sotelo, EddyJournal of Medicinal Chemistry (2011), 54 (2), 457-471CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)Two regioisomeric series of diaryl 2- or 4-amidopyrimidines e. g. I, II have been synthesized and their adenosine receptor affinities were detd. in radioligand binding assays at the four human adenosine receptors (hARs). Some of the ligands prepd. herein exhibit remarkable affinities (Ki < 10 nm) and, most noticeably, the absence of activity at the A1, A2A, and A2B receptors. The structural determinants that support the affinity and selectivity profiles of the series were highlighted through an integrated computational approach, combining a 3D-QSAR model built on the second generation of GRid Independent Descriptors (GRIND2) with a novel homol. model of the hA3 receptor. The robustness of the computational model was subsequently evaluated by the design of new derivs. exploring the alkyl substituent of the exocyclic amide group. The synthesis and evaluation of the novel compds. validated the predictive power of the model, exhibiting excellent agreement between predicted and exptl. activities.
- 63Jaakola, V. P.; Griffith, M. T.; Hanson, M. A.; Cherezov, V.; Chien, E. Y. T.; Lane, J. R.; Ijzerman, A. P.; Stevens, R. C. The 2.6 Angstrom Crystal Structure of a Human A2A Adenosine Receptor Bound to an Antagonist. Science 2008, 322 (5905), 1211– 1217, DOI: 10.1126/science.116477263https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXhtlyqtbfN&md5=5bdb862b41f345c244f3c162e058206bThe 2.6 Angstrom Crystal Structure of a Human A2A Adenosine Receptor Bound to an AntagonistJaakola, Veli-Pekka; Griffith, Mark T.; Hanson, Michael A.; Cherezov, Vadim; Chien, Ellen Y. T.; Lane, J. Robert; IJzerman, Adriaan P.; Stevens, Raymond C.Science (Washington, DC, United States) (2008), 322 (5905), 1211-1217CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)The adenosine class of heterotrimeric guanine nucleotide-binding protein (G protein)-coupled receptors (GPCRs) mediates the important role of extracellular adenosine in many physiol. processes and is antagonized by caffeine. The authors have detd. the crystal structure of the human A2A adenosine receptor, in complex with a high-affinity subtype-selective antagonist, ZM241385, to 2.6 angstrom resoln. Four disulfide bridges in the extracellular domain, combined with a subtle repacking of the transmembrane helixes relative to the adrenergic and rhodopsin receptor structures, define a pocket distinct from that of other structurally detd. GPCRs. The arrangement allows for the binding of the antagonist in an extended conformation, perpendicular to the membrane plane. The binding site highlights an integral role for the extracellular loops, together with the helical core, in ligand recognition by this class of GPCRs and suggests a role for ZM241385 in restricting the movement of a tryptophan residue important in the activation mechanism of the class A receptors.
- 64Ballesteros, J. A.; Weinstein, H. [19] Integrated methods for the construction of three-dimensional models and computational probing of structure-function relations in G protein-coupled receptors. Receptor Molecular Biol. 1995, 25, 366– 428, DOI: 10.1016/S1043-9471(05)80049-7There is no corresponding record for this reference.
- 65Sastry, G. M.; Adzhigirey, M.; Day, T.; Annabhimoju, R.; Sherman, W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des. 2013, 27 (3), 221– 234, DOI: 10.1007/s10822-013-9644-865https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3srksFekug%253D%253D&md5=9eb1a820e121aca2742ed53f24481aceProtein and ligand preparation: parameters, protocols, and influence on virtual screening enrichmentsSastry G Madhavi; Adzhigirey Matvey; Day Tyler; Annabhimoju Ramakrishna; Sherman WoodyJournal of computer-aided molecular design (2013), 27 (3), 221-34 ISSN:.Structure-based virtual screening plays an important role in drug discovery and complements other screening approaches. In general, protein crystal structures are prepared prior to docking in order to add hydrogen atoms, optimize hydrogen bonds, remove atomic clashes, and perform other operations that are not part of the x-ray crystal structure refinement process. In addition, ligands must be prepared to create 3-dimensional geometries, assign proper bond orders, and generate accessible tautomer and ionization states prior to virtual screening. While the prerequisite for proper system preparation is generally accepted in the field, an extensive study of the preparation steps and their effect on virtual screening enrichments has not been performed. In this work, we systematically explore each of the steps involved in preparing a system for virtual screening. We first explore a large number of parameters using the Glide validation set of 36 crystal structures and 1,000 decoys. We then apply a subset of protocols to the DUD database. We show that database enrichment is improved with proper preparation and that neglecting certain steps of the preparation process produces a systematic degradation in enrichments, which can be large for some targets. We provide examples illustrating the structural changes introduced by the preparation that impact database enrichment. While the work presented here was performed with the Protein Preparation Wizard and Glide, the insights and guidance are expected to be generalizable to structure-based virtual screening with other docking methods.
- 66Lomize, M. A.; Pogozheva, I. D.; Joo, H.; Mosberg, H. I.; Lomize, A. L. OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Res. 2012, 40 (D1), D370– D376, DOI: 10.1093/nar/gkr70366https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhs12hurzJ&md5=b931126c56d227ab0eb6c5762c1dae6bOPM database and PPM web server: resources for positioning of proteins in membranesLomize, Mikhail A.; Pogozheva, Irina D.; Joo, Hyeon; Mosberg, Henry I.; Lomize, Andrei L.Nucleic Acids Research (2012), 40 (D1), D370-D376CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)The Orientations of Proteins in Membranes (OPM) database is a curated web resource that provides spatial positions of membrane-bound peptides and proteins of known three-dimensional structure in the lipid bilayer, together with their structural classification, topol. and intracellular localization. OPM currently contains more than 1200 transmembrane and peripheral proteins and peptides from approx. 350 organisms that represent approx. 3800 Protein Data Bank entries. Proteins are classified into classes, superfamilies and families and assigned to 21 distinct membrane types. Spatial positions of proteins with respect to the lipid bilayer are optimized by the PPM 2.0 method that accounts for the hydrophobic, hydrogen bonding and electrostatic interactions of the proteins with the anisotropic water-lipid environment described by the dielec. const. and hydrogen-bonding profiles. The OPM database is freely accessible at http://opm.phar.umich.edu. Data can be sorted, searched or retrieved using the hierarchical classification, source organism, localization in different types of membranes. The database offers downloadable coordinates of proteins and peptides with membrane boundaries. A gallery of protein images and several visualization tools are provided. The database is supplemented by the PPM server (http://opm.phar.umich.edu/server.php) which can be used for calcg. spatial positions in membranes of newly detd. proteins structures or theor. models.
- 67Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 1983, 79 (2), 926– 935, DOI: 10.1063/1.44586967https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL3sXksF2htL4%253D&md5=a1161334e381746be8c9b15a5e56f704Comparison of simple potential functions for simulating liquid waterJorgensen, William L.; Chandrasekhar, Jayaraman; Madura, Jeffry D.; Impey, Roger W.; Klein, Michael L.Journal of Chemical Physics (1983), 79 (2), 926-35CODEN: JCPSA6; ISSN:0021-9606.Classical Monte Carlo simulations were carried out for liq. H2O in the NPT ensemble at 25° and 1 atm using 6 of the simpler intermol. potential functions for the dimer. Comparisons were made with exptl. thermodn. and structural data including the neutron diffraction results of Thiessen and Narten (1982). The computed densities and potential energies agree with expt. except for the original Bernal-Fowler model, which yields an 18% overest. of the d. and poor structural results. The discrepancy may be due to the correction terms needed in processing the neutron data or to an effect uniformly neglected in the computations. Comparisons were made for the self-diffusion coeffs. obtained from mol. dynamics simulations.
- 68Dickson, C. J.; Walker, R. C.; Gould, I. R. Lipid21: Complex Lipid Membrane Simulations with AMBER. J. Chem. Theory Comput. 2022, 18 (3), 1726– 1736, DOI: 10.1021/acs.jctc.1c0121768https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38Xis1ymtLg%253D&md5=d6d7877157017f4853fc6dbe8d8987b5Lipid21: Complex Lipid Membrane Simulations with AMBERDickson, Callum J.; Walker, Ross C.; Gould, Ian R.Journal of Chemical Theory and Computation (2022), 18 (3), 1726-1736CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)We extend the modular AMBER lipid force field to include anionic lipids, polyunsatd. fatty acid (PUFA) lipids, and sphingomyelin, allowing the simulation of realistic cell membrane lipid compns., including raft-like domains. Head group torsion parameters are revised, resulting in improved agreement with NMR order parameters, and hydrocarbon chain parameters are updated, providing a better match with phase transition temp. Extensive validation runs (0.9μs per lipid type) show good agreement with exptl. measurements. Furthermore, the simulation of raft-like bilayers demonstrates the perturbing effect of increasing PUFA concns. on cholesterol mols. The force field derivation is consistent with the AMBER philosophy, meaning it can be easily mixed with protein, small mol., nucleic acid, and carbohydrate force fields.
- 69He, X.; Man, V. H.; Yang, W.; Lee, T. S.; Wang, J. A fast and high-quality charge model for the next generation general AMBER force field. J. Chem. Phys. 2020, 153(11). DOI: 10.1063/5.0019056 .There is no corresponding record for this reference.
- 70Joung, I. S.; Cheatham, T. E. Determination of Alkali and Halide Monovalent Ion Parameters for Use in Explicitly Solvated Biomolecular Simulations. J. Phys. Chem. B 2008, 112 (30), 9020– 9041, DOI: 10.1021/jp800161470https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXnvFGqtL4%253D&md5=aa489470ae1c7479bf0911710217bd28Determination of Alkali and Halide Monovalent Ion Parameters for Use in Explicitly Solvated Biomolecular SimulationsJoung, In Suk; Cheatham, Thomas E.Journal of Physical Chemistry B (2008), 112 (30), 9020-9041CODEN: JPCBFK; ISSN:1520-6106. (American Chemical Society)Alkali (Li+, Na+, K+, Rb+, and Cs+) and halide (F-, Cl-, Br-, and I-) ions play an important role in many biol. phenomena, roles that range from stabilization of biomol. structure, to influence on biomol. dynamics, to key physiol. influence on homeostasis and signaling. To properly model ionic interaction and stability in atomistic simulations of biomol. structure, dynamics, folding, catalysis, and function, an accurate model or representation of the monovalent ions is critically necessary. A good model needs to simultaneously reproduce many properties of ions, including their structure, dynamics, solvation, and moreover both the interactions of these ions with each other in the crystal and in soln. and the interactions of ions with other mols. At present, the best force fields for biomols. employ a simple additive, nonpolarizable, and pairwise potential for at. interaction. In this work, the authors describe their efforts to build better models of the monovalent ions within the pairwise Coulombic and 6-12 Lennard-Jones framework, where the models are tuned to balance crystal and soln. properties in Ewald simulations with specific choices of well-known water models. Although it has been clearly demonstrated that truly accurate treatments of ions will require inclusion of nonadditivity and polarizability (particularly with the anions) and ultimately even a quantum mech. treatment, the authors' goal was to simply push the limits of the additive treatments to see if a balanced model could be created. The applied methodol. is general and can be extended to other ions and to polarizable force-field models. The authors' starting point centered on observations from long simulations of biomols. in salt soln. with the AMBER force fields where salt crystals formed well below their soly. limit. The likely cause of the artifact in the AMBER parameters relates to the naive mixing of the Smith and Dang chloride parameters with AMBER-adapted Aqvist cation parameters. To provide a more appropriate balance, the authors reoptimized the parameters of the Lennard-Jones potential for the ions and specific choices of water models. To validate and optimize the parameters, the authors calcd. hydration free energies of the solvated ions and also lattice energies (LE) and lattice consts. (LC) of alkali halide salt crystals. This is the first effort that systematically scans across the Lennard-Jones space (well depth and radius) while balancing ion properties like LE and LC across all pair combinations of the alkali ions and halide ions. The optimization across the entire monovalent series avoids systematic deviations. The ion parameters developed, optimized, and characterized were targeted for use with some of the most commonly used rigid and nonpolarizable water models, specifically TIP3P, TIP4PEW, and SPC/E. In addn. to well reproducing the soln. and crystal properties, the new ion parameters well reproduce binding energies of the ions to water and the radii of the first hydration shells.
- 71Sengupta, A.; Li, Z.; Song, L. F.; Li, P.; Merz, K. M. Parameterization of Monovalent Ions for the OPC3, OPC, TIP3P-FB, and TIP4P-FB Water Models. J. Chem. Inf. Model. 2021, 61 (2), 869– 880, DOI: 10.1021/acs.jcim.0c0139071https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXivVejur4%253D&md5=91eafbb4a3a16b5034e3c6cf3fb3873bParameterization of Monovalent Ions for the OPC3, OPC, TIP3P-FB, and TIP4P-FB Water ModelsSengupta, Arkajyoti; Li, Zhen; Song, Lin Frank; Li, Pengfei; Merz Jr., Kenneth M.Journal of Chemical Information and Modeling (2021), 61 (2), 869-880CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)Monovalent ions play significant roles in various biol. and material systems. Recently, four new water models (OPC3, OPC, TIP3P-FB, and TIP4P-FB), with significantly improved descriptions of condensed phase water, have been developed. The pairwise interaction between the metal ion and water necessitates the development of ion parameters specifically for these water models. Herein, we parameterized the 12-6 and the 12-6-4 nonbonded models for 12 monovalent ions with the resp. four new water models. These monovalent ions contain eight cations including alkali metal ions (Li+, Na+, K+, Rb+, Cs+), transition-metal ions (Cu+ and Ag+), and Tl+ from the boron family, along with four halide anions (F-, Cl-, Br-, I-). Our parameters were designed to reproduce the target hydration free energies (the 12-6 hydration free energy (HFE) set), the ion-oxygen distances (the 12-6 ion-oxygen distance (IOD) set), or both of them (the 12-6-4 set). The 12-6-4 parameter set provides highly accurate structural features overcoming the limitations of the routinely used 12-6 nonbonded model for ions. Specifically, we note that the 12-6-4 parameter set is able to reproduce exptl. hydration free energies within 1 kcal/mol and exptl. ion-oxygen distances within 0.01 Å simultaneously. We further reproduced the exptl. detd. activity derivs. for salt solns., validating the ion parameters for simulations of ion pairs. The improved performance of the present water models over our previous parameter sets for the TIP3P, TIP4P, and SPC/E water models (P. Li et al., J. Chem. Theor. Comput., 2015, 11, 1645 1657) highlights the importance of the choice of water model in conjunction with the metal ion parameter set.