ACS Publications. Most Trusted. Most Cited. Most Read
Quantitative Characterization of the Binding and Unbinding of Millimolar Drug Fragments with Molecular Dynamics Simulations

OR SEARCH CITATIONS

My Activity
Publications

Figure 1Loading Img
Download Hi-Res ImageDownload to MS-PowerPointCite This:J. Chem. Theory Comput. 2017, 13, 7, 3372-3377
    Article

    Quantitative Characterization of the Binding and Unbinding of Millimolar Drug Fragments with Molecular Dynamics Simulations
    Click to copy article linkArticle link copied!

    View Author Information
    † D. E. Shaw Research, New York, New York 10036, United States
    ‡ Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York 10032, United States
    *E-mail: [email protected]; Phone: (212) 403-8664; Fax: (646) 873-2664.
    *E-mail: [email protected]; Phone: (212) 478-0163; Fax: (212) 845-1163.
    *E-mail: [email protected]; Phone: (212) 478-0260; Fax: (212) 845-1286.
    Other Access OptionsSupporting Information (2)

    Journal of Chemical Theory and Computation

    Cite this: J. Chem. Theory Comput. 2017, 13, 7, 3372–3377
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jctc.7b00172
    Published June 5, 2017
    Copyright © 2017 American Chemical Society
    Request reuse permissions

    Abstract

    Click to copy section linkSection link copied!
    Abstract Image

    A quantitative characterization of the binding properties of drug fragments to a target protein is an important component of a fragment-based drug discovery program. Fragments typically have a weak binding affinity, however, making it challenging to experimentally characterize key binding properties, including binding sites, poses, and affinities. Direct simulation of the binding equilibrium by molecular dynamics (MD) simulations can provide a computational route to characterize fragment binding, but this approach is so computationally intensive that it has thus far remained relatively unexplored. Here, we perform MD simulations of sufficient length to observe several different fragments spontaneously and repeatedly bind to and unbind from the protein FKBP, allowing the binding affinities, on- and off-rates, and relative occupancies of alternative binding sites and alternative poses within each binding site to be estimated, thereby illustrating the potential of long time scale MD as a quantitative tool for fragment-based drug discovery. The data from the long time scale fragment binding simulations reported here also provide a useful benchmark for testing alternative computational methods aimed at characterizing fragment binding properties. As an example, we calculated binding affinities for the same fragments using a standard free energy perturbation approach and found that the values agreed with those obtained from the fragment binding simulations within statistical error.

    Copyright © 2017 American Chemical Society

    Subjects

    what are subjects

    Read this article

    To access this article, please review the available access options below.

    Get instant access

    Purchase Access

    Read this article for 48 hours. Check out below using your ACS ID or as a guest.

    Recommended

    Access through Your Institution

    You may have access to this article through your institution.

    Your institution does not have access to this content. Add or change your institution or let them know you’d like them to include access.

    Supporting Information

    Click to copy section linkSection link copied!

    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jctc.7b00172.

    • Table providing a list of simulations (Table S1), four additional figures (Figures S1–S4), and more details about the simulations (PDF)

    • MD structure files corresponding to the equilibrated starting frames of the reversible binding simulations, including force field information (ZIP)

    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.

    Cited By

    Click to copy section linkSection link copied!

    This article is cited by 105 publications.

    1. Anupam Anand Ojha, Lane W. Votapka, Rommie E. Amaro. Advances and Challenges in Milestoning Simulations for Drug–Target Kinetics. Journal of Chemical Theory and Computation 2024, 20 (22) , 9759-9769. https://doi.org/10.1021/acs.jctc.4c01108
    2. Runtong Qian, Jing Xue, You Xu, Jing Huang. Alchemical Transformations and Beyond: Recent Advances and Real-World Applications of Free Energy Calculations in Drug Discovery. Journal of Chemical Information and Modeling 2024, 64 (19) , 7214-7237. https://doi.org/10.1021/acs.jcim.4c01024
    3. Maurice Karrenbrock, Alberto Borsatto, Valerio Rizzi, Dominykas Lukauskis, Simone Aureli, Francesco Luigi Gervasio. Absolute Binding Free Energies with OneOPES. The Journal of Physical Chemistry Letters 2024, 15 (39) , 9871-9880. https://doi.org/10.1021/acs.jpclett.4c02352
    4. Jinan Wang, Yinglong Miao. Ligand Gaussian Accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides. Journal of Chemical Theory and Computation 2024, 20 (14) , 5829-5841. https://doi.org/10.1021/acs.jctc.4c00502
    5. Suemin Lee, Dedi Wang, Markus A. Seeliger, Pratyush Tiwary. Calculating Protein–Ligand Residence Times through State Predictive Information Bottleneck Based Enhanced Sampling. Journal of Chemical Theory and Computation 2024, 20 (14) , 6341-6349. https://doi.org/10.1021/acs.jctc.4c00503
    6. Vitali Stanevich, Oluyemi Oyeniran, Sandeep Somani. Modeling Chromatography Binding through Molecular Dynamics Simulations with Resin Fragments. The Journal of Physical Chemistry B 2024, 128 (23) , 5557-5566. https://doi.org/10.1021/acs.jpcb.4c00578
    7. Junlin Dong, Shiyu Wang, Wenqiang Cui, Xiaolin Sun, Haojie Guo, Hailu Yan, Horst Vogel, Zhi Wang, Shuguang Yuan. Machine Learning Deciphered Molecular Mechanistics with Accurate Kinetic and Thermodynamic Prediction. Journal of Chemical Theory and Computation 2024, 20 (11) , 4499-4513. https://doi.org/10.1021/acs.jctc.3c01412
    8. Lieyang Chen, Yujie Wu, Chuanjie Wu, Ana Silveira, Woody Sherman, Huafeng Xu, Emilio Gallicchio. Performance and Analysis of the Alchemical Transfer Method for Binding-Free-Energy Predictions of Diverse Ligands. Journal of Chemical Information and Modeling 2024, 64 (1) , 250-264. https://doi.org/10.1021/acs.jcim.3c01705
    9. Dhiman Ray, Michele Parrinello. Kinetics from Metadynamics: Principles, Applications, and Outlook. Journal of Chemical Theory and Computation 2023, 19 (17) , 5649-5670. https://doi.org/10.1021/acs.jctc.3c00660
    10. Agastya P. Bhati, Art Hoti, Andrew Potterton, Mateusz K. Bieniek, Peter V. Coveney. Long Time Scale Ensemble Methods in Molecular Dynamics: Ligand–Protein Interactions and Allostery in SARS-CoV-2 Targets. Journal of Chemical Theory and Computation 2023, 19 (11) , 3359-3378. https://doi.org/10.1021/acs.jctc.3c00020
    11. Shinji Iida, Tomoshi Kameda. Dissociation Rate Calculation via Constant-Force Steered Molecular Dynamics Simulation. Journal of Chemical Information and Modeling 2023, 63 (11) , 3369-3376. https://doi.org/10.1021/acs.jcim.2c01529
    12. Wei Chen, Di Cui, Steven V. Jerome, Mayako Michino, Eelke B. Lenselink, David J. Huggins, Alexandre Beautrait, Jeremie Vendome, Robert Abel, Richard A. Friesner, Lingle Wang. Enhancing Hit Discovery in Virtual Screening through Absolute Protein–Ligand Binding Free-Energy Calculations. Journal of Chemical Information and Modeling 2023, 63 (10) , 3171-3185. https://doi.org/10.1021/acs.jcim.3c00013
    13. Jack B. Greisman, Lindsay Willmore, Christine Y. Yeh, Fabrizio Giordanetto, Sahar Shahamadtar, Hunter Nisonoff, Paul Maragakis, David E. Shaw. Discovery and Validation of the Binding Poses of Allosteric Fragment Hits to Protein Tyrosine Phosphatase 1b: From Molecular Dynamics Simulations to X-ray Crystallography. Journal of Chemical Information and Modeling 2023, 63 (9) , 2644-2650. https://doi.org/10.1021/acs.jcim.3c00236
    14. Arup Mondal, G.V.T. Swapna, Maria M. Lopez, Laura Klang, Jingzhou Hao, LiChung Ma, Monica J. Roth, Gaetano T. Montelione, Alberto Perez. Structure Determination of Challenging Protein–Peptide Complexes Combining NMR Chemical Shift Data and Molecular Dynamics Simulations. Journal of Chemical Information and Modeling 2023, 63 (7) , 2058-2072. https://doi.org/10.1021/acs.jcim.2c01595
    15. Jinan Wang, Yinglong Miao. Ligand Gaussian Accelerated Molecular Dynamics 2 (LiGaMD2): Improved Calculations of Ligand Binding Thermodynamics and Kinetics with Closed Protein Pocket. Journal of Chemical Theory and Computation 2023, 19 (3) , 733-745. https://doi.org/10.1021/acs.jctc.2c01194
    16. Sonia Ziada, Julien Diharce, Eric Raimbaud, Samia Aci-Sèche, Pierre Ducrot, Pascal Bonnet. Estimation of Drug-Target Residence Time by Targeted Molecular Dynamics Simulations. Journal of Chemical Information and Modeling 2022, 62 (22) , 5536-5549. https://doi.org/10.1021/acs.jcim.2c00852
    17. Matteo Pavan, Silvia Menin, Davide Bassani, Mattia Sturlese, Stefano Moro. Qualitative Estimation of Protein–Ligand Complex Stability through Thermal Titration Molecular Dynamics Simulations. Journal of Chemical Information and Modeling 2022, 62 (22) , 5715-5728. https://doi.org/10.1021/acs.jcim.2c00995
    18. Talant Ruzmetov, Ruben Montes, Jianan Sun, Si-Han Chen, Zhiye Tang, Chia-en A. Chang. Binding Kinetics Toolkit for Analyzing Transient Molecular Conformations and Computing Free Energy Landscapes. The Journal of Physical Chemistry A 2022, 126 (46) , 8761-8770. https://doi.org/10.1021/acs.jpca.2c05499
    19. Haohao Fu, Yan Zhou, Xiang Jing, Xueguang Shao, Wensheng Cai. Meta-Analysis Reveals That Absolute Binding Free-Energy Calculations Approach Chemical Accuracy. Journal of Medicinal Chemistry 2022, 65 (19) , 12970-12978. https://doi.org/10.1021/acs.jmedchem.2c00796
    20. Payal Chatterjee, Mert Y. Sengul, Anmol Kumar, Alexander D. MacKerell, Jr.. Harnessing Deep Learning for Optimization of Lennard-Jones Parameters for the Polarizable Classical Drude Oscillator Force Field. Journal of Chemical Theory and Computation 2022, 18 (4) , 2388-2407. https://doi.org/10.1021/acs.jctc.2c00115
    21. Wanling Song, Robin A. Corey, T. Bertie Ansell, C. Keith Cassidy, Michael R. Horrell, Anna L. Duncan, Phillip J. Stansfeld, Mark S. P. Sansom. PyLipID: A Python Package for Analysis of Protein–Lipid Interactions from Molecular Dynamics Simulations. Journal of Chemical Theory and Computation 2022, 18 (2) , 1188-1201. https://doi.org/10.1021/acs.jctc.1c00708
    22. Solmaz Azimi, Sheenam Khuttan, Joe Z. Wu, Rajat K. Pal, Emilio Gallicchio. Relative Binding Free Energy Calculations for Ligands with Diverse Scaffolds with the Alchemical Transfer Method. Journal of Chemical Information and Modeling 2022, 62 (2) , 309-323. https://doi.org/10.1021/acs.jcim.1c01129
    23. Dhiman Ray, Sharon Emily Stone, Ioan Andricioaei. Markovian Weighted Ensemble Milestoning (M-WEM): Long-Time Kinetics from Short Trajectories. Journal of Chemical Theory and Computation 2022, 18 (1) , 79-95. https://doi.org/10.1021/acs.jctc.1c00803
    24. Qianqian Zhao, Riccardo Capelli, Paolo Carloni, Bernhard Lüscher, Jinyu Li, Giulia Rossetti. Enhanced Sampling Approach to the Induced-Fit Docking Problem in Protein–Ligand Binding: The Case of Mono-ADP-Ribosylation Hydrolase Inhibitors. Journal of Chemical Theory and Computation 2021, 17 (12) , 7899-7911. https://doi.org/10.1021/acs.jctc.1c00649
    25. Paul Labute Maximilian Ebert . Optimizing Simulations Protocols for Relative Free Energy Calculations. , 227-245. https://doi.org/10.1021/bk-2021-1397.ch009
    26. Henrique F. Carvalho, Valerio Ferrario, Jürgen Pleiss. Molecular Mechanism of Methanol Inhibition in CALB-Catalyzed Alcoholysis: Analyzing Molecular Dynamics Simulations by a Markov State Model. Journal of Chemical Theory and Computation 2021, 17 (10) , 6570-6582. https://doi.org/10.1021/acs.jctc.1c00559
    27. Zhixiong Lin, Junjie Zou, Shuai Liu, Chunwang Peng, Zhipeng Li, Xiao Wan, Dong Fang, Jian Yin, Gianpaolo Gobbo, Yongpan Chen, Jian Ma, Shuhao Wen, Peiyu Zhang, Mingjun Yang. A Cloud Computing Platform for Scalable Relative and Absolute Binding Free Energy Predictions: New Opportunities and Challenges for Drug Discovery. Journal of Chemical Information and Modeling 2021, 61 (6) , 2720-2732. https://doi.org/10.1021/acs.jcim.0c01329
    28. Joe Z. Wu, Solmaz Azimi, Sheenam Khuttan, Nanjie Deng, Emilio Gallicchio. Alchemical Transfer Approach to Absolute Binding Free Energy Estimation. Journal of Chemical Theory and Computation 2021, 17 (6) , 3309-3319. https://doi.org/10.1021/acs.jctc.1c00266
    29. Miroslav Suruzhon, Michael S. Bodnarchuk, Antonella Ciancetta, Russell Viner, Ian D. Wall, Jonathan W. Essex. Sensitivity of Binding Free Energy Calculations to Initial Protein Crystal Structure. Journal of Chemical Theory and Computation 2021, 17 (3) , 1806-1821. https://doi.org/10.1021/acs.jctc.0c00972
    30. Navjeet Ahalawat, Jagannath Mondal. An Appraisal of Computer Simulation Approaches in Elucidating Biomolecular Recognition Pathways. The Journal of Physical Chemistry Letters 2021, 12 (1) , 633-641. https://doi.org/10.1021/acs.jpclett.0c02785
    31. Joan F. Gilabert, Oriol Gracia Carmona, Anders Hogner, Victor Guallar. Combining Monte Carlo and Molecular Dynamics Simulations for Enhanced Binding Free Energy Estimation through Markov State Models. Journal of Chemical Information and Modeling 2020, 60 (11) , 5529-5539. https://doi.org/10.1021/acs.jcim.0c00406
    32. Tai-Sung Lee, Bryce K. Allen, Timothy J. Giese, Zhenyu Guo, Pengfei Li, Charles Lin, T. Dwight McGee, Jr., David A. Pearlman, Brian K. Radak, Yujun Tao, Hsu-Chun Tsai, Huafeng Xu, Woody Sherman, Darrin M. York. Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery. Journal of Chemical Information and Modeling 2020, 60 (11) , 5595-5623. https://doi.org/10.1021/acs.jcim.0c00613
    33. Yuwei Zhang, Zexing Cao, John Zenghui Zhang, Fei Xia. Double-Well Ultra-Coarse-Grained Model to Describe Protein Conformational Transitions. Journal of Chemical Theory and Computation 2020, 16 (10) , 6678-6689. https://doi.org/10.1021/acs.jctc.0c00551
    34. Gregory A. Ross, Ellery Russell, Yuqing Deng, Chao Lu, Edward D. Harder, Robert Abel, Lingle Wang. Enhancing Water Sampling in Free Energy Calculations with Grand Canonical Monte Carlo. Journal of Chemical Theory and Computation 2020, 16 (10) , 6061-6076. https://doi.org/10.1021/acs.jctc.0c00660
    35. Bhupendra R. Dandekar, Jagannath Mondal. Capturing Protein–Ligand Recognition Pathways in Coarse-Grained Simulation. The Journal of Physical Chemistry Letters 2020, 11 (13) , 5302-5311. https://doi.org/10.1021/acs.jpclett.0c01683
    36. Masatake Sugita, Masataka Hamano, Kota Kasahara, Takeshi Kikuchi, Fumio Hirata. New Protocol for Predicting the Ligand-Binding Site and Mode Based on the 3D-RISM/KH Theory. Journal of Chemical Theory and Computation 2020, 16 (4) , 2864-2876. https://doi.org/10.1021/acs.jctc.9b01069
    37. Zhiye Tang, Si-Han Chen, Chia-en A. Chang. Transient States and Barriers from Molecular Simulations and the Milestoning Theory: Kinetics in Ligand–Protein Recognition and Compound Design. Journal of Chemical Theory and Computation 2020, 16 (3) , 1882-1895. https://doi.org/10.1021/acs.jctc.9b01153
    38. Abdennour Braka, Norbert Garnier, Pascal Bonnet, Samia Aci-Sèche. Residence Time Prediction of Type 1 and 2 Kinase Inhibitors from Unbinding Simulations. Journal of Chemical Information and Modeling 2020, 60 (1) , 342-348. https://doi.org/10.1021/acs.jcim.9b00497
    39. Ryuhei Harada, Vladimir Sladek, Yasuteru Shigeta. Nontargeted Parallel Cascade Selection Molecular Dynamics Based on a Nonredundant Selection Rule for Initial Structures Enhances Conformational Sampling of Proteins. Journal of Chemical Information and Modeling 2019, 59 (12) , 5198-5206. https://doi.org/10.1021/acs.jcim.9b00753
    40. John F. Darby, Adam P. Hopkins, Seishi Shimizu, Shirley M. Roberts, James A. Brannigan, Johan P. Turkenburg, Gavin H. Thomas, Roderick E. Hubbard, Marcus Fischer. Water Networks Can Determine the Affinity of Ligand Binding to Proteins. Journal of the American Chemical Society 2019, 141 (40) , 15818-15826. https://doi.org/10.1021/jacs.9b06275
    41. Stephanie Maria Linker, Aniket Magarkar, Jürgen Köfinger, Gerhard Hummer, Daniel Seeliger. Fragment Binding Pose Predictions Using Unbiased Simulations and Markov-State Models. Journal of Chemical Theory and Computation 2019, 15 (9) , 4974-4981. https://doi.org/10.1021/acs.jctc.9b00069
    42. Debabrata Pramanik, Zachary Smith, Adam Kells, Pratyush Tiwary. Can One Trust Kinetic and Thermodynamic Observables from Biased Metadynamics Simulations?: Detailed Quantitative Benchmarks on Millimolar Drug Fragment Dissociation. The Journal of Physical Chemistry B 2019, 123 (17) , 3672-3678. https://doi.org/10.1021/acs.jpcb.9b01813
    43. João Marcelo Lamim Ribeiro, Sun-Ting Tsai, Debabrata Pramanik, Yihang Wang, Pratyush Tiwary. Kinetics of Ligand–Protein Dissociation from All-Atom Simulations: Are We There Yet?. Biochemistry 2019, 58 (3) , 156-165. https://doi.org/10.1021/acs.biochem.8b00977
    44. Daria B. Kokh, Marta Amaral, Joerg Bomke, Ulrich Grädler, Djordje Musil, Hans-Peter Buchstaller, Matthias K. Dreyer, Matthias Frech, Maryse Lowinski, Francois Vallee, Marc Bianciotto, Alexey Rak, Rebecca C. Wade. Estimation of Drug-Target Residence Times by τ-Random Acceleration Molecular Dynamics Simulations. Journal of Chemical Theory and Computation 2018, 14 (7) , 3859-3869. https://doi.org/10.1021/acs.jctc.8b00230
    45. Gerard Martinez-Rosell, Matt J. Harvey, Gianni De Fabritiis. Molecular-Simulation-Driven Fragment Screening for the Discovery of New CXCL12 Inhibitors. Journal of Chemical Information and Modeling 2018, 58 (3) , 683-691. https://doi.org/10.1021/acs.jcim.7b00625
    46. Samuel D Lotz and Alex Dickson . Unbiased Molecular Dynamics of 11 min Timescale Drug Unbinding Reveals Transition State Stabilizing Interactions. Journal of the American Chemical Society 2018, 140 (2) , 618-628. https://doi.org/10.1021/jacs.7b08572
    47. Zhiye Tang and Chia-en A. Chang . Binding Thermodynamics and Kinetics Calculations Using Chemical Host and Guest: A Comprehensive Picture of Molecular Recognition. Journal of Chemical Theory and Computation 2018, 14 (1) , 303-318. https://doi.org/10.1021/acs.jctc.7b00899
    48. Clare R. Trevitt, D. R. Yashwanth Kumar, Nicholas J. Fowler, Mike P. Williamson. Interactions between the protein barnase and co-solutes studied by NMR. Communications Chemistry 2024, 7 (1) https://doi.org/10.1038/s42004-024-01127-0
    49. Tim Kirkman, Catharina dos Santos Silva, Manuela Tosin, Marcio Vinicius Bertacine Dias. How to Find a Fragment: Methods for Screening and Validation in Fragment‐Based Drug Discovery. ChemMedChem 2024, 206 https://doi.org/10.1002/cmdc.202400342
    50. Gianluca Interlandi. Exploring ligands that target von Willebrand factor selectively under oxidizing conditions through docking and molecular dynamics simulations. Proteins: Structure, Function, and Bioinformatics 2024, 92 (11) , 1261-1275. https://doi.org/10.1002/prot.26706
    51. Joseph M. Paggi, Ayush Pandit, Ron O. Dror. The Art and Science of Molecular Docking. Annual Review of Biochemistry 2024, 93 (1) , 389-410. https://doi.org/10.1146/annurev-biochem-030222-120000
    52. Jianzhong Chen, Wei Wang, Haibo Sun, Weikai He. Roles of Accelerated Molecular Dynamics Simulations in Predictions of Binding Kinetic Parameters. Mini-Reviews in Medicinal Chemistry 2024, 24 (14) , 1323-1333. https://doi.org/10.2174/0113895575252165231122095555
    53. Jinan Wang, Yinglong Miao. Ligand Gaussian accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides. 2024https://doi.org/10.1101/2024.05.06.592668
    54. Birte Schmitz, Benedikt Frieg, Nadine Homeyer, Gisela Jessen, Holger Gohlke. Extracting binding energies and binding modes from biomolecular simulations of fragment binding to endothiapepsin. Archiv der Pharmazie 2024, 357 (5) https://doi.org/10.1002/ardp.202300612
    55. Suemin Lee, Dedi Wang, Markus A. Seeliger, Pratyush Tiwary. Calculating Protein-Ligand Residence Times Through State Predictive Information Bottleneck based Enhanced Sampling. 2024https://doi.org/10.1101/2024.04.16.589710
    56. Bertil Schmidt, Andreas Hildebrandt. Dedicated Bioinformatics Analysis Hardware. 2024https://doi.org/10.1016/B978-0-323-95502-7.00022-1
    57. Yujing Zhao, Qilei Liu, Jian Du, Qingwei Meng, Lei Zhang. Machine learning methods for developments of binding kinetic models in predicting protein‐ligand dissociation rate constants. Smart Molecules 2023, 1 (3) https://doi.org/10.1002/smo.20230012
    58. Kento Kasahara, Ren Masayama, Kazuya Okita, Nobuyuki Matubayasi. Elucidating protein–ligand binding kinetics based on returning probability theory. The Journal of Chemical Physics 2023, 159 (13) https://doi.org/10.1063/5.0165692
    59. Davide Bassani, Stefano Moro. Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies. Molecules 2023, 28 (9) , 3906. https://doi.org/10.3390/molecules28093906
    60. Ashfaq Ur Rehman, Beenish Khurshid, Yasir Ali, Salman Rasheed, Abdul Wadood, Ho-Leung Ng, Hai-Feng Chen, Zhiqiang Wei, Ray Luo, Jian Zhang. Computational approaches for the design of modulators targeting protein-protein interactions. Expert Opinion on Drug Discovery 2023, 18 (3) , 315-333. https://doi.org/10.1080/17460441.2023.2171396
    61. Huafeng Xu. The slow but steady rise of binding free energy calculations in drug discovery. Journal of Computer-Aided Molecular Design 2023, 37 (2) , 67-74. https://doi.org/10.1007/s10822-022-00494-x
    62. Shinji Iida, Kameda Tomoshi. Free energy and kinetic rate calculation via non-equilibrium molecular simulation: application to biomolecules. Biophysical Reviews 2022, 14 (6) , 1303-1314. https://doi.org/10.1007/s12551-022-01036-3
    63. Christine Toelzer, Kapil Gupta, Sathish K. N. Yadav, Lorna Hodgson, Maia Kavanagh Williamson, Dora Buzas, Ufuk Borucu, Kyle Powers, Richard Stenner, Kate Vasileiou, Frederic Garzoni, Daniel Fitzgerald, Christine Payré, Gunjan Gautam, Gérard Lambeau, Andrew D. Davidson, Paul Verkade, Martin Frank, Imre Berger, Christiane Schaffitzel. The free fatty acid–binding pocket is a conserved hallmark in pathogenic β-coronavirus spike proteins from SARS-CoV to Omicron. Science Advances 2022, 8 (47) https://doi.org/10.1126/sciadv.adc9179
    64. Sadanandam Namsani, Debabrata Pramanik, Mohd Aamir Khan, Sudip Roy, Jayant Kumar Singh. Metadynamics-based enhanced sampling protocol for virtual screening: case study for 3CLpro protein for SARS-CoV-2. Journal of Biomolecular Structure and Dynamics 2022, 40 (15) , 7002-7017. https://doi.org/10.1080/07391102.2021.1892530
    65. Mrinal Shekhar, Zachary Smith, Markus A. Seeliger, Pratyush Tiwary. Protein Flexibility and Dissociation Pathway Differentiation Can Explain Onset of Resistance Mutations in Kinases**. Angewandte Chemie International Edition 2022, 61 (28) https://doi.org/10.1002/anie.202200983
    66. Mrinal Shekhar, Zachary Smith, Markus A. Seeliger, Pratyush Tiwary. Protein Flexibility and Dissociation Pathway Differentiation Can Explain Onset of Resistance Mutations in Kinases**. Angewandte Chemie 2022, 134 (28) https://doi.org/10.1002/ange.202200983
    67. Amit Kahana, Doron Lancet, Zoltan Palmai. Micellar Composition Affects Lipid Accretion Kinetics in Molecular Dynamics Simulations: Support for Lipid Network Reproduction. Life 2022, 12 (7) , 955. https://doi.org/10.3390/life12070955
    68. Katya Ahmad, Andrea Rizzi, Riccardo Capelli, Davide Mandelli, Wenping Lyu, Paolo Carloni. Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective. Frontiers in Molecular Biosciences 2022, 9 https://doi.org/10.3389/fmolb.2022.899805
    69. Shefali Jain, Ashok Sekhar. Elucidating the mechanisms underlying protein conformational switching using NMR spectroscopy. Journal of Magnetic Resonance Open 2022, 10-11 , 100034. https://doi.org/10.1016/j.jmro.2022.100034
    70. Yunhui Ge, Vincent A. Voelz. Estimation of binding rates and affinities from multiensemble Markov models and ligand decoupling. The Journal of Chemical Physics 2022, 156 (13) https://doi.org/10.1063/5.0088024
    71. Yibing Shan, Venkatesh P. Mysore, Abba E. Leffler, Eric T. Kim, Shiori Sagawa, David E. Shaw, . How does a small molecule bind at a cryptic binding site?. PLOS Computational Biology 2022, 18 (3) , e1009817. https://doi.org/10.1371/journal.pcbi.1009817
    72. Daniel Alvarez-Garcia, Peter Schmidtke, Elena Cubero, Xavier Barril. Extracting Atomic Contributions to Binding Free Energy Using Molecular Dynamics Simulations with Mixed Solvents (MDmix). Current Drug Discovery Technologies 2022, 19 (2) https://doi.org/10.2174/1570163819666211223162829
    73. Hayato Aida, Yasuteru Shigeta, Ryuhei Harada. Ligand Binding Path Sampling Based on Parallel Cascade Selection Molecular Dynamics: LB-PaCS-MD. Materials 2022, 15 (4) , 1490. https://doi.org/10.3390/ma15041490
    74. Xubo Lin. Applications of molecular dynamics simulations in drug discovery. 2022, 455-465. https://doi.org/10.1016/B978-0-323-90264-9.00027-1
    75. Jinan Wang, Apurba Bhattarai, Hung N. Do, Yinglong Miao. Challenges and frontiers of computational modelling of biomolecular recognition. QRB Discovery 2022, 3 https://doi.org/10.1017/qrd.2022.11
    76. Tomio Iwasaki, Masashi Maruyama, Tatsuya Niwa, Toshiki Sawada, Takeshi Serizawa. Design of peptides with strong binding affinity to poly(methyl methacrylate) resin by use of molecular simulation-based materials informatics. Polymer Journal 2021, 53 (12) , 1439-1449. https://doi.org/10.1038/s41428-021-00543-6
    77. Lorena Zara, Nina-Louisa Efrém, Jacqueline E. van Muijlwijk-Koezen, Iwan J.P. de Esch, Barbara Zarzycka. Progress in Free Energy Perturbation: Options for Evolving Fragments. Drug Discovery Today: Technologies 2021, 40 , 36-42. https://doi.org/10.1016/j.ddtec.2021.10.001
    78. Wallace K. B. Chan, Debarati DasGupta, Heather A. Carlson, John R. Traynor. Mixed‐solvent molecular dynamics simulation‐based discovery of a putative allosteric site on regulator of G protein signaling 4. Journal of Computational Chemistry 2021, 42 (30) , 2170-2180. https://doi.org/10.1002/jcc.26747
    79. Serena H. Chen, David R. Bell. Evolution of Thyroglobulin Loop Kinetics in EpCAM. Life 2021, 11 (9) , 915. https://doi.org/10.3390/life11090915
    80. Éderson Sales Moreira Pinto, Bruno César Feltes, Conrado Pedebos, Márcio Dorn. Modifying the catalytic preference of alpha‐amylase toward n ‐alkanes for bioremediation purposes using in silico strategies. Journal of Computational Chemistry 2021, 42 (22) , 1540-1551. https://doi.org/10.1002/jcc.26562
    81. Juan J. Perez, Roman A. Perez, Alberto Perez. Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering. Frontiers in Molecular Biosciences 2021, 8 https://doi.org/10.3389/fmolb.2021.681617
    82. Bin Sun, Peter M. Kekenes-Huskey. Assessing the Role of Calmodulin’s Linker Flexibility in Target Binding. International Journal of Molecular Sciences 2021, 22 (9) , 4990. https://doi.org/10.3390/ijms22094990
    83. Sheenam Khuttan, Solmaz Azimi, Joe Z. Wu, Emilio Gallicchio. Alchemical transformations for concerted hydration free energy estimation with explicit solvation. The Journal of Chemical Physics 2021, 154 (5) https://doi.org/10.1063/5.0036944
    84. Justin Spiriti, Chung F. Wong. Qualitative Prediction of Ligand Dissociation Kinetics from Focal Adhesion Kinase Using Steered Molecular Dynamics. Life 2021, 11 (2) , 74. https://doi.org/10.3390/life11020074
    85. Yunhui Ge, Vincent A. Voelz. Markov State Models to Elucidate Ligand Binding Mechanism. 2021, 239-259. https://doi.org/10.1007/978-1-0716-1209-5_14
    86. Xiaowen Wang, Nidhi Singh, Wenjin Li. Molecular Dynamics Simulation of Biomolecular Interactions. 2021, 182-189. https://doi.org/10.1016/B978-0-12-801238-3.11465-5
    87. Shashank Pant, Zachary Smith, Yihang Wang, Emad Tajkhorshid, Pratyush Tiwary. Confronting pitfalls of AI-augmented molecular dynamics using statistical physics. The Journal of Chemical Physics 2020, 153 (23) https://doi.org/10.1063/5.0030931
    88. Ali Dashti, Ghoncheh Mashayekhi, Mrinal Shekhar, Danya Ben Hail, Salah Salah, Peter Schwander, Amedee des Georges, Abhishek Singharoy, Joachim Frank, Abbas Ourmazd. Retrieving functional pathways of biomolecules from single-particle snapshots. Nature Communications 2020, 11 (1) https://doi.org/10.1038/s41467-020-18403-x
    89. Dhiman Ray, Trevor Gokey, David L. Mobley, Ioan Andricioaei. Kinetics and free energy of ligand dissociation using weighted ensemble milestoning. The Journal of Chemical Physics 2020, 153 (15) https://doi.org/10.1063/5.0021953
    90. Maicol Bissaro, Mattia Sturlese, Stefano Moro. The rise of molecular simulations in fragment-based drug design (FBDD): an overview. Drug Discovery Today 2020, 25 (9) , 1693-1701. https://doi.org/10.1016/j.drudis.2020.06.023
    91. Robert Hall, Tom Dixon, Alex Dickson. On Calculating Free Energy Differences Using Ensembles of Transition Paths. Frontiers in Molecular Biosciences 2020, 7 https://doi.org/10.3389/fmolb.2020.00106
    92. Francesca Nerattini, Matteo Figliuzzi, Chiara Cardelli, Luca Tubiana, Valentino Bianco, Christoph Dellago, Ivan Coluzza. Identification of Protein Functional Regions. ChemPhysChem 2020, 21 (4) , 335-347. https://doi.org/10.1002/cphc.201900898
    93. Pavan Ravindra, Zachary Smith, Pratyush Tiwary. Automatic mutual information noise omission (AMINO): generating order parameters for molecular systems. Molecular Systems Design & Engineering 2020, 5 (1) , 339-348. https://doi.org/10.1039/C9ME00115H
    94. Zhenliang Wu, Yuwei Zhang, John Zenghui Zhang, Kelin Xia, Fei Xia. Determining Optimal Coarse‐Grained Representation for Biomolecules Using Internal Cluster Validation Indexes. Journal of Computational Chemistry 2020, 41 (1) , 14-20. https://doi.org/10.1002/jcc.26070
    95. Chetan Chintha, Antonio Carlesso, Adrienne M. Gorman, Afshin Samali, Leif A. Eriksson. Molecular modeling provides a structural basis for PERK inhibitor selectivity towards RIPK1. RSC Advances 2020, 10 (1) , 367-375. https://doi.org/10.1039/C9RA08047C
    96. Rajat K. Pal, Emilio Gallicchio. Perturbation potentials to overcome order/disorder transitions in alchemical binding free energy calculations. The Journal of Chemical Physics 2019, 151 (12) https://doi.org/10.1063/1.5123154
    97. Bertil Schmidt, Andreas Hildebrandt. Dedicated Bioinformatics Analysis Hardware. 2019, 1142-1150. https://doi.org/10.1016/B978-0-12-809633-8.20186-6
    98. Vojtěch Spiwok. Predictive Power of Biomolecular Simulations. 2018, 1-26. https://doi.org/10.1002/9783527806836.ch1
    99. Lucas A. Defelipe, Juan Pablo Arcon, Carlos P. Modenutti, Marcelo A. Marti, Adrián G. Turjanski, Xavier Barril. Solvents to Fragments to Drugs: MD Applications in Drug Design. Molecules 2018, 23 (12) , 3269. https://doi.org/10.3390/molecules23123269
    100. Jörg Baz, Julia Gebhardt, Hamzeh Kraus, Daniel Markthaler, Niels Hansen. Insights into Noncovalent Binding Obtained from Molecular Dynamics Simulations. Chemie Ingenieur Technik 2018, 90 (11) , 1864-1875. https://doi.org/10.1002/cite.201800050
    Load all citations
    Download PDF
    Go to Journal of Chemical Theory and Computation
    Get e-Alerts
    Get e-Alerts

    Journal of Chemical Theory and Computation

    Cite this: J. Chem. Theory Comput. 2017, 13, 7, 3372–3377
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jctc.7b00172
    Published June 5, 2017
    Copyright © 2017 American Chemical Society
    Request reuse permissions

    Article Views

    3885

    Altmetric

    -

    Citations

    105
    Learn about these metrics

    Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.

    Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.

    The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.