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Current Assessment of Docking into GPCR Crystal Structures and Homology Models: Successes, Challenges, and Guidelines

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Schrödinger, Inc., 120 West 45th Street, New York, New York, United States
*E-mail: [email protected]. Phone: 212 295 5800. Fax: 212 295 5801.
Cite this: J. Chem. Inf. Model. 2012, 52, 12, 3263–3277
Publication Date (Web):November 3, 2012
https://doi.org/10.1021/ci300411b
Copyright © 2012 American Chemical Society

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Abstract

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The growing availability of novel structures for several G protein-coupled receptors (GPCRs) has provided new opportunities for structure-based drug design of ligands against this important class of targets. Here, we report a systematic analysis of the accuracy of docking small molecules into GPCR structures and homology models using both rigid receptor (Glide SP and Glide XP) and flexible receptor (Induced Fit Docking; IFD) methods. The ability to dock ligands into different structures of the same target (cross-docking) is evaluated for both agonist and inverse agonist structures of the A2A receptor and the β1- and β2-adrenergic receptors. In addition, we have produced homology models for the β1-adrenergic, β2-adrenergic, D3 dopamine, H1 histamine, M2 muscarine, M3 muscarine, A2A adenosine, S1P1, κ-opioid, and C-X-C chemokine 4 receptors using multiple templates and investigated the ability of docking to predict the binding mode of ligands in these models. Clear correlations are observed between the docking accuracy and the similarity of the sequence of interest to the template, suggesting regimes in which docking can correctly identify ligand binding modes.

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Full RMSD results for all methods and benchmarks are available as Tables S1–S9. A comparison of the performance of all methods with and without loops is shown in Table S10. Pairwise sequence identities for the full sequences are shown in Table S11. Rigid docking example poses are provided in Figures S1 and S2. A correlation plot between docking RMSD and binding site RMSD is shown in Figure S3. This material is available free of charge via the Internet at http://pubs.acs.org.

Accession Codes

PDB ID Codes:2VT4, 2YCW, 2Y00, 2Y02, 2Y03, 2Y04, 2RH1, 3D4S, 3NY8, 3NY9, 3NYA, 3P0G, 3PBL, 3RZE, 3UON, 4DAJ, 3EML, 3REY, 3RFM, 2YDO, 2YDV, 3QAK, 3V2Y, 3ODU, 4DHJ, 4DKL

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  6. Paul C. D. Hawkins, Brian P. Kelley, and Gregory L. Warren . The Application of Statistical Methods to Cognate Docking: A Path Forward?. Journal of Chemical Information and Modeling 2014, 54 (5) , 1339-1355. https://doi.org/10.1021/ci5001086
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  9. Ákos Tarcsay, Gábor Paragi, Márton Vass, Balázs Jójárt, Ferenc Bogár, and György M. Keserű . The Impact of Molecular Dynamics Sampling on the Performance of Virtual Screening against GPCRs. Journal of Chemical Information and Modeling 2013, 53 (11) , 2990-2999. https://doi.org/10.1021/ci400087b
  10. Daniele Pala, Thijs Beuming, Woody Sherman, Alessio Lodola, Silvia Rivara, and Marco Mor . Structure-Based Virtual Screening of MT2 Melatonin Receptor: Influence of Template Choice and Structural Refinement. Journal of Chemical Information and Modeling 2013, 53 (4) , 821-835. https://doi.org/10.1021/ci4000147
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  17. Pratanphorn Nakliang, Raudah Lazim, Hyerim Chang, Sun Choi. Multiscale Molecular Modeling in G Protein-Coupled Receptor (GPCR)-Ligand Studies. Biomolecules 2020, 10 (4) , 631. https://doi.org/10.3390/biom10040631
  18. Lu Qu, Qingtong Zhou, Yueming Xu, Yu Guo, Xiaoyu Chen, Deqiang Yao, Gye Won Han, Zhi-Jie Liu, Raymond C. Stevens, Guisheng Zhong, Dong Wu, Suwen Zhao. Structural Basis of the Diversity of Adrenergic Receptors. Cell Reports 2019, 29 (10) , 2929-2935.e4. https://doi.org/10.1016/j.celrep.2019.10.088
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  20. . The Dopaminergic System. 2019, 1-39. https://doi.org/10.1002/9783527813421.ch1
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  23. Mercedes Alfonso-Prieto, Luciano Navarini, Paolo Carloni. Understanding Ligand Binding to G-Protein Coupled Receptors Using Multiscale Simulations. Frontiers in Molecular Biosciences 2019, 6 https://doi.org/10.3389/fmolb.2019.00029
  24. Mei Qian Yau, Abigail L. Emtage, Nathaniel J. Y. Chan, Stephen W. Doughty, Jason S. E. Loo. Evaluating the performance of MM/PBSA for binding affinity prediction using class A GPCR crystal structures. Journal of Computer-Aided Molecular Design 2019, 33 (5) , 487-496. https://doi.org/10.1007/s10822-019-00201-3
  25. Christian A. Söldner, Anselm H. C. Horn, Heinrich Sticht. A Metadynamics-Based Protocol for the Determination of GPCR-Ligand Binding Modes. International Journal of Molecular Sciences 2019, 20 (8) , 1970. https://doi.org/10.3390/ijms20081970
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  27. Jason S.E. Loo, Abigail L. Emtage, Kar Weng Ng, Alene S.J. Yong, Stephen W. Doughty. Assessing GPCR homology models constructed from templates of various transmembrane sequence identities: Binding mode prediction and docking enrichment. Journal of Molecular Graphics and Modelling 2018, 80 , 38-47. https://doi.org/10.1016/j.jmgm.2017.12.017
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  40. Agnieszka A. Kaczor, Ewelina Rutkowska, Damian Bartuzi, Katarzyna M. Targowska-Duda, Dariusz Matosiuk, Jana Selent. Computational methods for studying G protein-coupled receptors (GPCRs). 2016, 359-399. https://doi.org/10.1016/bs.mcb.2015.11.002
  41. Minsup Kim, Art E. Cho. Incorporating QM and solvation into docking for applications to GPCR targets. Physical Chemistry Chemical Physics 2016, 18 (40) , 28281-28289. https://doi.org/10.1039/C6CP04742D
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  44. Antonella Ciancetta, Davide Sabbadin, Stephanie Federico, Giampiero Spalluto, Stefano Moro. Advances in Computational Techniques to Study GPCR–Ligand Recognition. Trends in Pharmacological Sciences 2015, 36 (12) , 878-890. https://doi.org/10.1016/j.tips.2015.08.006
  45. Jan Jakubík, Esam E. El-Fakahany, Vladimír Doležal. Towards predictive docking at aminergic G-protein coupled receptors. Journal of Molecular Modeling 2015, 21 (11) https://doi.org/10.1007/s00894-015-2824-9
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  47. Elizabeth Yuriev, Jessica Holien, Paul A. Ramsland. Improvements, trends, and new ideas in molecular docking: 2012-2013 in review. Journal of Molecular Recognition 2015, 28 (10) , 581-604. https://doi.org/10.1002/jmr.2471
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  52. Edit Szőllősi, Amrita Bobok, László Kiss, Márton Vass, Dalma Kurkó, Sándor Kolok, András Visegrády, György M. Keserű. Cell-based and virtual fragment screening for adrenergic α2C receptor agonists. Bioorganic & Medicinal Chemistry 2015, 23 (14) , 3991-3999. https://doi.org/10.1016/j.bmc.2015.01.013
  53. Gengyang Yuan, Nicholas G Gedeon, Tanner C Jankins, Graham B Jones. Novel approaches for targeting the adenosine A 2A receptor. Expert Opinion on Drug Discovery 2015, 10 (1) , 63-80. https://doi.org/10.1517/17460441.2015.971006
  54. Marie-Annick Persuy, Guenhaël Sanz, Anne Tromelin, Thierry Thomas-Danguin, Jean-François Gibrat, Edith Pajot-Augy. Mammalian Olfactory Receptors. 2015, 1-36. https://doi.org/10.1016/bs.pmbts.2014.11.001
  55. Mayako Michino, Thijs Beuming, Prashant Donthamsetti, Amy Hauck Newman, Jonathan A. Javitch, Lei Shi, . What Can Crystal Structures of Aminergic Receptors Tell Us about Designing Subtype-Selective Ligands?. Pharmacological Reviews 2015, 67 (1) , 198-213. https://doi.org/10.1124/pr.114.009944
  56. Mayako Michino, Lei Shi. Computational Approaches in the Structure–Function Studies of Dopamine Receptors. 2015, 31-42. https://doi.org/10.1007/978-1-4939-2196-6_3
  57. Thijs Beuming, Bart Lenselink, Daniele Pala, Fiona McRobb, Matt Repasky, Woody Sherman. Docking and Virtual Screening Strategies for GPCR Drug Discovery. 2015, 251-276. https://doi.org/10.1007/978-1-4939-2914-6_17
  58. Kavita Kumari Kakarala, Kaiser Jamil, Vinod Devaraji. Structure and putative signaling mechanism of Protease activated receptor 2 (PAR2) – A promising target for breast cancer. Journal of Molecular Graphics and Modelling 2014, 53 , 179-199. https://doi.org/10.1016/j.jmgm.2014.07.012
  59. Bryan D. Cox, Anthony R. Prosser, Brooke M. Katzman, Ana A. Alcaraz, Dennis C. Liotta, Lawrence J. Wilson, James P. Snyder. Anti-HIV Small-Molecule Binding in the Peptide Subpocket of the CXCR4:CVX15 Crystal Structure. ChemBioChem 2014, 15 (11) , 1614-1620. https://doi.org/10.1002/cbic.201402056
  60. Tobias Schmidt, Andreas Bergner, Torsten Schwede. Modelling three-dimensional protein structures for applications in drug design. Drug Discovery Today 2014, 19 (7) , 890-897. https://doi.org/10.1016/j.drudis.2013.10.027
  61. Krzysztof Rataj, Jagna Witek, Stefan Mordalski, Tomasz Kosciolek, Andrzej J. Bojarski. Impact of Template Choice on Homology Model Efficiency in Virtual Screening. Journal of Chemical Information and Modeling 2014, 54 (6) , 1661-1668. https://doi.org/10.1021/ci500001f
  62. Subha Kalyaanamoorthy, Yi-Ping Phoebe Chen. Modelling and enhanced molecular dynamics to steer structure-based drug discovery. Progress in Biophysics and Molecular Biology 2014, 114 (3) , 123-136. https://doi.org/10.1016/j.pbiomolbio.2013.06.004
  63. Nikos S. Hatzakis. Single molecule insights on conformational selection and induced fit mechanism. Biophysical Chemistry 2014, 186 , 46-54. https://doi.org/10.1016/j.bpc.2013.11.003
  64. Raymond J. Terryn, Helen W. German, Theresa M. Kummerer, Richard R. Sinden, J. Clayton Baum, Mark J. Novak. Novel computational study on π -stacking to understand mechanistic interactions of Tryptanthrin analogues with DNA. Toxicology Mechanisms and Methods 2014, 24 (1) , 73-79. https://doi.org/10.3109/15376516.2013.859194
  65. Guenhaël Sanz, Jean-François Gibrat, Edith Pajot-Augy. Olfactory Receptor Proteins. 2014, 47-68. https://doi.org/10.1007/978-94-017-8613-3_3
  66. Sid Topiol. X-ray structural information of GPCRs in drug design: what are the limitations and where do we go?. Expert Opinion on Drug Discovery 2013, 8 (6) , 607-620. https://doi.org/10.1517/17460441.2013.783815
  67. Yohsuke Hagiwara, Kazuki Ohno, Masazumi Kamohara, Jun Takasaki, Toshihiro Watanabe, Yoshifumi Fukunishi, Haruki Nakamura, Masaya Orita. Molecular modeling of vasopressin receptor and in silico screening of V1b receptor antagonists. Expert Opinion on Drug Discovery 2013, 8 (8) , 951-964. https://doi.org/10.1517/17460441.2013.799134
  68. Elisabet Gregori-Puigjané. Computational methods based on molecular shape. 2013, 120-132. https://doi.org/10.4155/ebo.13.183
  69. Elizabeth Dong Nguyen, Christoffer Norn, Thomas M. Frimurer, Jens Meiler, . Assessment and Challenges of Ligand Docking into Comparative Models of G-Protein Coupled Receptors. PLoS ONE 2013, 8 (7) , e67302. https://doi.org/10.1371/journal.pone.0067302
  70. Miquel Duran-Frigola, Roberto Mosca, Patrick Aloy. Structural Systems Pharmacology: The Role of 3D Structures in Next-Generation Drug Development. Chemistry & Biology 2013, 20 (5) , 674-684. https://doi.org/10.1016/j.chembiol.2013.03.004
  71. Stefano Costanzi. Modeling G protein-coupled receptors and their interactions with ligands. Current Opinion in Structural Biology 2013, 23 (2) , 185-190. https://doi.org/10.1016/j.sbi.2013.01.008
  72. Daniele Pala, Alessio Lodola, Annalida Bedini, Gilberto Spadoni, Silvia Rivara. Homology Models of Melatonin Receptors: Challenges and Recent Advances. International Journal of Molecular Sciences 2013, 14 (4) , 8093-8121. https://doi.org/10.3390/ijms14048093
  73. G. Madhavi Sastry, Matvey Adzhigirey, Tyler Day, Ramakrishna Annabhimoju, Woody Sherman. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. Journal of Computer-Aided Molecular Design 2013, 27 (3) , 221-234. https://doi.org/10.1007/s10822-013-9644-8

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