Predicting Binding Poses and Affinities in the CSAR 2013-2014 Docking Exercises Using the Knowledge-Based Convex-PL Potential

Journal of Chemical Information and Modeling

Volumn 56, Issue 6, 2016, Pages 1053-1062

  Grudinin, Sergei ^a,b,c   Popov, Petr ^a,b,c,d   Neveu, Emilie ^a,b,c   Cheremovskiy, Georgy ^a,b,c,d  

^a UNIV GRENOBLE ALPES   (France)

^b UNIV GRENOBLE ALPES   (France)

^c INRIA   (France)

^d MOSCOW INSTITUTE OF PHYSICS AND TECHNOLOGY   (Russian Federation)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING; LIGANDS; PROTEINS;

BINDING CONSTANT; KNOWLEDGE BASED; POSE DETECTION; PROTEIN LIGANDS; SPEARMAN CORRELATION COEFFICIENTS; STRUCTURAL INFORMATION;

KNOWLEDGE BASED SYSTEMS;

LIGAND; PROTEIN; PROTEIN BINDING;

BIOLOGY; CHEMISTRY; DRUG DEVELOPMENT; METABOLISM; MOLECULAR DOCKING; PROTEIN CONFORMATION; PROTEIN DATABASE;

COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; DRUG DISCOVERY; LIGANDS; MOLECULAR DOCKING SIMULATION; PROTEIN BINDING; PROTEIN CONFORMATION; PROTEINS;

EID: 84976394935     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/acs.jcim.5b00339     Document Type: Article

References (62)

1. Wang, R.; Lu, Y.; Wang, S. Comparative Evaluation of 11 Scoring Functions for Molecular Docking J. Med. Chem. 2003, 46, 2287-2303 10.1021/jm0203783
2. Jorgensen, W. L. The Many Roles of Computation in Drug Discovery Science 2004, 303, 1813-1818 10.1126/science.1096361
3. Warren, G. L. A Critical Assessment of Docking Programs and Scoring Functions J. Med. Chem. 2006, 49, 5912-5931 10.1021/jm050362n
4. Jorgensen, W. L. Efficient Drug Lead Discovery and Optimization Acc. Chem. Res. 2009, 42, 724-733 10.1021/ar800236t
5. Cheng, T.; Li, X.; Li, Y.; Liu, Z.; Wang, R. Comparative Assessment of Scoring Functions on a Diverse Test Set J. Chem. Inf. Model. 2009, 49, 1079-1093 10.1021/ci9000053
6. Trott, O.; Olson, A. J. AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading J. Comput. Chem. 2010, 31, 455-461 10.1002/jcc.21334
7. Cheng, T.; Li, Q.; Zhou, Z.; Wang, Y.; Bryant, S. H. Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review AAPS J. 2012, 14, 133-141 10.1208/s12248-012-9322-0
8. Damm-Ganamet, K. L.; Smith, R. D.; Dunbar, J. B., Jr; Stuckey, J. A.; Carlson, H. A. CSAR Benchmark Exercise 2011-2012: Evaluation of Results from Docking and Relative Ranking of Blinded Congeneric Series J. Chem. Inf. Model. 2013, 53, 1853-1870 10.1021/ci400025f
9. Kitchen, D. B.; Decornez, H.; Furr, J. R.; Bajorath, J. Docking and Scoring in Virtual Screening for Drug Discovery: Methods and Applications Nat. Rev. Drug Discovery 2004, 3, 935-949 10.1038/nrd1549
10. Li, Y.; Han, L.; Liu, Z.; Wang, R. Comparative Assessment of Scoring Functions on an Updated Benchmark: 2. Evaluation Methods an General Results J. Chem. Inf. Model. 2014, 54, 1717-36 10.1021/ci500081m
11. Smith, R. D.; Dunbar, J. B.; Ung, P. M.; Esposito, E. X.; Yang, C. Y.; Wang, S.; Carlson, H. A. CSAR Benchmark Exercise of 2010: Combined Evaluation Across All Submitted Scoring Functions J. Chem. Inf. Model. 2011, 51, 2115-2131 10.1021/ci200269q
12. McInnes, C. Virtual Screening Strategies in Drug Discovery Curr. Opin. Chem. Biol. 2007, 11, 494-502 10.1016/j.cbpa.2007.08.033
13. Zheng, Z.; Merz, K. M. Development of the Knowledge-Based and Empirical Combined Scoring Algorithm (KECSA) to Score Protein-Ligand Interactions J. Chem. Inf. Model. 2013, 53, 1073-1083 PMID: 23560465 10.1021/ci300619x
14. Liu, J.; Wang, R. Classification of Current Scoring Functions J. Chem. Inf. Model. 2015, 55, 475-482 PMID: 25647463 10.1021/ci500731a
15. Brooks, B. R.; Bruccoleri, R. E.; Olafson, B. D.; States, D. J.; Swaminathan, S.; Karplus, M. Charmm: A Program for Macromolecular Energy, Minimization, and Dynamics Calculations J. Comput. Chem. 1983, 4, 187-217 10.1002/jcc.540040211
16. Jorgensen, W. L.; Maxwell, D. S.; Tirado-Rives, J. Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids J. Am. Chem. Soc. 1996, 118, 11225-11236 10.1021/ja9621760
17. Ewing, T. J.; Makino, S.; Skillman, A. G.; Kuntz, I. D. Dock 4.0: Search Strategies for Automated Molecular Docking of Flexible Molecule Databases J. Comput.-Aided Mol. Des. 2001, 15, 411-428 10.1023/A:1011115820450
18. Case, D. A.; Cheatham, T. E.; Darden, T.; Gohlke, H.; Luo, R.; Merz, K. M.; Onufriev, A.; Simmerling, C.; Wang, B.; Woods, R. J. The Amber Biomolecular Simulation Programs J. Comput. Chem. 2005, 26, 1668-1688 10.1002/jcc.20290
19. Hess, B.; Kutzner, C.; Van Der Spoel, D.; Lindahl, E. GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation J. Chem. Theory Comput. 2008, 4, 435-447 10.1021/ct700301q
20. Kuhn, B.; Gerber, P.; Schulz-Gasch, T.; Stahl, M. Validation and Use of the MM-PBSA Approach for Drug Discovery J. Med. Chem. 2005, 48, 4040-4048 PMID: 15943477 10.1021/jm049081q
21. Chaskar, P.; Zoete, V.; Röhrig, U. F. Toward On-The-Fly Quantum Mechanical/Molecular Mechanical (QM/MM) Docking: Development and Benchmark of a Scoring Function J. Chem. Inf. Model. 2014, 54, 3137-3152 PMID: 25296988 10.1021/ci5004152
22. Böhm, H.-J. The Development of a Simple Empirical Scoring Function to Estimate the Binding Constant for a Protein-Ligand Complex of Known Three-Dimensional Structure J. Comput.-Aided Mol. Des. 1994, 8, 243-256 10.1007/BF00126743
23. Eldridge, M. D.; Murray, C. W.; Auton, T. R.; Paolini, G. V.; Mee, R. P. Empirical Scoring Functions: I. The Development of a Fast Empirical Scoring Function to Estimate the Binding Affinity of Ligands in Receptor Complexes J. Comput.-Aided Mol. Des. 1997, 11, 425-445 10.1023/A:1007996124545
24. Wang, R.; Lai, L.; Wang, S. Further Development and Validation of Empirical Scoring Functions for Structure-Based Binding Affinity Prediction J. Comput.-Aided Mol. Des. 2002, 16, 11-26 10.1023/A:1016357811882
25. Friesner, R. A. Glide: a New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy J. Med. Chem. 2004, 47, 1739-1749 10.1021/jm0306430
26. Korb, O.; Stutzle, T.; Exner, T. E. Empirical Scoring Functions for Advanced Protein-Ligand Docking With Plants J. Chem. Inf. Model. 2009, 49, 84-96 10.1021/ci800298z
27. Muegge, I.; Martin, Y. C. A General and Fast Scoring Function for Protein-Ligand Interactions: a Simplified Potential Approach J. Med. Chem. 1999, 42, 791-804 10.1021/jm980536j
28. Mooij, W.; Verdonk, M. L. General and Targeted Statistical Potentials for Protein-Ligand Interactions Proteins: Struct., Funct., Genet. 2005, 61, 272-287 10.1002/prot.20588
29. Huang, S.-Y.; Zou, X. Mean-Force Scoring Functions for Protein-Ligand Binding Annu. Rep. Comput. Chem. 2010, 6, 280-296 10.1016/S1574-1400(10)06014-7
30. Zhou, H.; Skolnick, J. GOAP: a Generalized Orientation-Dependent, All-Atom Statistical Potential for Protein Structure Prediction Biophys. J. 2011, 101, 2043-2052 10.1016/j.bpj.2011.09.012
31. Huang, S.-Y.; Zou, X. Scoring and Lessons Learned with the CSAR Benchmark Using an Improved Iterative Knowledge-Based Scoring Function J. Chem. Inf. Model. 2011, 51, 2097-2106 10.1021/ci2000727
32. Popov, P.; Grudinin, S. Knowledge of Native Protein-Protein Interfaces is Sufficient to Construct Predictive Models for the Selection of Binding Candidates J. Chem. Inf. Model. 2015, 55, 2242 PMID: 26353078 10.1021/acs.jcim.5b00372
33. Gohlke, H.; Hendlich, M.; Klebe, G. Knowledge-Based Scoring Function to Predict Protein-Ligand Interactions J. Mol. Biol. 2000, 295, 337-356 10.1006/jmbi.1999.3371
34. Huang, S.-Y.; Zou, X. Inclusion of Solvation and Entropy in the Knowledge-Based Scoring Function for Protein-Ligand Interactions J. Chem. Inf. Model. 2010, 50, 262-273 PMID: 20088605 10.1021/ci9002987
35. Kinnings, S. L.; Liu, N.; Tonge, P. J.; Jackson, R. M.; Xie, L.; Bourne, P. E. A Machine Learning-Based Method to Improve Docking Scoring Functions and its Application to Drug Repurposing J. Chem. Inf. Model. 2011, 51, 408-419 PMID: 21291174 10.1021/ci100369f
36. Zilian, D.; Sotriffer, C. A. SFCscoreRF: A Random Forest-Based Scoring Function for Improved Affinity Prediction of Protein-Ligand Complexes J. Chem. Inf. Model. 2013, 53, 1923-1933 PMID: 23705795 10.1021/ci400120b
37. Li, G.-B.; Yang, L.-L.; Wang, W.-J.; Li, L.-L.; Yang, S.-Y. D-Score: A New Empirical Scoring Function Based on a Comprehensive Set of Descriptors Related to Protein-Ligand Interactions J. Chem. Inf. Model. 2013, 53, 592-600 PMID: 23394072 10.1021/ci300493w
38. Gabel, J.; Desaphy, J.; Rognan, D. Beware of Machine Learning-Based Scoring Functions-On the Danger of Developing Black Boxes J. Chem. Inf. Model. 2014, 54, 2807-2815 PMID: 25207678 10.1021/ci500406k
39. Sotriffer, C. Scoring Functions for Protein-Ligand Interactions. In Protein-Ligand Interactions, First ed.; Wiley Online Library, 2012; pp 237-263.
40. Sotriffer, C.; Matter, H. The Challenge of Affinity Prediction: Scoring Functions for Structure-Based Virtual Screening. In Virtual Screening: Principles, Challenges, and Practical Guidelines, 10 th ed.; Wiley Online Library, 2011; pp 177-221.
41. Cross, J. B.; Thompson, D. C.; Rai, B. K.; Baber, J. C.; Fan, K. Y.; Hu, Y.; Humblet, C. Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy J. Chem. Inf. Model. 2009, 49, 1455-1474 10.1021/ci900056c
42. Hsieh, J.-H.; Yin, S.; Liu, S.; Sedykh, A.; Dokholyan, N. V.; Tropsha, A. Combined Application of Cheminformatics and Physical Force Field-Based Scoring Functions Improves Binding Affinity Prediction for Csar Data Sets J. Chem. Inf. Model. 2011, 51, 2027-2035 10.1021/ci200146e
43. Huang, S.-Y.; Zou, X. An Iterative Knowledge-Based Scoring Function for Protein-Protein Recognition Proteins: Struct., Funct., Genet. 2008, 72, 557-579 10.1002/prot.21949
44. Chuang, G.-Y.; Kozakov, D.; Brenke, R.; Comeau, S. R.; Vajda, S. Dars (Decoys As The Reference State) Potentials for Protein-Protein Docking Biophys. J. 2008, 95, 4217-4227 10.1529/biophysj.108.135814
45. Maiorov, V. N.; Grippen, G. M. Contact Potential that Recognizes the Correct Folding of Globular Proteins J. Mol. Biol. 1992, 227, 876-888 10.1016/0022-2836(92)90228-C
46. Qiu, J.; Elber, R. Atomically Detailed Potentials to Recognize Native and Approximate Protein Structures Proteins: Struct., Funct., Genet. 2005, 61, 44-55 10.1002/prot.20585
47. Rajgaria, R.; McAllister, S.; Floudas, C. A Novel High Resolution Cα-Cα Distance Dependent Force Field Based on a High Quality Decoy Set Proteins: Struct., Funct., Genet. 2006, 65, 726-741 10.1002/prot.21149
48. Tobi, D.; Bahar, I. Optimal Design of Protein Docking Potentials: Efficiency and Limitations Proteins: Struct., Funct., Genet. 2006, 62, 970-981 10.1002/prot.20859
49. Ravikant, D.; Elber, R. PIE-Efficient Filters and Coarse Grained Potentials for Unbound Protein-Protein Docking Proteins: Struct., Funct., Genet. 2010, 78, 400-419 10.1002/prot.22550
50. Chae, M.-H.; Krull, F.; Lorenzen, S.; Knapp, E.-W. Predicting Protein Complex Geometries with a Neural Network Proteins: Struct., Funct., Genet. 2010, 78, 1026-1039 10.1002/prot.22626
51. Boyd, S.; Vandenberghe, L. Convex Optimization; Cambridge University Press: New York, 2004.
52. Nocedal, J.; Wright, S. Numerical Optimization; Springer Series in Operations Research and Financial Engineering; Springer: New York, 2000.
53. Wang, R.; Fang, X.; Lu, Y.; Yang, C.-Y.; Wang, S. The PDBBind Database: Methodologies And Updates J. Med. Chem. 2005, 48, 4111-9 10.1021/jm048957q
54. Wang, R.; Fang, X.; Lu, Y.; Wang, S. The PDBBind Database: Collection of Binding Affinities for Protein-Ligand Complexes with Known Three-Dimensional Structures J. Med. Chem. 2004, 47, 2977-80 10.1021/jm030580l
55. Popov, P.; Grudinin, S. Rapid Determination of RMSDs Corresponding to Macromolecular Rigid Body Motions J. Comput. Chem. 2014, 35, 950-956 10.1002/jcc.23569
56. Neudert, G.; Klebe, G. fconv: Format Conversion, Manipulation and Feature Computation of Molecular Data Bioinformatics 2011, 27, 1021-1022 10.1093/bioinformatics/btr055
57. O'Boyle, N. M.; Banck, M.; James, C. A.; Morley, C.; Vandermeersch, T.; Hutchison, G. R. Open Babel: an Open Chemical Toolbox J. Cheminf. 2011, 3, 33 10.1186/1758-2946-3-33
58. Webb, B.; Sali, A. Comparative Protein Structure Modeling Using Modeller Curr. Protoc. Bioinformatics 2014, 5.6.1 10.1002/0471250953.bi0506s47
59. The PyMOL Molecular Graphics System, Version 1.3; Schrödinger, LLC, 2011.
60. Moustakas, D. T.; Lang, P. T.; Pegg, S.; Pettersen, E.; Kuntz, I. D.; Brooijmans, N.; Rizzo, R. C. Development and Validation of a Modular, Extensible Docking Program: DOCK 5 J. Comput.-Aided Mol. Des. 2006, 20, 601-619 10.1007/s10822-006-9060-4
61. Molecular Operating Environment (MOE), 2013.08; Chemical Computing Group Inc.: Montreal, QC, Canada, 2015.
62. Halgren, T. A. Merck Molecular Force Field. I. Basis, Form, Scope, Parameterization, and Performance of Mmff94 J. Comput. Chem. 1996, 17, 490-519 10.1002/(SICI)1096-987X(199604)17:6<490::AID-JCC1>3.3.CO;2-V