An electronic environment and contact direction sensitive scoring function for predicting affinities of protein-ligand complexes in Contour®

Journal of Molecular Graphics and Modelling

Volumn 53, Issue , 2014, Pages 118-127

  Lindblom, Peter R ^a   Wu, Guosheng ^a   Liu, Zhijie ^a   Jim, Kam Chuen ^a   Baldwin, John J ^a   Gregg, Richard E ^a   Claremon, David A ^a   Singh, Suresh B ^a  

^a Vitae Pharmaceuticals   (United States)

Author keywords

Binding energy; Interaction zones; Molecular orbitals; Protein ligand interactions; Scoring function

Indexed keywords

ATOMS; BINDING ENERGY; COMPLEXATION; ELECTROSTATICS; HYDROGEN BONDS; MOLECULAR ORBITALS; MOLECULES; ORBITAL CALCULATIONS;

HIGH RESOLUTION CRYSTAL STRUCTURE; INTERACTION ZONE; INTERMOLECULAR INTERACTIONS; PROTEIN-LIGAND ASSOCIATION; PROTEIN-LIGAND COMPLEXES; PROTEIN-LIGAND INTERACTIONS; SCORING FUNCTIONS; STRUCTURE-BASED DRUG DESIGN;

ASSOCIATION REACTIONS;

ALGORITHM; ARTICLE; BINDING AFFINITY; COMPLEX FORMATION; CORRELATION COEFFICIENT; CRYSTAL STRUCTURE; HYBRIDIZATION; HYDROGEN BOND; MOLECULAR INTERACTION; MOLECULAR MODEL; POLARIZATION; PRIORITY JOURNAL; PROTEIN BINDING; PROTEIN INTERACTION; STATIC ELECTRICITY; BINDING SITE; CHEMICAL STRUCTURE; CHEMISTRY; COMPUTER PROGRAM; PROTEIN TERTIARY STRUCTURE; QUANTUM THEORY; THERMODYNAMICS;

LIGAND; PROTEIN; PROTEIN BINDING;

BINDING SITES; HYDROGEN BONDING; LIGANDS; MODELS, MOLECULAR; PROTEIN BINDING; PROTEIN STRUCTURE, TERTIARY; PROTEINS; QUANTUM THEORY; SOFTWARE; THERMODYNAMICS;

EID: 84906076065     PISSN: 10933263     EISSN: 18734243     Source Type: Journal    
DOI: 10.1016/j.jmgm.2014.07.010     Document Type: Article

References (63)

1. A. Fersht Enzyme Structure and Mechanism 2nd ed. 1985 W.H. Freeman and Company New York 475
2. H. Gohlke Protein-Ligand Interactions vol. 53 2012 Wiley-VCH Verlag GmbH & Co. KGaA Weinheim 1 339
3. H.X. Zhou, and M.K. Gilson Theory of free energy and entropy in noncovalent binding Chem. Rev. 109 9 2009 4092 4107
4. K.W. Harpole, and K.A. Sharp Calculation of configurational entropy with a Boltzmann-quasiharmonic model: the origin of high-affinity protein-ligand binding J. Phys. Chem. B 115 30 2011 9461 9472
5. M.J. Stone NMR relaxation studies of the role of conformational entropy in protein stability and ligand binding Acc. Chem. Res. 34 5 2001 379 388
6. R.O. Dror, R.M. Dirks, J.P. Grossman, H. Xu, and D.E. Shaw Biomolecular simulation: a computational microscope for molecular biology Annu. Rev. Biophys. 41 2012 429 452
7. P. Kollman Free energy calculations: applications to chemical and biochemical phenomena Chem. Rev. 93 7 1993 2395 2417
8. W.L. Jorgensen Efficient drug lead discovery and optimization Acc. Chem. Res. 42 6 2009 724 733
9. J.D. Chodera, D.L. Mobley, M.R. Shirts, R.W. Dixon, K. Branson, and V.S. Pande Alchemical free energy methods for drug discovery: progress and challenges Curr. Opin. Struct. Biol. 21 2 2011 150 160
10. H.J. Bohm 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. 8 3 1994 243 256
11. D.K. Gehlhaar, G.M. Verkhivker, P.A. Rejto, C.J. Sherman, D.B. Fogel, L.J. Fogel, and S.T. Freer Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming Chem. Biol. 2 5 1995 317 324
12. G. Jones, P. Willett, and R.C. Glen Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation J. Mol. Biol. 245 1 1995 43 53
13. A.N. Jain Scoring noncovalent protein-ligand interactions: a continuous differentiable function tuned to compute binding affinities J. Comput. Aided Mol. Des. 10 5 1996 427 440
14. M.D. Eldridge, C.W. Murray, T.R. Auton, G.V. Paolini, and R.P. Mee 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. 11 5 1997 425 445
15. G. Jones, P. Willett, R.C. Glen, A.R. Leach, and R. Taylor Development and validation of a genetic algorithm for flexible docking J. Mol. Biol. 267 3 1997 727 748
16. H.J. Bohm Prediction of binding constants of protein ligands: a fast method for the prioritization of hits obtained from de novo design or 3D database search programs J. Comput. Aided Mol. Des. 12 4 1998 309 323
17. G.M. Morris, D.S. Goodsell, R.S. Halliday, R. Huey, W.E. Hart, R.K. Belew, and A.J. Olson Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function J. Comput. Chem. 19 14 1998 1639 1662
18. I. Muegge, and Y.C. Martin A general and fast scoring function for protein-ligand interactions: a simplified potential approach J. Med. Chem. 42 5 1999 791 804
19. H. Gohlke, M. Hendlich, and G. Klebe Knowledge-based scoring function to predict protein-ligand interactions J. Mol. Biol. 295 2 2000 337 356
20. T.J. Ewing, S. Makino, A.G. Skillman, and I.D. Kuntz DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases J. Comput. Aided Mol. Des. 15 5 2001 411 428
21. R. Wang, L. Lai, and S. Wang Further development and validation of empirical scoring functions for structure-based binding affinity prediction J. Comput. Aided Mol. Des. 16 1 2002 11 26
22. B.A. Grzybowski, A.V. Ishchenko, J. Shimada, and E.I. Shakhnovich From knowledge-based potentials to combinatorial lead design in silico Acc. Chem. Res. 35 5 2002 261 269
23. R.A. Friesner, J.L. Banks, R.B. Murphy, T.A. Halgren, J.J. Klicic, D.T. Mainz, M.P. Repasky, E.H. Knoll, M. Shelley, J.K. Perry, D.E. Shaw, P. Francis, and P.S. Shenkin Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy J. Med. Chem. 47 7 2004 1739 1749
24. C. Zhang, S. Liu, Q. Zhu, and Y. Zhou A knowledge-based energy function for protein-ligand, protein-protein, and protein-DNA complexes J. Med. Chem. 48 7 2005 2325 2335
25. H.F. Velec, H. Gohlke, and G. Klebe DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction J. Med. Chem. 48 20 2005 6296 6303
26. W.T. Mooij, and M.L. Verdonk General and targeted statistical potentials for protein-ligand interactions Proteins: Struct. Funct. Bioinf. 61 2 2005 272 287
27. A. Krammer, P.D. Kirchhoff, X. Jiang, C.M. Venkatachalam, and M. Waldman LigScore: a novel scoring function for predicting binding affinities J. Mol. Graphics Modell. 23 5 2005 395 407
28. S.Y. Huang, and X. Zou An iterative knowledge-based scoring function to predict protein-ligand interactions: I. Derivation of interaction potentials J. Comput. Chem. 27 15 2006 1866 1875
29. I. Muegge PMF scoring revisited J. Med. Chem. 49 20 2006 5895 5902
30. P. Pfeffer, and H. Gohlke DrugScoreRNA - knowledge-based scoring function to predict RNA-ligand interactions J. Chem. Inf. Model. 47 5 2007 1868 1876
31. C.A. Sotriffer, P. Sanschagrin, H. Matter, and G. Klebe SFCscore: scoring functions for affinity prediction of protein-ligand complexes Proteins: Struct. Funct. Bioinf. 73 2 2008 395 419
32. S. Raub, A. Steffen, A. Kamper, and C.M. Marian AIScore chemically diverse empirical scoring function employing quantum chemical binding energies of hydrogen-bonded complexes J. Chem. Inf. Model. 48 7 2008 1492 1510
33. I. Reulecke, G. Lange, J. Albrecht, R. Klein, and M. Rarey Towards an integrated description of hydrogen bonding and dehydration: decreasing false positives in virtual screening with the HYDE scoring function ChemMedChem 3 2008 885 897 (1870-7187 (electronic))
34. O.V. Stroganov, F.N. Novikov, V.S. Stroylov, V. Kulkov, and G.G. Chilov Lead finder: an approach to improve accuracy of protein-ligand docking, binding energy estimation, and virtual screening J. Chem. Inf. Model. 48 12 2008 2371 2385
35. T. Cheng, X. Li, Y. Li, Z. Liu, and R. Wang Comparative assessment of scoring functions on a diverse test set J. Chem. Inf. Model. 49 4 2009 1079 1093
36. S.Y. Huang, and X. Zou Inclusion of solvation and entropy in the knowledge-based scoring function for protein-ligand interactions J. Chem. Inf. Model. 50 2 2010 262 273
37. M. Xue, M. Zheng, B. Xiong, Y. Li, H. Jiang, and J. Shen Knowledge-based scoring functions in drug design. 1. Developing a target-specific method for kinase-ligand interactions J. Chem. Inf. Model. 50 8 2010 1378 1386
38. G. Neudert, and G. Klebe DSX: a knowledge-based scoring function for the assessment of protein-ligand complexes J. Chem. Inf. Model. 51 10 2011 2731 2745
39. Y.T. Tang, and G.R. Marshall PHOENIX: a scoring function for affinity prediction derived using high-resolution crystal structures and calorimetry measurements J. Chem. Inf. Model. 51 2 2011 214 228
40. Q. Shen, B. Xiong, M. Zheng, X. Luo, C. Luo, X. Liu, Y. Du, J. Li, W. Zhu, J. Shen, and H. Jiang Knowledge-based scoring functions in drug design: 2. Can the knowledge base be enriched? J. Chem. Inf. Model. 51 2 2011 386 397
41. Z. Zheng, and K.M. Merz Ligand Identification Scoring Algorithm (LISA) J. Chem. Inf. Model. 51 6 2011 1296 1306
42. J.D. Durrant, and J.A. McCammon NNScore 2.0: a neural-network receptor-ligand scoring function J. Chem. Inf. Model. 51 11 2011 2897 2903
43. L. Li, B. Wang, and S.O. Meroueh Support vector regression scoring of receptor-ligand complexes for rank-ordering and virtual screening of chemical libraries J. Chem. Inf. Model. 51 9 2011 2132 2138
44. A. Hamza, N.-N. Wei, and C.-G. Zhan Ligand-based virtual screening approach using a new scoring function J. Chem. Inf. Model 52 4 2012 963 974
45. G.L. Warren, C.W. Andrews, A.-M. Capelli, B. Clarke, J. LaLonde, M.H. Lambert, M. Lindvall, N. Nevins, S.F. Semus, S. Senger, G. Tedesco, I.D. Wall, J.M. Woolven, C.E. Peishoff, and M.S. Head A critical assessment of docking programs and scoring functions J. Med. Chem. 49 20 2006 5912 5931
46. R.D. Smith, J.B. Dunbar, P.M.-U. Ung, E.X. Esposito, C.-Y. Yang, S. Wang, and H.A. Carlson CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions J. Chem. Inf. Model. 51 9 2011 2115 2131
47. S.A. Hayik, R. Dunbrack, and K.M. Merz Mixed quantum mechanics/molecular mechanics scoring function to predict protein-ligand binding affinity J. Chem. Theory Comput. 6 10 2010 3079 3091
48. A. Ishchenko, Z. Liu, P. Lindblom, G. Wu, K.-C. Jim, R.D. Gregg, D.A. Claremon, and S.B. Singh Structure-based design technology contour and its application to the design of renin inhibitors J. Chem. Inf. Model. 52 8 2012 2089 2097
49. F. Allen The Cambridge structural database: a quarter of a million crystal structures and rising Acta Crystallogr. Sect. B: Struct. Sci. 58 3 Part 1 2002 380 388
50. R. Wang, X. Fang, Y. Lu, and S. Wang The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures J. Med. Chem. 47 12 2004 2977 2980
51. Schrödinger Suite 2006 Protein Preparation Wizard, Schrödinger, LLC, New York, NY.
52. Schrödinger Maestro Graphical User Interface, Maestro Version 2006, Schrödinger, LLC, New York, NY.
53. Z. Liu, P. Lindblom, D.A. Claremon, and S.B. Singh Structure-based design technology CONTOUR and its application to drug discovery Innovations in Biomolecular Modeling and Simulations vol. 2 2012 The Royal Society of Chemistry Cambridge 265 280 (Chapter 11)
54. Z. Xu, S. Cacatian, J. Yuan, R.D. Simpson, L. Jia, W. Zhao, C.M. Tice, P.T. Flaherty, J. Guo, A. Ishchenko, S.B. Singh, Z. Wu, B.M. McKeever, B.B. Scott, Y. Bukhtiyarov, J. Berbaum, J. Mason, R. Panemangalore, M.G. Cappiello, R. Bentley, C.P. Doe, R.K. Harrison, G.M. McGeehan, L.W. Dillard, J.J. Baldwin, and D.A. Claremon Optimization of orally bioavailable alkyl amine renin inhibitors Bioorg. Med. Chem. Lett. 20 2 2009 694 699
55. C.M. Tice, Z. Xu, J. Yuan, R.D. Simpson, S.T. Cacatian, P.T. Flaherty, W. Zhao, J. Guo, A. Ishchenko, S.B. Singh, Z. Wu, B.B. Scott, Y. Bukhtiyarov, J. Berbaum, J. Mason, R. Panemangalore, M.G. Cappiello, D. Muller, R.K. Harrison, G.M. McGeehan, L.W. Dillard, J.J. Baldwin, and D.A. Claremon Design and optimization of renin inhibitors: orally bioavailable alkyl amines Bioorg. Med. Chem. Lett. 19 13 2009 3541 3545
56. C.M. Tice, W. Zhao, P.M. Krosky, B.A. Kruk, J. Berbaum, J.A. Johnson, Y. Bukhtiyarov, R. Panemangalore, B.B. Scott, Y. Zhao, J.G. Bruno, L. Howard, J. Togias, Y.J. Ye, S.B. Singh, B.M. McKeever, P.R. Lindblom, J. Guo, R. Guo, H. Nar, A. Schuler-Metz, R.E. Gregg, K. Leftheris, R.K. Harrison, G.M. McGeehan, L. Zhuang, and D.A. Claremon Discovery and optimization of adamantyl carbamate inhibitors of 11beta-HSD1 Bioorg. Med. Chem. Lett. 20 22 2010 6725 6729
57. Z. Xu, C.M. Tice, W. Zhao, S. Cacatian, Y.J. Ye, S.B. Singh, P. Lindblom, B.M. McKeever, P.M. Krosky, B.A. Kruk, J. Berbaum, R.K. Harrison, J.A. Johnson, Y. Bukhtiyarov, R. Panemangalore, B.B. Scott, Y. Zhao, J.G. Bruno, J. Togias, J. Guo, R. Guo, P.J. Carroll, G.M. McGeehan, L. Zhuang, W. He, and D.A. Claremon Structure-based design and synthesis of 1,3-oxazinan-2-one inhibitors of 11beta-hydroxysteroid dehydrogenase type 1 J. Med. Chem. 54 17 2011 6050 6062
58. L. Jia, R.D. Simpson, J. Yuan, Z. Xu, W. Zhao, S. Cacatian, C.M. Tice, J. Guo, A. Ishchenko, S.B. Singh, Z. Wu, B.M. McKeever, Y. Bukhtiyarov, J.A. Johnson, C.P. Doe, R.K. Harrison, G.M. McGeehan, L.W. Dillard, J.J. Baldwin, and D.A. Claremon Discovery of VTP-27999, an alkyl amine renin inhibitor with potential for clinical utility ACS Med. Chem. Lett. 2 10 2011 747 751
59. J. Yuan, R.D. Simpson, W. Zhao, C.M. Tice, Z. Xu, S. Cacatian, L. Jia, P.T. Flaherty, J. Guo, A. Ishchenko, Z. Wu, B.M. McKeever, B.B. Scott, Y. Bukhtiyarov, J. Berbaum, R. Panemangalore, R. Bentley, C.P. Doe, R.K. Harrison, G.M. McGeehan, S.B. Singh, L.W. Dillard, J.J. Baldwin, and D.A. Claremon Biphenyl/diphenyl ether renin inhibitors: filling the S1 pocket of renin via the S3 pocket Bioorg. Med. Chem. Lett. 21 16 2011 4836 4843
60. Prime Version 3.1 2012 Schrödinger, LLC New York, NY
61. M. Congreve, D. Aharony, J. Albert, O. Callaghan, J. Campbell, R.A. Carr, G. Chessari, S. Cowan, P.D. Edwards, M. Frederickson, R. McMenamin, C.W. Murray, S. Patel, and N. Wallis Application of fragment screening by X-ray crystallography to the discovery of aminopyridines as inhibitors of beta-secretase J. Med. Chem. 50 6 2007 1124 1132
62. Discovery Studio Discovery Studio Version 3.5.0.12158 2012 Accelrys, Inc. San Diego, CA
63. W.D. Cornell, P. Cieplak, C.I. Bayly, I.R. Gould, K.M. Merz Jr., D.M. Ferguson, D.C. Spellmeyer, T. Fox, J.W. Caldwell, and P.A. Kollman A second generation force field for the simulation of proteins, nucleic acids, and organic molecules J. Am. Chem. Soc. 117 1995 5179 5197