Can the Energy Gap in the Protein-Ligand Binding Energy Landscape Be Used as a Descriptor in Virtual Ligand Screening?

PLoS ONE

Volumn 7, Issue 10, 2012, Pages

  Grigoryan, Arsen V ^a   Wang, Hong ^a   Cardozo, Timothy J ^a  

^a NEW YORK UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CLUSTER ANALYSIS; ENERGY COST; HUMAN; LIGAND BINDING; NONHUMAN; NUCLEOTIDE SEQUENCE; PROTEIN ANALYSIS; PROTEIN BINDING; PROTEIN CONFORMATION; PROTEIN DOMAIN; PROTEIN TARGETING; RECEPTOR BINDING; STRUCTURAL HOMOLOGY; VIRTUAL LIGAND SCREENING;

LIGANDS; MOLECULAR DOCKING SIMULATION; PROTEIN CONFORMATION; PROTEINS;

EID: 84867365195     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0046532     Document Type: Article

References (61)

1. McCammon JA, (1998) Theory of biomolecular recognition. Curr Opin Struct Biol 8: 245-249.
2. Muller-Dethlefs K, Hobza P, (2000) Noncovalent interactions: a challenge for experiment and theory. Chem Rev 100: 143-168.
3. Verkhivker GM, Bouzida D, Gehlhaar DK, Rejto PA, Freer ST, et al. (2002) Complexity and simplicity of ligand-macromolecule interactions: the energy landscape perspective. Curr Opin Struct Biol 12: 197-203.
4. Abagyan R, Totrov M, (2001) High-throughput docking for lead generation. Curr Opin Chem Biol 5: 375-382.
5. Abagyan RA, (1993) Towards protein folding by global energy optimization. FEBS Lett 325: 17-22.
6. Frauenfelder H, Sligar SG, Wolynes PG, (1991) The energy landscapes and motions of proteins. Science 254: 1598-1603.
7. Wolynes PG, Onuchic JN, Thirumalai D, (1995) Navigating the folding routes. Science 267: 1619-1620.
8. Dill KA, Chan HS, (1997) From Levinthal to pathways to funnels. Nat Struct Biol 4: 10-19.
9. Onuchic JN, Nymeyer H, Garcia AE, Chahine J, Socci ND, (2000) The energy landscape theory of protein folding: insights into folding mechanisms and scenarios. Adv Protein Chem 53: 87-152.
10. Mirny L, Shakhnovich E, (2001) Protein folding theory: From lattice to all-atom models. Annual Review of Biophysics and Biomolecular Structure 30: 361-396.
11. Schug A, Onuchic JN, (2010) From protein folding to protein function and biomolecular binding by energy landscape theory. Curr Opin Pharmacol 10: 709-714.
12. Chen Y, Ding F, Nie H, Serohijos AW, Sharma S, et al. (2008) Protein folding: then and now. Arch Biochem Biophys 469: 4-19.
13. Dokholyan NV, Buldyrev SV, Stanley HE, Shakhnovich EI, (2000) Identifying the protein folding nucleus using molecular dynamics. J Mol Biol 296: 1183-1188.
14. Papoian GA, Wolynes PG, (2003) The physics and bioinformatics of binding and folding-an energy landscape perspective. Biopolymers 68: 333-349.
15. Zhuravlev PI, Papoian GA, (2010) Functional versus folding landscapes: the same yet different. Curr Opin Struct Biol 20: 16-22.
16. Potoyan DA, Papoian GA, (2011) Energy landscape analyses of disordered histone tails reveal special organization of their conformational dynamics. J Am Chem Soc 133: 7405-7415.
17. Zhuravlev PI, Materese CK, Papoian GA, (2009) Deconstructing the native state: energy landscapes, function, and dynamics of globular proteins. J Phys Chem B 113: 8800-8812.
18. Rejto PA, Verkhivker GM, (1996) Unraveling principles of lead discovery: from unfrustrated energy landscapes to novel molecular anchors. Proc Natl Acad Sci U S A 93: 8945-8950.
19. Tsai CJ, Xu D, Nussinov R, (1998) Protein folding via binding and vice versa. Fold Des 3: R71-80.
20. Verkhivker GM, Rejto PA, Bouzida D, Arthurs S, Colson AB, et al. (1999) Towards understanding the mechanisms of molecular recognition by computer simulations of ligand-protein interactions. J Mol Recognit 12: 371-389.
21. Wang J, Verkhivker GM, (2003) Energy landscape theory, funnels, specificity, and optimal criterion of biomolecular binding. Phys Rev Lett 90: 188101.
22. Wang J, Zheng X, Yang Y, Drueckhammer D, Yang W, et al. (2007) Quantifying intrinsic specificity: a potential complement to affinity in drug screening. Phys Rev Lett 99: 198101.
23. Janin J, (1996) Quantifying biological specificity: the statistical mechanics of molecular recognition. Proteins 25: 438-445.
24. Tsai CJ, Kumar S, Ma B, Nussinov R, (1999) Folding funnels, binding funnels, and protein function. Protein Sci 8: 1181-1190.
25. Tovchigrechko A, Vakser IA, (2001) How common is the funnel-like energy landscape in protein-protein interactions? Protein Sci 10: 1572-1583.
26. Kallblad P, Mancera RL, Todorov NP, (2004) Assessment of multiple binding modes in ligand-protein docking. J Med Chem 47: 3334-3337.
27. Gorelik B, Goldblum A, (2008) High quality binding modes in docking ligands to proteins. Proteins 71: 1373-1386.
28. Ruvinsky AM, Kozintsev AV, (2005) New and fast statistical-thermodynamic method for computation of protein-ligand binding entropy substantially improves docking accuracy. Journal of Computational Chemistry 26: 1089-1095.
29. Wei D, Zheng H, Su N, Deng M, Lai L, (2010) Binding energy landscape analysis helps to discriminate true hits from high-scoring decoys in virtual screening. J Chem Inf Model 50: 1855-1864.
30. Shan Y, Kim ET, Eastwood MP, Dror RO, Seeliger MA, et al. (2011) How does a drug molecule find its target binding site? J Am Chem Soc 133: 9181-9183.
31. Mobley DL, Dill KA, (2009) Binding of small-molecule ligands to proteins: "what you see" is not always "what you get". Structure 17: 489-498.
32. Good AC, Oprea TI, (2008) Optimization of CAMD techniques 3. Virtual screening enrichment studies: a help or hindrance in tool selection? J Comput Aided Mol Des 22: 169-178.
33. Huang N, Shoichet BK, Irwin JJ, (2006) Benchmarking sets for molecular docking. J Med Chem 49: 6789-6801.
34. Bottegoni G, Rocchia W, Rueda M, Abagyan R, Cavalli A, (2011) Systematic exploitation of multiple receptor conformations for virtual ligand screening. PLoS One 6: e18845.
35. Wang H, Blais J, Ron D, Cardozo T, (2010) Structural Determinants of PERK Inhibitor Potency and Selectivity. Chem Biol Drug Des 76: 480-495.
36. Abagyan R, Totrov M, Kuznetsov D, (1994) Icm- a New Method for Protein Modeling and Design- Applications to Docking and Structure Prediction from the Distorted Native Conformation. Journal of Computational Chemistry 15: 488-506.
37. Abagyan R, Totrov M, (1994) Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins. J Mol Biol 235: 983-1002.
38. Abagyan RA, Totrov M, (1999) Ab initio folding of peptides by the optimal-bias Monte Carlo minimization procedure. Journal of Computational Physics 151: 402-421.
39. Abagyan R, Kufareva I, (2009) The flexible pocketome engine for structural chemogenomics. Methods Mol Biol 575: 249-279.
40. Kufareva I, Ilatovskiy AV, Abagyan R, (2012) Pocketome: an encyclopedia of small-molecule binding sites in 4D. Nucleic Acids Res 40: D535-D540.
41. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, et al. (2000) The Protein Data Bank. Nucl Acids Res 28: 235-242.
42. Padyana AK, Qiu H, Roll-Mecak A, Hinnebusch AG, Burley SK, (2005) Structural basis for autoinhibition and mutational activation of eukaryotic initiation factor 2alpha protein kinase GCN2. J Biol Chem 280: 29289-29299.
43. Halgren TA, (1996) Merck molecular force field.1. Basis, form, scope, parameterization, and performance of MMFF94. Journal of Computational Chemistry 17: 490-519.
44. Totrov M, Abagyan R. Derivation of Sensitive Discrimination Potential for Virtual Ligand Screening. In: Istrail S, Pevzner P, Waterman M, editors. RECOMB 99: Proceedings of the third annual international conference on Computational molecular biology; 1999; Lyon, France. ACM Press, New York. 312-320.
45. Hanley JA, McNeil BJ, (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143: 29-36.
46. Abramowitz M, Stegun IA (1972) Handbook of mathematical functions with formulas, graphs, and mathematical tables. New York: Dover Publications.
47. Rueda M, Bottegoni G, Abagyan R, (2010) Recipes for the selection of experimental protein conformations for virtual screening. J Chem Inf Model 50: 186-193.
48. Damm KL, Carlson HA, (2007) Exploring experimental sources of multiple protein conformations in structure-based drug design. J Am Chem Soc 129: 8225-8235.
49. McCammon JA, (2005) Target flexibility in molecular recognition. Biochim Biophys Acta 1754: 221-224.
50. Teague SJ, (2003) Implications of protein flexibility for drug discovery. Nat Rev Drug Discov 2: 527-541.
51. Totrov M, Abagyan R, (2008) Flexible ligand docking to multiple receptor conformations: a practical alternative. Curr Opin Struct Biol 18: 178-184.
52. Rueda M, Bottegoni G, Abagyan R, (2009) Consistent improvement of cross-docking results using binding site ensembles generated with elastic network normal modes. J Chem Inf Model 49: 716-725.
53. Huang SY, Zou X, (2007) Ensemble docking of multiple protein structures: considering protein structural variations in molecular docking. Proteins 66: 399-421.
54. Teodoro ML, Kavraki LE, (2003) Conformational flexibility models for the receptor in structure based drug design. Curr Pharm Des 9: 1635-1648.
55. Knegtel RM, Kuntz ID, Oshiro CM, (1997) Molecular docking to ensembles of protein structures. J Mol Biol 266: 424-440.
56. Barril X, Morley SD, (2005) Unveiling the full potential of flexible receptor docking using multiple crystallographic structures. J Med Chem 48: 4432-4443.
57. Kokh DB, Wade RC, Wenzel W, (2011) Receptor flexibility in small-molecule docking calculations. WIREs Computational Molecular Science 1: 298-314.
58. Ding F, Yin S, Dokholyan NV, (2010) Rapid flexible docking using a stochastic rotamer library of ligands. J Chem Inf Model 50: 1623-1632.
59. Davis IW, Baker D, (2009) RosettaLigand docking with full ligand and receptor flexibility. J Mol Biol 385: 381-392.
60. Truchon JF, Bayly CI, (2007) Evaluating virtual screening methods: good and bad metrics for the "early recognition" problem. J Chem Inf Model 47: 488-508.
61. Neves MA, Totrov M, Abagyan R, (2012) Docking and scoring with ICM: the benchmarking results and strategies for improvement. J Comput Aided Mol Des 26: 675-686.