Druggable pockets and binding site centric chemical space: A paradigm shift in drug discovery

Drug Discovery Today

Volumn 15, Issue 15-16, 2010, Pages 656-667

  Pérot, Stéphanie ^a   Sperandio, Olivier ^a,b   Miteva, Maria A ^a   Camproux, Anne Claude ^a   Villoutreix, Bruno O ^a,b  

^a INSERM   (France)

^b IGM   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CELECOXIB; CYCLOOXYGENASE 2; FATTY ACID BINDING PROTEIN; GEMCITABINE; LIGAND; NUCLEOTIDE; SIALIDASE;

CHEMICAL ANALYSIS; DRUG BINDING SITE; DRUG DEVELOPMENT; GEOMETRY; LIGAND BINDING; MOLECULAR DOCKING; PREDICTION; PROTEIN ANALYSIS; PROTEIN FUNCTION; PROTEIN STRUCTURE; REVIEW;

BINDING SITES; DRUG DELIVERY SYSTEMS; DRUG DESIGN; DRUG DISCOVERY; LIGANDS; PHARMACEUTICAL PREPARATIONS; PROTEIN BINDING; SMALL MOLECULE LIBRARIES;

EID: 77955517509     PISSN: 13596446     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.drudis.2010.05.015     Document Type: Review

References (123)

1. Weigelt J., et al. Structural genomics and drug discovery: all in the family. Curr. Opin. Chem. Biol. 2008, 12:32-39.
2. Fedorov O., et al. Insights for the development of specific kinase inhibitors by targeted structural genomics. Drug Discov. Today 2007, 12:365-372.
3. Kolb P., et al. Docking and chemoinformatic screens for new ligands and targets. Curr. Opin. Biotechnol. 2009, 20:429-436.
4. Cavasotto C.N., Phatak S.S. Homology modeling in drug discovery: current trends and applications. Drug Discov. Today 2009, 14:676-683.
5. Vajda S., Guarnieri F. Characterization of protein-ligand interaction sites using experimental and computational methods. Curr. Opin. Drug Discov. Devel. 2006, 9:354-362.
6. An J., et al. Comprehensive identification of 'druggable' protein ligand binding sites. Genome Inform. 2004, 15:31-41.
7. Hunter W.N. Structure-based ligand design and the promise held for antiprotozoan drug discovery. J. Biol. Chem. 2009, 284:11749-11753.
8. Keller T.H., et al. A practical view of 'druggability'. Curr. Opin. Chem. Biol. 2006, 10:357-361.
9. Macchiarulo A., Pellicciari R. Exploring the other side of biologically relevant chemical space: insights into carboxylic, sulfonic and phosphonic acid bioisosteric relationships. J. Mol. Graph. Model. 2007, 26:728-739.
10. Kellenberger E., et al. How to measure the similarity between protein ligand-binding sites?. Curr. Comput. Aided Drug Design 2008, 4:209-220.
11. Andersson C.D., et al. Mapping of ligand-binding cavities in proteins. Proteins 2009, 78:1408-1422.
12. Chalk A.J., et al. PDBLIG: classification of small molecular protein binding in the Protein Data Bank. J. Med. Chem. 2004, 47:3807-3816.
13. Fuller J.C., et al. Predicting druggable binding sites at the protein-protein interface. Drug Discov. Today 2009, 14:155-161.
14. Laurie A.T.R., Jackson R.M. Methods for the prediction of protein-ligand binding sites for structure-based drug design and virtual ligand screening. Curr. Protein Pept. Sci. 2006, 7:395-406.
15. Abagyan R., Kufareva I. The flexible pocketome engine for structural chemogenomics. Methods Mol. Biol. 2009, 575:249-279.
16. Kahraman A., et al. Shape variation in protein binding pockets and their ligands. J. Mol. Biol. 2007, 368:283-301.
17. Gunasekaran K., Nussinov R. How different are structurally flexible and rigid binding sites? Sequence and structural features discriminating proteins that do and do not undergo conformational change upon ligand binding. J. Mol. Biol. 2007, 365:257-273.
18. Weisel M., et al. Architectural repertoire of ligand-binding pockets on protein surfaces. ChemBioChem 2010, 11:556-563.
19. Campagna-Slater V., et al. Pharmacophore screening of the Protein Data Bank for specific binding site chemistry. J. Chem. Inf. Model 2010, 50:358-367.
20. Segers K., et al. Design of protein membrane interaction inhibitors by virtual ligand screening, proof of concept with the C2 domain of factor V. Proc. Natl. Acad. Sci. U.S.A. 2007, 104:12697-12702.
21. Wells J.A., McClendon C.L. Reaching for high-hanging fruit in drug discovery at protein-protein interfaces. Nature 2007, 450:1001-1009.
22. Villoutreix B.O., et al. In silico-in vitro screening of protein-protein interactions: towards the next generation of therapeutics. Curr. Pharm. Biotechnol. 2008, 9:103-122.
23. Higueruelo A.P., et al. Atomic interactions and profile of small molecules disrupting protein-protein interfaces: the TIMBAL database. Chem. Biol. Drug Des. 2009, 74:457-467.
24. Dahlback B., Villoutreix B.O. Molecular recognition in the protein C anticoagulant pathway. J. Thromb. Haemost. 2003, 1:1525-1534.
25. Weisel M., et al. Form follows function: shape analysis of protein cavities for receptor-based drug design. Proteomics 2009, 9:451-459.
26. Hartshorn M.J., et al. Diverse, high-quality test set for the validation of protein-ligand docking performance. J. Med. Chem. 2007, 50:726-741.
27. Liang J., et al. Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design. Protein Sci. 1998, 7:1884-1897.
28. Nayal M., Honig B. On the nature of cavities on protein surfaces: application to the identification of drug-binding sites. Proteins 2006, 63:892-906.
29. Laskowski R.A., et al. Protein clefts in molecular recognition and function. Protein Sci. 1996, 5:2438-2452.
30. Sonavane S., Chakrabarti P. Cavities and atomic packing in protein structures and interfaces. PLOS Comput. Biol. 2008, 4:e1000188.
31. Hajduk P.J., et al. Druggability indices for protein targets derived from NMR-based screening data. J. Med. Chem. 2005, 48:2518-2525.
32. An J., et al. Pocketome via comprehensive identification and classification of ligand binding envelopes. Mol. Cell. Proteomics 2005, 4:752-761.
33. Hu L., et al. Binding MOAD (Mother Of All Databases). Proteins 2005, 60:333-340.
34. Carlson H.A., et al. Differences between high- and low-affinity complexes of enzymes and nonenzymes. J. Med. Chem. 2008, 51:6432-6441.
35. Schulz-Gasch T., Stahl M. Binding site characteristics in structure-based virtual screening: evaluation of current docking tools. J. Mol. Model. 2003, 9:47-57.
36. Miteva M.A., et al. Fast structure-based virtual ligand screening combining FRED, DOCK, and Surflex. J. Med. Chem. 2005, 48:6012-6022.
37. Laskowski R.A. SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions. J. Mol. Graph. 1995, 13:323-330.
38. Hendlich M., et al. LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins. J. Mol. Graph. Model. 1997, 15:359-363.
39. Kalidas Y., Chandra N. PocketDepth: a new depth based algorithm for identification of ligand binding sites in proteins. J. Struct. Biol. 2008, 161:31-42.
40. Weisel M., et al. PocketPicker: analysis of ligand binding-sites with shape descriptors. Chem. Cent. J. 2007, 1:7.
41. Goodford P.J. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem. 1985, 28:849-857.
42. Laurie A.T.R., Jackson R.M. Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites. Bioinformatics 2005, 21:1908-1916.
43. Harris R., et al. Automated prediction of ligand-binding sites in proteins. Proteins 2008, 70:1506-1517.
44. Ruppert J., et al. Automatic identification and representation of protein binding sites for molecular docking. Protein Sci. 1997, 6:524-533.
45. Kortvelyesi T., et al. Algorithms for computational solvent mapping of proteins. Proteins 2003, 51:340-351.
46. Landon M.R., et al. Identification of hot spots within druggable binding regions by computational solvent mapping of proteins. J. Med. Chem. 2007, 50:1231-1240.
47. Soga S., et al. Use of amino acid composition to predict ligand-binding sites. J. Chem. Inf. Model. 2007, 47:400-406.
48. Halgren T. New method for fast and accurate binding-site identification and analysis. Chem. Biol. Drug Des. 2007, 69:146-148.
49. Cheng A.C., et al. Structure-based maximal affinity model predicts small-molecule druggability. Nat. Biotechnol. 2007, 25:71-75.
50. Seco J., et al. Binding site detection and druggability index from first principles. J. Med. Chem. 2009, 52:2363-2371.
51. Weber A., et al. Unexpected nanomolar inhibition of carbonic anhydrase by COX-2-selective celecoxib: new pharmacological opportunities due to related binding site recognition. J. Med. Chem. 2004, 47:550-557.
52. Schmitt S., et al. A new method to detect related function among proteins independent of sequence and fold homology. J. Mol. Biol. 2002, 323:387-406.
53. Keiser M.J., et al. Predicting new molecular targets for known drugs. Nature 2009, 462:175-181.
54. Renner S., et al. Bioactivity-guided mapping and navigation of chemical space. Nat. Chem. Biol. 2009, 5:585-592.
55. Campillos M., et al. Drug target identification using side-effect similarity. Science 2008, 321:263-266.
56. Gupta A., et al. Structural models in the assessment of protein druggability based on HTS data. J. Comput. Aided Mol. Des. 2009, 23:583-592.
57. Wlodarski T., Zagrovic B. Conformational selection and induced fit mechanism underlie specificity in noncovalent interactions with ubiquitin. Proc. Natl. Acad. Sci. U.S.A. 2009, 106:19346-19351.
58. Luque I., Freire E. Structural stability of binding sites: consequences for binding affinity and allosteric effects. Proteins 2000, (Suppl. 4):63-71.
59. Cozzini P., et al. Target flexibility: an emerging consideration in drug discovery and design. J. Med. Chem. 2008, 51:6237-6255.
60. McCammon J.A. Target flexibility in molecular recognition. Biochim. Biophys. Acta 2005, 1754:221-224.
61. Eyrisch S., Helms V. Transient pockets on protein surfaces involved in protein-protein interaction. J. Med. Chem. 2007, 50:3457-3464.
62. Withers I.M., et al. Active site pressurization: a new tool for structure-guided drug design and other studies of protein flexibility. J. Chem. Inf. Model. 2008, 48:1448-1454.
63. Bottegoni G., et al. A new method for ligand docking to flexible receptors by dual alanine scanning and refinement (SCARE). J. Comput. Aided Mol. Des. 2008, 22:311-325.
64. Ekonomiuk D., et al. Flaviviral protease inhibitors identified by fragment-based library docking into a structure generated by molecular dynamics. J. Med. Chem. 2009, 52:4860-4868.
65. Bolstad E.S., Anderson A.C. In pursuit of virtual lead optimization: the role of the receptor structure and ensembles in accurate docking. Proteins 2008, 73:566-580.
66. Rueda M., et al. Recipes for the selection of experimental protein conformations for virtual screening. J. Chem. Inf. Model. 2010, 50:186-193.
67. Sperandio, O. et al. How to choose relevant multiple receptor conformations for virtual screening: a test case of Cdk2 and normal mode analysis. Eur. Biophys. J. (in press).
68. Teramoto R., Fukunishi H. Consensus scoring with feature selection for structure-based virtual screening. J. Chem. Inf. Model. 2008, 48:288-295.
69. Feher M. Consensus scoring for protein-ligand interactions. Drug Discov. Today 2006, 11:421-428.
70. Seifert M.H., et al. Virtual high-throughput screening of molecular databases. Curr. Opin. Drug Discov. Devel. 2007, 10:298-307.
71. Seifert M.H. Targeted scoring functions for virtual screening. Drug Discov. Today 2009, 14:562-569.
72. Orry A.J., et al. Structure-based development of target-specific compound libraries. Drug Discov. Today 2006, 11:261-266.
73. Cuff A.L., et al. The CATH classification revisited-architectures reviewed and new ways to characterize structural divergence in superfamilies. Nucleic Acids Res. 2009, 37(Database issue):D310-D314.
74. Akritopoulou-Zanze I., et al. Topography-biased compound library design: the shape of things to come?. Drug Discov. Today 2007, 12:948-952.
75. Sauer W.H., Schwarz M.K. Molecular shape diversity of combinatorial libraries: a prerequisite for broad bioactivity. J. Chem. Inf. Comput. Sci. 2003, 43:987-1003.
76. Weskamp N., et al. Merging chemical and biological space: structural mapping of enzyme binding pocket space. Proteins 2009, 76:317-330.
77. Gold N.D., Jackson R.M. Fold independent structural comparisons of protein-ligand binding sites for exploring functional relationships. J. Mol. Biol. 2006, 355:1112-1124.
78. Macchiarulo A., et al. Charting the chemical space of target sites: insights into the binding modes of amine and amidine groups. J. Chem. Inf. Model. 2009, 49:900-912.
79. Wang R., et al. The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. J. Med. Chem. 2004, 47:2977-2980.
80. Halgren T.A. Identifying and characterizing binding sites and assessing druggability. J. Chem. Inf. Model. 2009, 49:377-389.
81. Pettit F.K., Bowie J.U. Protein surface roughness and small molecular binding sites. J. Mol. Biol. 1999, 285:1377-1382.
82. Kinnings S.L., Jackson R.M. Binding site similarity analysis for the functional classification of the protein kinase family. J. Chem. Inf. Model. 2009, 42:318-329.
83. Jain A.N. Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J. Med. Chem. 2003, 46:499-511.
84. Pettersen E.F., et al. UCSF Chimera-a visualization system for exploratory research and analysis. J. Comput. Chem. 2004, 25:1605-1612.
85. Lagorce D., et al. FAF-Drugs2: free ADME/tox filtering tool to assist drug discovery and chemical biology projects. BMC Bioinformatics 2008, 9:396.
86. Murzin A.G., et al. SCOP: a structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol. 1995, 247:536-540.
87. Huang B., Schroeder M. LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation. BMC Struct. Biol. 2006, 6:19.
88. Glaser F., et al. A method for localizing ligand binding pockets in protein structures. Proteins 2006, 62:479-488.
89. Peters K.P., et al. The automatic search for ligand binding sites in proteins of known three-dimensional structure using only geometric criteria. J. Mol. Biol. 1996, 256:201-213.
90. Zhong S., MacKerell A.D. Binding response: a descriptor for selecting ligand binding site on protein surfaces. J. Chem. Inf. Model. 2007, 47:2303-2315.
91. Binkowski T.A., et al. CASTp: computed atlas of surface topography of proteins. Nucleic Acids Res. 2003, 31:3352-3355.
92. Petrek M., et al. CAVER: a new tool to explore routes from protein clefts, pockets and cavities. BMC Bioinformatics 2006, 7:316.
93. Le Guilloux V., et al. Fpocket: an open source platform for ligand pocket detection. BMC Bioinformatics 2009, 10:168.
94. Kawabata T., Go N. Detection of pockets on protein surfaces using small and large probe spheres to find putative ligand binding sites. Proteins 2007, 68:516-529.
95. Till M.S., Ullmann G. McVol-a program for calculating protein volumes and identifying cavities by a Monte Carlo algorithm. J. Mol. Model. 2009, 16:419-429.
96. Brady G.P., Stouten P.F. Fast prediction and visualization of protein binding pockets with PASS. J. Comput. Aided Mol. Des. 2000, 14:383-401.
97. Levitt D.G., Banaszak L.J. POCKET: a computer graphics method for identifying and displaying protein cavities and their surrounding amino acids. J. Mol. Graph. 1992, 10:229-234.
98. Tseng Y.Y., et al. SplitPocket: identification of protein functional surfaces and characterization of their spatial patterns. Nucleic Acids Res. 2009, 37(Web Server issue):W384-389.
99. Tseng Y.Y., Li W.-H. Identification of protein functional surfaces by the concept of a split pocket. Proteins 2009, 76:959-976.
100. Coleman R.G., Sharp K.A. Travel depth, a new shape descriptor for macromolecules: application to ligand binding. J. Mol. Biol. 2006, 362:441-458.
101. Tripathi A., Kellogg G.E. A novel and efficient tool for locating and characterizing protein cavities and binding sites. Proteins 2010, 78:825-842.
102. Kleywegt G.J., Jones T.A. Detection, delineation, measurement and display of cavities in macromolecular structures. Acta Crystallogr. D: Biol. Cryst. 1994, 50:178-185.
103. Xie L., Bourne P.E. A robust and efficient algorithm for the shape description of protein structures and its application in predicting ligand binding sites. BMC Bioinformatics 2007, 8(Suppl. 4):S9.
104. Ghersi D., Sanchez R. Improving accuracy and efficiency of blind protein-ligand docking by focusing on predicted binding sites. Proteins 2009, 74:417-424.
105. Chang D.T.-H., et al. MEDock: a web server for efficient prediction of ligand binding sites based on a novel optimization algorithm. Nucleic Acids Res. 2005, 33(Web Server issue):W233-238.
106. Powers R., et al. Comparison of protein active site structures for functional annotation of proteins and drug design. Proteins 2006, 65:124-135.
107. Milik M., et al. Common structural cliques: a tool for protein structure and function analysis. Protein Eng. 2003, 16:543-552.
108. Kinoshita K., et al. Identification of protein functions from a molecular surface database, eF-site. J. Struct. Funct. Genom. 2002, 2:9-22.
109. Brylinski M., Skolnick J. A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation. Proc. Natl. Acad. Sci. U.S.A. 2008, 105:129-134.
110. Najmanovich R., et al. Detection of 3D atomic similarities and their use in the discrimination of small molecule protein-binding sites. Bioinformatics 2008, 24:i105-i111.
111. Shulman-Peleg A., et al. MultiBind and MAPPIS: webservers for multiple alignment of protein 3D-binding sites and their interactions. Nucleic Acids Res. 2008, 36(Web Server issue):W260-264.
112. Park K., Kim D. Binding similarity network of ligand. Proteins 2008, 71:960-971.
113. Minai R., et al. Method for comparing the structures of protein ligand-binding sites and application for predicting protein-drug interactions. Proteins 2008, 72:367-381.
114. Ausiello G., et al. Query3d: a new method for high-throughput analysis of functional residues in protein structures. BMC Bioinformatics 2005, 6(Suppl. 4):S5.
115. Ramensky V., et al. A novel approach to local similarity of protein binding sites substantially improves computational drug design results. Proteins 2007, 69:349-357.
116. Schalon C., et al. A simple and fuzzy method to align and compare druggable ligand-binding sites. Proteins 2008, 71:1755-1778.
117. Brakoulias A., Jackson R.M. Towards a structural classification of phosphate binding sites in protein-nucleotide complexes: an automated all-against-all structural comparison using geometric matching. Proteins 2004, 56:250-260.
118. Shulman-Peleg A., et al. Recognition of functional sites in protein structures. J. Mol. Biol. 2004, 339:607-633.
119. Snyder K.A., et al. Domain-based small molecule binding site annotation. BMC Bioinformatics 2006, 7:152.
120. Jambon M., et al. The SuMo server: 3D search for protein functional sites. Bioinformatics 2005, 21:3929-3930.
121. McGready A., et al. Vicinity analysis: a methodology for the identification of similar protein active sites. J. Mol. Model. 2009, 15:489-498.
122. Weskamp N., et al. Multiple graph alignment for the structural analysis of protein active sites. IEEE/ACM Trans. Comput. Biol. Bioinform. 2007, 4:310-320.
123. Pons J.L., Labesse G. @TOME-2: a new pipeline for comparative modeling of protein-ligand complexes. Nucleic Acids Res. 2009, 37(Web Server issue):W485-491.