Structural models in the assessment of protein druggability based on HTS data

Journal of Computer-Aided Molecular Design

Volumn 23, Issue 8, 2009, Pages 583-592

  Gupta, Anvita ^a   Gupta, Arun Kumar ^a   Seshadri, Kothandaraman ^a  

^a ASTRAZENECA   (United Kingdom)

Author keywords

Druggability; High throughput screening; Hit rates; Protein binding pockets; Target identification; Target validation

Indexed keywords

BIOCHEMISTRY; REGRESSION ANALYSIS; THROUGHPUT;

BINDING POCKETS; DRUGGABILITY; HIGH THROUGHPUT SCREENING; HIT RATE; PROTEIN BINDING POCKET; PROTEIN-BINDING; SCREENING DATA; STRUCTURAL MODELING; TARGET VALIDATIONS; TARGET'S IDENTIFICATIONS;

PROTEINS;

ARTICLE; BINDING AFFINITY; BINDING SITE; COMPUTER AIDED DESIGN; COMPUTER PREDICTION; CONTROLLED STUDY; DRUG SCREENING; DRUG TARGETING; HIGH THROUGHPUT SCREENING; INTERNAL CONSISTENCY; LIGAND BINDING; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MOLECULAR WEIGHT; NUCLEAR MAGNETIC RESONANCE; PRIORITY JOURNAL; PROTEIN BINDING; REGRESSION ANALYSIS;

COMPUTER-AIDED DESIGN; DRUG DISCOVERY; HIGH-THROUGHPUT SCREENING ASSAYS; HUMANS; LIGANDS; MAGNETIC RESONANCE SPECTROSCOPY; MOLECULAR CONFORMATION; PROTEIN BINDING; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SMALL MOLECULE LIBRARIES; SOFTWARE;

EID: 67651005599     PISSN: 0920654X     EISSN: 15734951     Source Type: Journal    
DOI: 10.1007/s10822-009-9279-y     Document Type: Article

References (29)

1. Betz UA (2005) How many genomic targets can a portfolio afford? Drug Discov Today 10(15):1057-1063. doi: 10.1016/S1359-6446(05)03498-7
2. Hopkins AL, Groom CR (2002) The druggable genome. Nat Rev Drug Discov 1(9):727-730. doi: 10.1038/nrd892
3. Drews J (2006) Whats's in a number? Nat Rev Drug Discov 5:975. doi: 10.1038/nrd2205
4. Zambrowicz BP, Sands AT (2003) Knockouts model the 100 best-selling drugs-will they model the next 100? Nat Rev Drug Discov 2(1):38-51. doi: 10.1038/nrd987
5. Hajduk PJ, Huth JR, Fesik SW (2005) Druggability indices for protein targets derived from NMR-based screening data. J Med Chem 48(7):2518-2525. doi: 10.1021/jm049131r
6. Han LY, Zheng CJ, Xie B, Jia J, Ma XH, Zhu F, Lin HH, Chen X, Chen YZ (2007) Support vector machines approach for predicting druggable proteins: Recent progress in its exploration and investigation of its usefulness. Drug Discov Today 12(7-8):304-313. doi: 10.1016/ j.drudis.2007.02.015
7. Nayal M, Honig B (2006) On the nature of cavities on protein surfaces: application to the identification of drug-binding sites. Proteins 63(4):892-906. doi: 10.1002/prot.20897
8. Cheng AC, Coleman RG, Smyth KT, Cao Q, Soulard P, Caffrey DR, Salzberg AC, Huang ES (2007) Structure-based maximal affinity model redicts small-molecule druggability. Nat Biotechnol 25(1):71-75. doi: 10.1038/ nbt1273
9. Overington JP, Al-Lazikani B, Hopkins AL (2006) How many drug targets are the there? Nature 5(12):993-996. doi: 10.1038/nrd2199
10. Lipinski CA (2000) Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods 44(1):235-249. doi: 10.1016/S1056-8719(00)00107-6
11. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46(1-3):3-26. doi: 10.1016/S0169-409X(00)00129-0
12. Kuntz ID, Chen K, Sharp KA, Kollman PA (1999) The maximal affinity of ligands. Proc Natl Acad Sci USA 96(18):9997-10002. doi: 10.1073/ pnas.96.18.9997
13. Bajorath J (2002) Integration of virtual and high-throughput screening. Nat Rev Drug Discov 1(11):882-894. doi: 10.1038/nrd941
14. Davies JW, Glick M, Jenkins JL (2006) Streamlining lead discovery by aligning in silico and high-throughput screening. Curr Opin Chem Biol 10(4):343-351. doi: 10.1016/j.cbpa.2006.06.022
15. Pereira DA, Williams JA (2007) Origin and evolution of high throughput screening. Br J Pharmacol 152(1):53-61. doi: 10.1038/sj.bjp.0707373
16. Pellecchia M, Bertini I, Cowburn D, Dalvit C, Giralt E, Jahnke W, James TL, Homans SW, Kessler H, Luchinat C, Meyer B, Oschkinat H, Peng J, Schwalbe H, Siegal G (2008) Perspectives on NMR in drug discovery: A technique comes of age. Nat Rev Drug Discov 7:738-745. doi: 10.1038/ nrd2606
17. Puvanendrampillai D, Mitchell JB (2003) L/D Protein ligand database (PLD): Additional understanding of the nature and specificity of protein-ligand complexes. Bioinformatics 19(14):1856-1857. doi: 10.1093/ bioinformatics/btg243
18. Kumar MD, Gromiha MM (2006) Protein Ligand Database (PLD): Additional understanding of the nature and specificity of protein-ligand complexes. Nucleic Acids Res 34(Database issue):195-198. doi: 10.1093/nar/gkj017
19. Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK (2007) BindingDB: A web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res 35(Database issue):198-201. doi: 10.1093/nar/gkl999
20. Perola E, Walters WP, Charifson PS (2004) A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance Proteins. Proteins 56(2):235-249. doi: 10.1002/prot.20088
21. Böhm HJ (1998) 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):309-323. doi: 10.1023/A:1007999920146
22. Schrödinger SiteMap Fast, accurate and practical binding site identification. 8.0. (2008) New York, NY, Schrödinger, LLC. 2005. Ref Type: Computer Program
23. Connolly ML (1993) The molecular surface package. J Mol Graph 11(2):139-141. doi: 10.1016/0263-7855(93)87010-3
24. Case DA, Cheatham TE, Darden T, Gohlke H, Luo R, Merz KM, Onufriev A, Simmerling C, Wang B, Woods RJ (2005) The Amber biomolecular simulation programs. J Comput Chem 26(16):1668-1688. doi: 10.1002/jcc.20290
25. Gupta AK, Babu MA, Kaskhedikar SG (2004) VALSTAT: Validation program for quantitative structure activity relationship studies. Indian J Pharm Sci 66(4):396-402
26. Wold S, Eriksson L (1995) Statistical validation of QSAR results. In: van de Waterbeemd H (ed) Chemometrics methods in molecular design. VCH, Weinheim, pp 309-318
27. Veretnik S, Fink JL, Bourne PE (2008) Computational biology resources lack persistence and usability. PLOS Comput Biol 4(7):e1000136. doi: 10.1371/journal.pcbi.1000136
28. Abad-Zapatero CMJT (2005) Ligand efficiency indices as guideposts for drug discovery. Drug Discov Today 10(7):464-469. doi: 10.1016/ S1359-6446(05)03386-6
29. Hopkins AL, Groom CR, Alex A (2004) Ligand efficiency: A useful metric for lead selection. Drug Discov Today 9(10):430-431. doi: 10.1016/ S1359-6446(04)03069-7