Prediction of specificity and cross-reactivity of kinase inhibitors

Letters in Drug Design and Discovery

Volumn 8, Issue 3, 2011, Pages 223-228

  Mishra, Nitish Kumar ^a   Raghava, Gajendra P S ^a  

^a INSTITUTE OF MICROBIAL TECHNOLOGY   (India)

Author keywords

Dissociation constant; Kinase inhibitors; Molecular descriptors; Prediction; QSAR; Support vector machine (SVM)

Indexed keywords

PROTEIN KINASE; PROTEIN KINASE INHIBITOR;

ARTICLE; BINDING AFFINITY; CROSS REACTION; ENZYME SPECIFICITY; ENZYME STRUCTURE; PREDICTION; PRIORITY JOURNAL; WEB BROWSER;

EID: 79952285855     PISSN: 15701808     EISSN: None     Source Type: Journal    
DOI: 10.2174/157018011794578204     Document Type: Article

References (39)

1. Cohen, P. Protein kinases the major drug targets of the twenty first century? Nat. Rev. Drug Discov., 2002, 2, 296-313.
2. Huse, M.; Kuriyan, J. The conformational plasticity of protein kinases. Cell, 2002, 109, 275-282. (Pubitemid 34606870)
3. Hopkins, A.L. Can we rationally design promiscuous drugs? Curr. Opin. Struct. Biol., 2006, 16, 127-136. (Pubitemid 43221881)
4. Veith, M. Kinomics-structural biology and chemogenomics of kinase inhibitors and targets. Biochem. Biophys. Acta, 2004, 1697, 243-257.
5. Kumar, S.; Lee, J.C. P38 MAP kinase: key signaling molecules as therapeutic targets for inflammatory diseases. Nat. Rev. Drug Discov., 2003, 2, 717-726. (Pubitemid 37361787)
6. Dancey, J.; Sausville, E.A. Issue and progress with protein kinase inhibitors for cancer treatment. Nat. Rev. Drug Discov., 2003, 2, 296-313.
7. Garberken The second wave in kinase cancer drugs. Nat. Biotechnol., 2006, 24, 127-130.
8. Bain, J.; Cohen, P. The specificities of protein kinase inhibitors: an update. Biochemistry, 2003, 371, 199-204. (Pubitemid 36458094)
9. Davies, S.P.; Cohen, P. Specificity and mechanism of action of some commonly used protein kinase inhibitors. Biochem. J., 2000, 351, 95-105.
10. Fabin, M.A. A small molecule kinase interaction map for clinical kinase inhibitors. Nat. Biotechnol., 2005, 23, 329-336.
11. Manning, G.; Whyte, D.B.; Martinez, R.; Hunter, T.; Sudarsanam, S. The protein kinase complement of the human genome. Science, 2002, 298, 1912-1934. (Pubitemid 35425239)
12. Gasteiger, J. Calculation of structure descriptors. In Chemoinformatics, Wiley-VCH, 2003, pp. 401-431.
13. Molinspiration [http://www.molinspiration.org]
14. PreADMET. Available at: [http://www.preadmet.bmdrc.org/preadmet/index. html]
15. Joachims, T. Making large scale SVM learning practical. In Advanced in Kernel Methods- Support Vector Learning. Scholkopf, B.; Burges, C.J.C.; Smola, A.J., Eds. MIT Press Cambridge, USA, 1999, pp. 42-56.
16. Perveen, S.; Yasmeen, A.; Khan, M.A.; Dar, A.; Jafri, R.; Ahmed, A. Structure activity relationship of organic alcohol and esters for antidepressant like activity. Lett. Drug Des. Discov., 2010, 7, 14-17.
17. Kallech-Ziri, O.; Luis, J.; Faljoun, Z.; Sabatier, J.M.; Lehmann, M.; Ayeb, M.E1.; Marrakchi. N.; Loret, E. Structure function relationships of KTS disintegrins and design of antiangiogenic drugs. Lett. Drug Des. Discov., 2010, 7, 36-40.
18. Imming, P.; Sinning, C.; Meyer, A. Drugs, their targets and the nature and number of drug targets. Nat. Rev. Drug Discov., 2006, 5, 821-834.
19. Gray, N.S. Exploiting chemical libraries, structure and genomics in the search of kinase inhibitors. Science, 2003, 281, 533-538.
20. Mishra, N.K.; Kumar, M.; Raghava, G.P.S. Support vector machine based prediction of glutathione s-transferase proteins. Prot. Pept. Lett., 2007, 14, 575-80. (Pubitemid 47041576)
21. Kumar, M.; Verma, R.; Raghava, G.P.S. Prediction of mitochondrial proteins using support vector machine and hidden markov model. J. Biol. Chem., 2006, 281, 5357-5363. (Pubitemid 43847629)
22. Garg, A.; Raghava, G.P.S. ESLpred2: improved method for predicting subcellular localization of eukaryotic proteins. BMC Bioinformatics, 2008, 9, 503.
23. Bhasin, M.; Raghava, G.P.S. PSLPred: prediction of subcellular localization of bacterial proteins. Bioinformatics, 2005, 21, 2522-2524.
24. Kaur, H.; Raghava, G.P.S. Prediction of alpha-turns in proteins using PSI-BLAST profiles and secondary structure information. Proteins, 2004, 55, 83-90. (Pubitemid 38292836)
25. Kaur, H.; Raghava, G.P.S. A neural network method for prediction of beta-turn types in proteins using evolutionary information. Bioinformatics, 2004, 20, 2751-2758. (Pubitemid 39530161)
26. Mishra, N.K.; Raghava, G.P.S. Prediction of FAD interacting residues in a protein from its primary sequence using evolutionary information. BMC Bioinformatics, 2010, 11, S48.
27. Chauhan, J.S.; Mishra, N.K.; Raghava, G.P.S. Identification of ATP binding residues of a protein from its primary sequence. BMC Bioinformatics, 2009, 10, 434.
28. Chauhan, J.S.; Mishra, N.K.; Raghava, G.P.S. Prediction of GTP interacting residues, dipeptides and tripeptides in a protein from its evolutionary information. BMC Bioinformatics, 2010, 11, 301.
29. Ansari, H.R.; Raghava, G.P.S. Identification of NAD interacting residues in proteins. BMC Bioinformatics, 2010, 11, 160.
30. Lata. S.; Mishra, N.K.; Raghava, G.P.S. AntiBP2: improved version of antibacterial peptide prediction. BMC Bioinformatics, 2010, 11, S19.
31. Mishra, N.K.; Agarwal, S.; Raghava, G.P.S. Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule. BMC Pharmavol., 2010, 10, 8.
32. Garg, A.; Tewari, R.; Raghava, G.P.S. KiDoQ: using docking based energy to develop ligand based model for predicting antibacterials. BMC Bioinformatics, 2010, 11, 125.
33. Furey, T.S.; Haussler, D. Support vector machine classification and validation of cancer tissue sample using microarray express data. Bioinformatics, 2002, 16, 906-914.
34. Brown, M.P.S.; Lin, D. Knowledge-based analysis of microarray gene expression data by using support vector machine. Proc. Natl. Acad. Sci. USA, 2000, 97, 262-267. (Pubitemid 30055819)
35. Trotter, M.W.B.; Holden, S.B. Support vector machines for ADME property classification. QSAR Comb. Chem., 2003, 22, 533-548. (Pubitemid 36928903)
36. Burdige, R. Drug design by machine learning: support vector machine for pharmaceutical data analysis. Comput. Chem., 2001, 26, 5-14.
37. Totter, M.B.W.; Halden, S.B. Support vector machine in combinatorial chemistry. Measur. Control, 2003, 34, 235-239.
38. Zernov, V.V. Drug discovery using support vector machine. The case studies of drug-likeness, agrochemical-likeness and enzyme inhibition prediction. J. Chem. Inf. Comp. Model., 2003, 43, 2048-2056.
39. Yap, C.W.; Chen, Y.Z. Prediction of tarsade-causing potential of drugs by support vector machine approach. Toxicol. Sci., 2004, 79, 170-177. (Pubitemid 38660260)