3D-QSAR AND MOLECULAR DOCKING STUDIES ON 1, 2, 4 TRIAZOLES AS MetAP2 INHIBITORS
Objective: Angiogenesis inhibitors are a novel class of promising therapeutic agents for treating cancer and other human diseases. Biological transformations and pathways that play a role in angiogenesis are, therefore, particularly attractive targets as potential methods for inhibiting solid tumors. MetAP2 is of particular interest because the enzyme plays a key role inÂ angiogenesis, the growth of new blood vessels, which is necessary for the progression of diseases including solid tumorÂ cancersÂ and rheumatoid arthritis. In this paper we report the quantitative structure activity relationship and docking studies of 1, 2, 4 triazole derivatives for designing novel MetAP2 inhibitors.
Methods: Tripos Sybyl X 2.1 program was used to conduct docking based CoMFA, CoMSIA and Topomer CoMFA QSAR modeling for a dataset of 77 triazoles.
Results: The CoMFA, CoMSIA and Topomer CoMFA models demonstrated good statistical results with cross-validated coefficient (q2) of 0.703, 0.704, 0.746 and correlation coefficient (r2) of 0.894, 0.889, 0.886 respectively and these models have been externally validated.
Conclusion: Based on the statistical results obtained from the above model, the CoMFA, CoMSIA and Topomer CoMFA model can be utilized to design new molecules having 1, 2, 4 triazoles as common core with significant MetAP2 inhibitory activity.
2. "How many different types of cancer are there? Cancer Research UK: Cancer Help UK". Retrieved 11 May 2012.
3. Anand P, Kunnumakkara AB, Kunnumakara AB, Sundaram C, Harikumar KB, Tharakan ST, et al. Cancer is a preventable disease that requires major lifestyle changes. Pharm Res 2008;25:2097â€“116.
4. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell 2000;100:57â€“70.
5. Risau W. Mechanisms of angiogenesis. Nature 1997;386:671-4.
6. Yang G, Kirkpatrick RB, Ho T, Zhang G, Liang P, Johanson KO, et al. Steadystate kinetic characterization of substrates and metal-ion specificities of the full-length and N-terminally truncated recombinant human methionine aminopeptidases (type 2). Bio chem 2001;40:10645-54.
7. Arfin SM, Kendall RL, Hall L, Weaver LH, Stewart AE, Matthews BW, et al. Eukaryotic methionylaminopeptidases: two classes of cobalt-dependent enzymes. Proc Natl Acad Sci USA 1995;92:7714â€“8.
8. Li X, Chang YH. Evidence that the human homologue of a rat initiation factor-2 associated protein (p67) is a methionine aminopeptidase. Bio Chem Biophys Res Commun 1996;227:152â€“9.
9. Bennett B Holz. EPR Studies on the Mono-and Dicobalt (II)-Substituted Forms of the Aminopeptidase from Aeromonasproteolytica. Insight into the Catalytic Mechanism of Dinuclear Hydrolases. J Am Chem Soc 1997;119:1923â€“33.
10. Johansson FB, Bond AD, Nielsen UG, Moubaraki B, Murray KS, Berry KJ, et al. Dicobalt II-II, II-III, and III-III complexes as spectroscopic models for dicobalt enzyme active sites. Inorg Chem 2008;47:5079â€“92.
11. Larrabee JA, Leung CH, Moore RL, Thamrong-nawasawat T, Wessler BS. Magnetic circular dichroism and cobalt (II) binding equilibrium studies of Escherichia coli methionyl aminopeptidase. J Am Chem Soc 2004;126:12316â€“24.
12. Taunton J. "How to starve a tumor". Chem Biol 1997;4:493â€“6.
13. Sin N, Meng L, Wang MQ, Wen JJ, Bornmann WG, Crews CM. The anti-angiogenic agent fumagillin covalently binds and inhibits the methionine aminopeptidase, MetAP-2. Proc Natl Acad Sci USA 1997;94:6099â€“103.
14. Griffith EC, Su Z, Turk BE, Chen S, Chang YH, Wu Z, et al. Methionine aminopeptidase (type 2) is the common target for angiogenesis inhibitors AGM-1470 and ovalicin. Chem Biol 1997;4:461â€“71.
15. Lowther WT, McMillen DA, Orville AM, Matthews BW. The anti-angiogenic agent fumagillin covalently modifies a conserved active-site histidine in the Escherichia coli methionine aminopeptidase. Proc Natl Acad Sci USA 1998;95:12153â€“57.
16. Folkman J. Angiogenesis in cancer, vascular, rheumatoid and other disease. Nat Med 1995;1:27â€“31.
17. Yashwant S. Recent advancements of triazoles as anticancer agents. IJPCR 2010;2:95-7.
18. Verner E, Katz BA, Spencer JR, Allen D, Hataye J, Hruzewicz W, et al. Development of serine protease inhibitors displaying a multicentered short (<2.3 A) hydrogen bond binding mode: Inhibitors of Urokinase-type plasminogen activator and factor Xa. J Med Chem 2001;44:2753.
19. Mackman RL, Katz BA, Breitenbucher JG, Hui HC, Loung C, Liu L, et al. Exploiting subsite S1 of trypsin-like serine proteases for selectivity: potent and selective inhibitors of urokinase-type plasminogen activator. J Med Chem 2001;44:3856.
20. Barber CG, Dickinson RP, Horne VA. Selective urokinase-type plasminogen activator (uPA) inhibitors. Part 1:2 pyridinylguanidines. Bioorg Med Chem Lett 2002;12:181.
21. Barber CG, Dickinson RP. Selective urokinase-type plasminogen activator (uPA) inhibitors. Part 2:(3-substituted-5-halo-2-pyridinyl) guanidines. Bioorg Med Chem Lett 2002;12:185.
22. Cramer RD, Jilek RJ, Guessregen S, Clark SJ, Wendt B, Clark RD. Lead hopping. Validation of topomer similarity as a superior predictor of similar biological activities. J Med Chem 2004;47:6777â€“91.
23. Bhongade BA, Gadad AK. 3D-QSAR CoMFA/CoMSIA studies on Urokinase plasminogen activator (uPA) inhibitors: a strategic design in novel anticancer agents. Bioorg Med Chem 2004;12:2797â€“2805.
24. Joseph P M Jr, Paul WF, Glenn AH, Robert BK, Cheryl AJ, Randall KJ, et al. Highly Potent Inhibitors of Methionine Aminopeptidase-2 Based on a 1, 2, 4-Triazole Pharmacophore. J Med Chem 2007;50:3777-85.
25. Carvalho LL, Maltarollo VG, Lima EF, Weber KC, Honorio KM, Silva ABF. Molecular Features Related to HIV Integrase Inhibition Obtained from Structure-and Ligand-Based Approaches. PLOS one 2014;9:1-9.
26. Bostrom J, Bohm M, Gundertofte K, Klebe G. J Chem Inf Comput Sci 2003;43:1020â€“1027.
27. Zaheer UH, Khan W, Zia SR, Iqbal S. Structure-based 3D-QSAR models and dynamics analysis of novel N-benzyl pyridinone as p38Î± MAP kinase inhibitors for anticytokine activity. J Mol Graph Model 2012;36:48-61.
28. Cramer RD, Bunce JD, Patterson DE, Frank IE. Crossvalidation, Bootstrapping, and Partial Least Squares Compared with Multiple Regression in Conventional QSAR Studies. Quant Str Act Rel 1988;7:18-25.
29. Wold S, Ruhe A, Wold H, Dunn WJ. The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses. SIAM J Sci Stat Comput 1984;5:735â€“43.
30. Roy PP, Roy K. On Some Aspects of Variable Selection for Partial Least Squares Regression Models. Quant Str Act 2008;27:302-13.
31. Golbraikh A, Tropsha A. Beware of q2!. J Mol Graph Model 2002;20:269-76.
32. Mohan KK, Bharathkumar I, Pujar GV, Purohit MN, Vijaykumar G S. Design, synthesis and 3D-QSAR studies of new diphenylamine containing 1,2,4-triazoles as potential antitubercular agents. E J Med Chem 2014;82:516-29.
33. Roy PP, Roy K. On two novel parameters for validation of predictive QSAR models. Mole 2009;14:1660-701.
34. Roy PP, Roy K. Comparative chemometric modeling of cytochrome 3A4 inhibitory activity of structurally diverse compounds using stepwise MLR, FA-MLR, PLS, GFA, G/PLS and ANN techniques. Eur J Med Chem 2009;44:2913-22.