Docking and quantitative structure–activity relationship studies for sulfonyl hydrazides as inhibitors of cytosolic human branched-chain amino acid aminotransferase
- 250 Downloads
We have performed the docking of sulfonyl hydrazides complexed with cytosolic branched-chain amino acid aminotransferase (BCATc) to study the orientations and preferred active conformations of these inhibitors. The study was conducted on a selected set of 20 compounds with variation in structure and activity. In addition, the predicted inhibitor concentration (IC50) of the sulfonyl hydrazides as BCAT inhibitors were obtained by a quantitative structure–activity relationship (QSAR) method using three-dimensional (3D) vectors. We found that three-dimensional molecule representation of structures based on electron diffraction (3D-MoRSE) scheme contains the most relevant information related to the studied activity. The statistical parameters [cross-validate correlation coefficient (Q 2 = 0.796) and fitted correlation coefficient (R 2 = 0.899)] validated the quality of the 3D-MoRSE predictive model for 16 compounds. Additionally, this model adequately predicted four compounds that were not included in the training set.
KeywordsBranched-chain amino acid aminotransferase inhibitors Molecular docking Quantitative structure–activity relationships Three-dimensional descriptors
Unable to display preview. Download preview PDF.
- 1.Ichihara A (1985) Aminotransferases of branched-chain amino acids. In: Christen P, Metzler DE (eds) Transaminases. Wiley, New York, pp 430–439Google Scholar
- 8.Hays SJ, Hu L-Y, Lei H, Scholten JD, Wustrow DJ (2002) US Patent 6632831Google Scholar
- 10.Hu L-Y, Boxer PA, Kesten SR, Lei HJ, Wustrow DJ, Moreland DW, Zhang L, Ahn K, Ryder TR, Liu X, Rubin JR, Fahnoe K, Carroll R, Dutta T, Fahnoe S, Probert DC, Roof AW, Rafferty RL, Kostlan MF, Scholten CR, Hood JD, Ren M, Schielke X-D, Su GP, Taylor T-Z, Mistry CP, Mc A, Connell P, Hasemann C, Ohren J (2006) The design and synthesis of human branched-chain amino acid aminotransferase inhibitors for treatment of neurodegenerative diseases. Bioorg Med Chem Lett 16: 2337–2340. doi: 10.1016/j.bmcl.2005.07.058 PubMedCrossRefGoogle Scholar
- 11.Sekhar PN, Amrutha RN, Sangam S, Verma DPS, Kishor PBK (2007) Biochemical characterization, homology modeling and docking studies of ornithine δ-aminotransferase—an important enzyme in proline biosynthesis of plants. J Mol Graph Model 26: 709–719. doi: 10.1016/j.jmgm.2007.04.006 PubMedCrossRefGoogle Scholar
- 15.Biosym Technologies (1993) Insight II Version 2.3, Discover Version 2.9.5 Biosym Technologies, San Diego, USAGoogle Scholar
- 16.Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19: 1639–1662. doi: 10.1002/(SICI)1096-987X(19981115)19:14<1639::AID-JCC10>3.0.CO;2-B CrossRefGoogle Scholar
- 17.Tripos Inc. (2006) SYBYL version 7.3, Tripos Inc., 1699 South Hanley Road, St. Louis, MO 63144, USAGoogle Scholar
- 18.Todeschini R, Consonni V, Pavan M (2002) Dragon Software version 2.1Google Scholar
- 24.The Mathworks Inc. (2004) MATLAB version 7.0. The Mathworks Inc., Natick, MA, http://www.mathworks.com
- 31.Caballero J, Fernández M (2008) Artificial neural networks from MATLAB® in medicinal chemistry. Bayesian-regularized genetic neural networks (BRGNN): Application to the prediction of the antagonistic activity against human platelet thrombin receptor (PAR-1). Curr Top Med Chem 8: 1580–1605. doi: 10.2174/156802608786786570 PubMedCrossRefGoogle Scholar
- 32.Saíz-Urra L, González MP, Teijeira M (2006) QSAR studies about cytotoxicity of benzophenazines with dual inhibition toward both topoisomerases I and II: 3D-MoRSE descriptors and statistical considerations about variable selection. Bioorg Med Chem 14: 7347–7358. doi: 10.1016/j.bmc.2006.05.081 PubMedCrossRefGoogle Scholar