QSAR and molecular docking based design of some n-benzylacetamide as ?-aminobutyrate-aminotransferase inhibitors
DOI:
https://doi.org/10.18540/jcecvl4iss1pp0065-0084Keywords:
γ-aminobutyrate-aminotransferase, Ligand-based design, Quantitative structure activity relationship, Kennard-Stone algorithm, Molecular docking, Genetic function algorithmAbstract
Quantitative structure activity relationship study (QSAR) and molecular docking were used to design and virtually screen some new N-benzylacetamide derivatives for their ability to inhibit ?-amino butyrate-aminotransferase. Ninety compounds with anticonvulsant activity against maximal electroshock induced seizures were used for QSAR study. B3LYP/6-31G** quantum mechanical method was employed to optimize/minimize the molecular structure of these compounds. Genetic Function Algorithm (GFA) method was used to develop the QSAR models. Each model gave an octa-parametric equation with good statistical qualities (R2 ranged from 0.823 to 0.893, Q2 from 0.772 to 0.854, F from 36.53 to 37.10, R2pred(test) from 0.768 to 0.893). Information obtained from the parameter contained in the model suggested that increasing the molecular mass and linearity of molecule would lead to increase in anticonvulsant activity of studied compounds. These informed the design and virtual screening of 118 new N-benzylacetamide derivatives using 2-acetamido-N-benzyl-2-(5-methylfuran-2-yl)acetamides as the template. The designed molecules were docked with ?-amino butyrate-aminotransferase (GABA_AT; PDB: 1OHV) using Internal Coordinate Mechanics Program (ICM-pro 3.8-3). The binding affinity of the designed compounds with GABA_AT were comparable to that of 4-aminohex-5-enoic acid (vigabatrin) and 3, 3-diphenylpyrrolidine-2, 5-dione (phenytoin) and 5H-dibenzo [b,f]azepine-5-carboxamide (carbamazepine), which are known inhibitors of GABA_AT. Therefore, the designed molecules have potential as inhibitors of GABA_AT and consequently as anticonvulsant agent.
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Stock LM. The origin of the inductive effect. J. Chem. Edu. 1972; 49 (6): 400.
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Ambure P, Aher RB, Gajewicz A, Pyzyt WRK, “NanoBRIDGES” software:open acess tools to perform QSAR and nano-QSAR modeling. Chem. Intel. Lab. Syst. 2015; 147:1-13.Roy PP, Leonard JT, Roy K. Exploring the impact of training sets for the development of predictive QSAR models. Chem. Intel. Lab Syst. 2008; 90:31–42.
David TT, Laura B, Peter MJM, Simon MC, Nicholas LS. Gamma-aminobutyric acid (GABA) transport across human intestinal epithelial (Caco-2) cell monolayers. J Pharmacol. 2000; 129(3): 457–464.
Dearden JC, Cronin MTD, Kaiser KLE. How not to develop a quantitative structure–activity or structure–property relationship. SAR and QSAR in Env. Res. 2009; 20:3-4.
Eriksson L, Jaworska J, Worth AP, Cronin MT, McDowell RM, et al. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ. Health Perspect. 2003;111: 1361-1375.
Golbraikh A, Tropsha A. Beware of q2! J. Mol. Graph. Mod. 2002; 20: 269–276.
Hall LH, Kier LB. Electrotopological state indices for atom types: A novel combination of electronic, topological, and valence state information. J. Chem. Inf. Comput. Sci. 1995; 35: 1039-1045.
Huuskonen, JJ, Livingstone DJ, Tetko IV. Neural network modeling for estimation of partition coefficient based on atom-type electrotopological state indices. J. Chem. Inf.Comput. Sci. 2000; 40: 947–955.
Ibezim EC, Duchowicz PR, Ibezim NE, Mullen LMA, Onyishi IV, Brown SA, Castro EA. Computer aided linear modeling employing QSAR for drug discovery. Scientific Research and Essay. 2009; 4(13):1559-1564.
Jaworska J, Nikolova-Jeliazkova N, Aldenberg T. QSAR applicability domain estimation by projection of the training set descriptor space: a review. Altern. Lab. Anim. 2005; 33: 445.
K. Roy. On some aspects of validation of predictive QSAR models. Expert Opin. Drug Discov. 2007; 2: 1567–1577.
Kennard RW, Stone LA. Computer aided design of experiments. Tech. 1969; 11:137–148.
Kier LB, Hall LH. Molecular connectivity in chemistry and drug research. New York: Academic Press. 1976.
King AM. Synthesis and pharmacological evaluation of primary amino acid derivatives (PAADs): novel neurological agents for the treatment of epilepsy and neuropathic pain. Thesis submitted to Division of Medicinal Chemistry and Natural Products University of North Carolina at Chapel Hill, 2010. Retrieved 5th May 2016from https://cdr.lib.unc.edu/.
Lesk AM. Introduction to bioinformatics. Oxford University Press; 2002.
Mattson RH. Efficacy and adverse effects of established and new antiepileptic drugs. Epil. 1995; 36:13–26.
Molsoft Internal Coordinate Mechanics Program (ICM-pro 3.8.3) for bioinformatics available from www.molsoft.com.
Moreau G, Turpin C. Use of similarity analysis to reduce large molecular libraries to smaller sets of representative molecules. Analusis. 1996; 24:M17–M21.
Oluwaseye A, Uzairu A, Shallangwa G, Abechi S. A novel QSAR model for designing, evaluating, and predicting the anti-MES activity of new 1H-pyrazole-5-carboxylic acid derivatives. JOSTCA. 2017; 4(3):739–74.
Paola S, De Biase D, Francesco B, Stefano B, Andrea M, Caroline P, Richard B. S. Tilman S. Structures of ?-aminobutyric acid (GABA) aminotransferase, a pyridoxal 5?-phosphate, and [2Fe-2S] Cluster-containing Enzyme, complexed with ?-ethynyl-GABA and with the antiepilepsy drug vigabatrin. J. Bio. Chem. 2004; 279: 363-373.
Pedro JB, John BOM. A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking. Bioinformatics. 2010; 26(9): 1169-1175.
Phyllis JB. A Biochemist’s experience with GABA. J. Ortho-mole. Med. 2011; 26(1).
Rogers D. Hopfinger AJ. Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. J. Chem. Inf: Comput. Sci. 1994, 34; 854-866.
Roy K, Das RN, Ambure P, Aher RB. Be Aware of Error Measures. Further Studies on Validation of Predictive QSAR Models. Chem. Intel. Lab. Syst. 2016; 152: 18-33.
Roy K, Kar S, Ambure P. On a simple approach for determining applicability domain of QSAR models. Chem. Intel. Lab. Syst. 2015; 145: 22-9.
Roy K, Mitra I, Kar S, Ojha PK, Das RN, Kabir H. Comparative studies on some metrics for external validation of QSPR models. J. Chem. Inf. Mod. 2012; 52: 396–408.
Shao Y, Molnar LF, Jung Y, Kussmann J, Ochsenfeld C, Brown ST, et al. Advances in methods and algorithms in modern quantum chemistry program package. Phys. Chem. Chem. Phys. 2006; 8:3172.
Singh P. Quantitative structure-activity relationship study of substituted-[1,2,4] oxadiazoles as S1P1 agonists. J. Cur. Chem. Pharm. Sci. 2013; 3(1):64-67.
Stock LM. The origin of the inductive effect. J. Chem. Edu. 1972; 49 (6): 400.
Storici P, Qiu J, Schirmer T, Silverman RB. Mechanistic crystallography. Mechanism of inactivation of gamma-aminobutyric acid aminotransferase by (1R,3S,4S)-3-amino-4-fluorocyclopentane-1-carboxylic acid as elucidated by crystallography. Biochem. 2004; 43(44): 14057–14063.
Todeschini R, Consonni V. Molecular Descriptors for Chemoinformatics, Vol I and II WILEY-VCH Verlag GmbH and Co, Weinheim. 2009: 6-745.
Topliss JG, Costello RJ. Chance correlations in structure– activity studies using multiple regression analysis. J. Med. Chem. 1972; 15:1066–1068.
Tropsha A, Gramatica P, Gombar VK. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb. Sci. 2003; 22:69–77.
Usman A, Adamu U, Sani U. Quantitative structure-activity relationship and molecular docking studies of a series of quinazolinonyl analogues as inhibitors of gamma amino butyric acid aminotransferase. J. Adv. Res. 2017; 8:33–43.
Whiting PJ. GABA-A receptor subtypes in the brain: a paradigm for CNS drug discovery. Reviews in D. D.T. 2003; 8(10): 445-449.
Yap CW. PaDEL-Descriptor: An open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem. 2011; 32 (7):1466-1474.
Yeh S, Tsai MY, Xu Q, Mu XM, Lardy H, Huang KE et al. Generation and characterization of androgen receptor knockout (ARKO) Mice: an in vivo model for the study of androgen Functions in Selective Tissues. Proc. Natl. Acad. Sci. 2002; 99(21): 13498–13503.
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Published
2018-01-16
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Adedirin, O., Uzairu, A., Shallangwa, G. A., & Abechi, S. E. (2018). QSAR and molecular docking based design of some n-benzylacetamide as ?-aminobutyrate-aminotransferase inhibitors. The Journal of Engineering and Exact Sciences, 4(1), 0065–0084. https://doi.org/10.18540/jcecvl4iss1pp0065-0084
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