IN-SILICO STUDIES OF SOME INDOLE DERIVATIVES AS AN ANTI-HEPATITIS C DRUG.
Palavras-chave:QSAR, molecular docking, indole, HCV, NS5B polymerase, Binding energy.
A combined three-dimensional quantitative structure-activity relationship (QSAR) modeling and molecular docking studies were carried out on the 64 indole derivatives and was accomplished to profoundly understand the structure-activity correlation of indole-based inhibitors of the HCV NS5B polymerase against HCV. Genetic function approximation (GFA) of Material studio software version 8 was used to perform the QSAR study while Autodock vina version 4.0 of Pyrx software was used for molecular docking studies of the selected indole derivatives. The optimum model builds exhibited statistically significant results: squared correlation coefficient (R2) of 0.760, adjusted squared correlation coefficient (R2 adj) value of 0.708, Leave one out (LOO) cross-validation coefficient value of 0.634 and the external validation (R2 pred) of 0.621. Molecular docking study of the indole derivative with 1G8Q as the protein target revealed that the best binding affinity with the docking scores of -9.4 kcal/mol formed hydrophobic interaction and H-bonding with amino acid residues of HCV NS5B polymerase. The QSAR model generated and molecular docking results proposed that the model had a good level of stability, strength, and predictability at internal and external validation, and the physicochemical parameters are to be analyzed when designing new indole derivatives agent with better activity against the 1G8Q target site.
ABDULFATAI, U.; UZAIRU, A.; & UBA, S. Quantitative structure-activity relationship and molecular docking studies of a series of quinazolinonyl analogs as inhibitors of gamma-aminobutyric acid aminotransferase. Journal of advanced research, 8(1), 33-43, 2017.
ALTER, M. J. Epidemiology of hepatitis c virus infection. World journal of gastroenterology: wjg, 13(17), 2436, 2007.
BALAVIGNESH, V.; SRINIVASAN, E.; RAMESH BABU, N.; & SARAVANAN, N. Molecular docking study on ns5b polymerase of hepatitis c virus by the screening of volatile compounds from acacia concinna and admit prediction. International journal of pharmacy & life sciences, 4(4), 2013.
BARRIL, X.; & MORLEY, S. D. Unveiling the full potential of flexible receptor docking using multiple crystallographic structures. Journal of medicinal chemistry, 48(13), 4432-4443, 2005.
CHOO, Q.-L.; KUO, G.; WEINER, A. J.; OVERBY, L. R.; BRADLEY, D. W.; & HOUGHTON, M. Isolation of a cdna clone derived from a blood-borne non-a, non-b viral hepatitis genome. Science, 244(4902), 359-362, 1989.
FAUVELLE, C.; LEPILLER, Q.; FELMLEE, D. J.; FOFANA, I.; HABERSETZER, F.; STOLL-KELLER, F.; FAFI-KREMER, S. Hepatitis c virus vaccines–progress and perspectives. Microbial pathogenesis, 58, 66-72, 2013.
HAUDECOEUR, R.; PEUCHMAUR, M.; AHMED?BELKACEM, A.; PAWLOTSKY, J. M.; & BOUMENDJEL, A. Structure-activity relationships in the development of allosteric hepatitis c virus rna?dependent RNA polymerase inhibitors: ten years of research. Medicinal research reviews, 33(5), 934-984, 2013.
JALALI-HERAVI, M.; & KYANI, A. Use of computer-assisted methods for the modeling of the retention time of a variety of volatile organic compounds: a PCA-mlr-ann approach. Journal of chemical information and computer sciences, 44(4), 1328-1335, 2004.
KENNARD, R. W.; & STONE, L. A. Computer-aided design of experiments. Technometrics, 11(1), 137-148, 1969.
KHALED, K. Modeling corrosion inhibition of iron in the acid medium by genetic function approximation method: a qsar model. Corrosion science, 53(11), 3457-3465, 2011.
LAVANCHY, D. The global burden of hepatitis c. Liver international, 29(s1), 74-81, 2009.
LAW, L. M. J.; LANDI, A.; MAGEE, W. C.; TYRRELL, D. L.; & HOUGHTON, M. Progress towards a hepatitis c virus vaccine. Emerging Microbes & infections, 2(11), e79, 2013.
LÜ, W.; & XUE, Y. Prediction of hepatitis c virus non-structural proteins 5b polymerase inhibitors using machine learning methods. Acta physico-Chimica Sinica, 27(6), 1407-1416, 2011.
MENG, X.-Y.; ZHANG, H.-X.; MEZEI, M.; & CUI, M. Molecular docking: a powerful approach for structure-based drug discovery. Current computer-aided drug design, 7(2), 146-157, 2011.
MOHAMMAD, H.; & ZOHREH, A. In-silico prediction of rgs4 inhibitory activity of sometiadiazolidinone. Int j med pharm, ,1 2013.
MOHD HANAFIAH, K.; GROEGER, J.; FLAXMAN, A. D.; & WIERSMA, S. T. Global epidemiology of hepatitis c virus infection: new estimates of age?specific antibody to HCV seroprevalence. Hepatology, 57(4), 1333-1342, 2013.
MORADPOUR, D.; PENIN, F.; & RICE, C. M. Replication of hepatitis c virus. Nature reviews microbiology, 5(6), 453, 2007.
SHEPARD, C. W.; FINELLI, L.; & ALTER, M. J. Global epidemiology of hepatitis c virus infection. The Lancet infectious diseases, 5(9), 558-567, 2005.
SOFIA, M. J.; CHANG, W.; FURMAN, P. A.; MOSLEY, R. T.; & ROSS, B. S. Nucleoside, nucleotide, and non-nucleoside inhibitors of hepatitis c virus ns5b RNA-dependent RNA-polymerase. Journal of medicinal chemistry, 55(6), 2481-2531, 2012.
TROTT, O.; & OLSON, A. J. Autodock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2), 455-461, 2010.
VEERASAMY, R.; RAJAK, H., JAIN, A.; SIVADASAN, S.; VARGHESE, C. P.; & AGRAWAL, R. K. Validation of qsar models-strategies and importance. International journal of drug design & discovery, 3, 511-519, 2011.
VRONTAKI, E.; MELAGRAKI, G.; MAVROMOUSTAKOS, T.; & AFANTITIS, A. Exploiting chembl database to identify indole analogs as HCV replication inhibitors. Methods, 71, 4-13, 2015.
WEI, Y.; Li, J.; QING, J.; Huang, M.; WU, M.; GAO, F.; HUANG, W. Discovery of novel hepatitis c virus ns5b polymerase inhibitors by combining random forest, multiple e-pharmacophore modeling, and docking. Plus one, 11(2), e0148181, 2016.
YAP, C. W. Padel?descriptor: an open source software to calculate molecular descriptors and fingerprints. Journal of computational chemistry, 32(7), 1466-1474, 2011.