QSAR Model for Prediction of some Non-Nucleoside Inhibitors of Dengue Virus Serotype 4 NS5 using GFA-MLR Approach

Document Type : Research Article

Authors

1 Department of Pure and Applied Chemistry, Faculty of Science, University of Maiduguri, P.M.B. 1069, Maiduguri, Borno State, Nigeria.

2 Department of Chemistry, Faculty of Physical Sciences, Ahmadu Bello University, P.M.B. 1044, Zaria, Kaduna State, Nigeria.

Abstract

B3LYP/631G** basis set of DFT quantum mechanical method was used to optimize the molecular geometry of some non-nucleoside inhibitors of dengue 4 virus. Molecular descriptors were mined from the optimized structure and used along with their experimental inhibitory activity (pIC50) as the database for the study. Genetic function algorithm and multiple linear regressions were used to build a robust quantitative structure-activity relationship model. The statistically satisfactory quality of the model as evidenced by its validation parameters: R2 = 0.971, R2adj = 0.961, cRp^2 = 0.809 Q2 = 0.944 and R2pred = 0.627. Thus, the model can be used to predict the activity of new chemicals within its applicability domain. The Average Broto-Moreau autocorrelation - lag 1 / weighted by mass, Centered Broto-Moreau autocorrelation - lag 2 / weighted by Sanderson electronegativities, Coefficient sum of the last eigenvector from Barysz matrix / weighted by van der Waals volumes, nhigh lowest polarizability weighted BCUTS and Fraction of sp3 carbons to sp2 carbons are the descriptors that influenced the anti-dengue activity of the studied compounds. The information obtained from the model in this work can be employed to optimize the anti-dengue activity of the compounds.

Keywords

References

[1]          S. S. Swain, D. Dude, Anti-dengue medicinal plants: a mini review. Res. Rev. J Pharmacogn Phytochem., 1 (2013) 5-9.
[2]          M. E. Beatty, A. Stone, W. D. Fitzsimons, J. N. Hanna, S. K. Lam, S. Vong, Best practices in dengue surveillance: a report from the Asia-Pacific and Americas Dengue Prevention Boards. PLoS. Negl. Trop. Dis., 4 (2010) 890. 
[3]          Z. Fatima, M. Idrees, M. A.  Bajwa, Z. Tahir, O. Ullah, M.Q.  Zia, Serotype and genotype analysis of dengue virus by sequencing followed by phylogenetic analysis using samples from three mini outbreaks-2007-2009 in Pakistan. BMC Microbiol., 11 (2011)200.
[4]          E. P. Toepak, U. S. F. Tambunan, In silico design of fragment-based drug targeting host processing α-glucosidase i for dengue fever. Materials Science and Engineering, 172 (2017) 01201.
 [5]         C. Nitsche, S. Holloway, T. Schirmeister and C. D. Klein, Biochemistry and medicinal chemistry of the dengue virus protease. Chem Rev., 114 (2014) 11348–11381.
[6]          A. J. Stevens, M. E. Gahan, S. Mahalingam, P. A. Keller, The medicinal chemistry of dengue fever. J. Med. Chem., 52 (2009) 7911–7926.
[7]          S. P. Lim, Q. Y. Wang, C. G. Noble, Y. L. Chen, H. Dong, B. Zou, F. Yokokawa, S. Nilar, P. Smith, D. Beer, J. Lescar, P. Y. Shi, Ten years of dengue drug discovery: Progress and prospects. Antiviral Res., 100 (2013) 500–519.
[8]          B.  Canard, Antiviral Research and Development against Dengue Virus CDC (2010) The Dengue Update: Dengue Outbreaks Worldwide. Centre for Disease Control and Prevention, 2 (2017) (1.1).
[9]          N. M. Nguyen, C. N. Tran, L. K. Phung,  K. T. Duong, K. T.; Huynh, Hle. A.;J. Farrar, Q. T. Nguyen, H. T. Tran, C. V. Nguyen, L. Merson, L. T. Hoang, M. L. Hibberd, P. P. Aw,  A. Wilm, N. Nagarajan, D. T. Nguyen, M. P. Pham, T. T. Nguyen, H. Javanbakht, K. Klumpp, J. Hammond, R. Petric, M. Wolbers, C. T. Nguyen, C. P. A. Simmons, Randomized, double-blind placebo controlled trial of balapiravir, a polymerase inhibitor, in adult dengue patients,  J. Infect. Dis., 207 (2013) 1442-1450.
[10]        Bhatt, S.; Gething, P.W.; Brady, O.J.; Messina, J.P.; Farlow, A.W.; Moyes, C.L.; Drake, J.M.;Blanton, R.E., L. K. Silva, V. G. Morato Genetic ancestry and income are associated with dengue hemorrhagic fever in a highly admixed population, European Journal of HumanGenetics, vol. 16, no. 6, (2008) 762–765,
[11]        M. G. Guzman, G. Kouri, Dengue haemorrhagic fever integral hypothesis: confirming observations, 1987–2007. Transactions of the Royal Society of Tropical Medicine and Hygiene, 102 (6) (2008) 522–523
[12]        P. M. Zanotto, E. A. Gould, G. F. Gao, P. H. Harvey, E. C. Holmes, Population dynamics of flaviviruses revealed by molecular phylogenies. Proceedings of the National Academy of Sciences of the United States of America, 93 (2) (1996) 548– 553.
[13]        A. S. Ndaghiya, A. I. Ogadimma, S. Sani, Modelling of some Schiff bases as anti-Salmonella typhi drugs: A QSAR approach. Journal of Computational Methods in Molecular Design, 5(4) (2015) 147-157.
[14]        S.  N.      Adawara, P. Mamza, G. A. Shallangwa, I.  Abdulkadir. Anti-Dengue potential, Molecular Docking Study of Some Chemical constituents in the leaves of Isatis tinctoria. Chemical Review and Letters, 3(3) (2020) 104-109.
[15]       J. C. Madden, M. T. Cronin, Structure-based methods for the prediction of drug metabolism. Expert opinion on drug metabolism & toxicology, 2(4) (2006) 545-557.
[16]        Y. C. Martin, Quantitative Drug Design, Marcel Dekker, New York, NY, USA, 1978.
[17]       F. Yokokawa, S. Nilar, C. G. Noble, S. P. Lim, R. Rao, S. Tania, G. Wang, G. Lee, J. Hunziker, R. Karuna, U. Manjunatha, Discovery of potent non-nucleoside inhibitors of dengue viral RNA-dependent RNA polymerase from a fragment hit using structure-based drug design. J. Medi. Chem., 59(8) (2016) 3935-52.
[18]        A. Tropsha, Best practices for QSAR model development, validation, and exploitation. Mol. Inform., 29(6–7) (2010) 476–488.
[19]        M. Abdullahi, G. A.  Shallangwa, A. Uzairu, In silico QSAR and molecular docking simulation of some novel aryl sulfonamide derivatives as inhibitors of H5N1 influenza A virus subtype. Beni-Suef University Journal of Basic and Applied Sciences, 9(1) (2020) 2.
[20]        A. D. Becke, Density‐functional thermochemistry III. The role of exact exchange. J. Chem. Phys., 98 (1993) 5648–5652.
[21]        C.           Lee, W. Yang, R. G. Parr, Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Phys. Rev., B 37(1988) 785.
[22]        A. Schäfer, C. Huber, R. Ahlrichs, Fully optimized contracted Gaussian basis sets of triple zeta valence quality for atoms Li to Kr. J. Chem. Phys., 100 (1994) 5829–5835.
[23]        C.W. Yap, PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem., 32(7) (2011) 1466–1474.
[24]        R.W. Kennard, L.A. Stone, Computer aided design of experiments. Technometrics 11(1) (1969) 137–148.
[25]        P.  Ambure, R.B. Aher, A.  Gajewicz, T. Puzyn, K. Roy, “NanoBRIDGES” software: Open access tools to performQ-SARand nano-QSAR modeling. Chemom. Intell. Lab. Syst., 147 (2015) 1–13.
[26]        N. Rob, Tutorial 6: Linear Regression, (2014) 1–14.
[27]        D. E. Arthur, A. Uzairu, P. Mamza, S. E. Abechi, G. Shallangwa, Insilco study on the toxicity of anti-cancer compounds tested against MOLT-4 and p388 cell lines using GA-MLR technique. Beni-Suef Univ. J. Basic Appl. Sci., 5 (2016b) 320–333.
[28]       A. Beheshti, E. Pourbasheer, M. Nekoei, S. Vahdani, Q-SAR modeling of antimalarial activity of urea derivatives using genetic algorithm–multiple linear regressions. J. Saudi Chem. Soc., 20 (2016) 282–290.
[29]        T. I. Netzeva, A. P. Worth, T. Aldenberg, R. Benigni, M. T. Cronin, P. Gramatica, J. S. Jaworska, S. Kahn, G. Klopman, C. A. Marchant, Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships, ATLA, 33 (2005) 155–173.
[30]        S. Dimitrov, G. Dimitrova, T. Pavlov, N. Dimitrova, G.  Patlewicz, J. Niemela, O. Mekenyan, A stepwise approach for defining the applicability domain of SAR and Q-SAR models, J. Chem. Inf. Model. 45 (2005) 839–849.
[31]        S. Alimohammadi, A. Hamidi, P. Pargolghasemi, N. Nourani, and M. S. Hoseininezhad-Namin, QSAR study of antiproliferative drug against A549 by GA-MLR and SW-MLR methods. Chemical Review and Letters, 2(4) (2019)193-198.
[32]        A. Golbraikh, A. Tropsha, Beware of q2! J. Mol. Graph. Model, 20(4) (2002) 269–276.

Files

Share

How to cite

Statistics