Computational Modeling and Molecular dynamics Simulations of Thiazolino 2-pyridone amide analog compounds as Chlamydia trachomatis inhibitor

Document Type : Research Article

Authors

1 Department of Pure and Applied Chemistry, University of Maiduguri, Borno State, Nigeria

2 Department of Chemistry, Ahmadu Bello University, Zaria, Nigeria

Abstract

Computer-aided drug screening by 2D-QSAR, CoMFA, molecular docking, and molecular dynamics (MD) simulation may provide an effective approach to identifying promising drug repurposing candidates for Chlamydia trachomatis treatment. In this analysis, molecular descriptors were used to achieve a statistically momentous 2D-QSAR model (R2 = 0.637; Q2 = 0.5388). The 2D-QSAR model’s robustness was considered by the internal leave-one-out cross-validated regression coefficient values (Q2) and the training set values [(r^2-r0^2)/r^2]. Between the experimental and predicted pIC50 value, the overall standard deviation error of prediction (SDEP) was 0.2448, showing strong 2D-QSAR model predictability. The QSAR model was able to systematically predict anti-bacterial behavior with an R2pred value of 0.506 for the external data set 9 of the thiazolino 2-pyridone amide derivative. Comparative molecular field analysis (CoMFA (FFDSEL) Q2LOO = 0.238, R2 = 0.943) and CoMFA (UVEPLS) (Q2LOO = 0.553, R2 = 0.943) were used. CoMFA (UVEPLS) had strong certification and prediction capabilities. We analyzed the binding effect of the derivatives, where compounds 29 and 31 have the least binding energy. Compounds 29 and 31 interact with main active site residues, including Glu154, Leu142, His87, Arg150, Phe151, Asn138, Gly141, His88, Ile137, Cys85 and 145, respectively, through the binding interaction modes of the molecular docking inhibitor sequence. Further molecular dynamics simulations (MD) were performed on both compounds, and their potential binding modes were explored. Glu154, Phe151, Arg150, Asn138, Gly141, Cys145, and Ile137 have been found to play a key role in stabilizing inhibitors. Besides, the prediction of a golden triangle for the series was carried out. The findings will provide useful guidance in the future for the design of new inhibitors of Chlamydia trachomatis.

Keywords

Main Subjects

References

[1] S. Marwaha, H. Uvell, O. Salin, A. E. G. Lindgren, J. Silver, M. Elofsson, A. Gylfe, N-acylated Derivatives of Sulfamethoxazole and Sulfafurazole Inhibit Intracellular Growth of Chlamydia trachomatis. Antimicrobial Agents and Chemotherapy 58(5) (2014) 2968–2971.
[2] B.E. Batteiger, “Chlamydia infection and epidemiology,” in Intracellular Pathogens I: Chlamydiales, eds M. Tan, and P. Bavoil (Washington, DC: ASMpress), 1–26 (2012).
[3] P. Engström, S. Krishnan, B.D. Ngyuen, E. Chorell, J. Normark, J. Silver, R.J. Bastidas, M.D. Welch, S.J. Hultgren, H. Wolf-Watz, R.H. Valdivia, F. Almqvist, S. Bergström, A 2-pyridone-amide inhibitor targets the glucose metabolism pathway of Chlamydia trachomatis. mBio 6(1):e02304-14 (2015). doi:10.1128/mBio.02304-14.
[4] WHO (2018). Trachoma: Fact Sheet. World Health Organization. Available online at http://www.who.int/news-room/fact-sheets/detail/trachoma (Accessed September 30, 2020).
[5] L. Newman, J. Rowley, S. Vander Hoorn, N. S. Wijesooriya, M. Unemo, N. Low, G. Steven, S. Gottlieb, J. Kiarie, M. Temmerman, Global estimates of the prevalence and incidence of four curable sexually transmitted infections in 2012 based on systematic review and global reporting. PLoS ONE (2015) Dec 8;10(12):e0143304.
[6] A. Balupuri, P.K. Balasubramanian, S. J. Cho. 3D-QSAR, docking, molecular dynamics simulation and free energy calculation studies of some pyrimidine derivatives as novel JAK3 inhibitors. Arabian Journal of Chemistry 13, (2020) 1052–1078.
[7] K. Zitouni, S. Belaidi, A. Kerassa, Conformational analysis and QSAR modeling 14-membered macrolide analogues against mycobacterium tuberculosis. J Fundam Appl Sci., 12(3), (2020) 1035-1066.
[8] M.K. Dahlgren, C. E. Zetterström, A. Gylfe, A. Linusson, M. Elofsson. Statistical molecular design of a focused salicylidene acylhydrazide library and multivariate QSAR of inhibition of type III secretion in the Gram-negative bacterium Yersinia. Bioorg. Med. Chem. 18 (2010) 2686–2703. [9] S.A. Mojica, A.U. Eriksson, R.A. Davis, W. Bahnan, M. Elofsson, A. Gylfe, Red Fluorescent Chlamydia trachomatis Applied to Live Cell Imaging and Screening for Antibacterial Agents. Front. Microbiol. 9 (2018) 3151. doi: 10.3389/fmicb.2018.0315.
[10] C. A. Ison, Antimicrobial resistance in sexually transmitted infections in the developed world: implications for rational treatment. Curr. Opin. Infect. Dis. 25 (2012) 73–78.
[11] E. Bojang, J. Jafali, V. Perreten, J. Hart, E.M. Harding-Esch, A. Sillah, D.C.W. Mabey, M.J. Holland, R.L. Bailey, A. Roca, S.E. Burr, Short-term increase in prevalence of nasopharyngeal carriage of macrolide-resistant Staphylococcus aureus following mass drug administration with azithromycin for trachoma control. BMC Microbiol. 17 (2017) 75.
[12] M. R. Hammerschlag, S. A. Kohlhoff, Treatment of chlamydial infections. Expert Opin. Pharmacother. 13, 545–552 . (2012).
[13] C.W. Yap, PaDEL‐descriptor: An open-source software to calculate molecular descriptors and fingerprints. Journal of Computational Chemistry, 32 (7) 1466-1474 (2011).
[14] P. Tosco, T. Balle, F. Shiri, Open3DALIGN: an open-source software aimed atunsupervised ligand alignment, J. Comput. Aided Mol. Des. 25 777–783 (2011).
[15] P. Tosco, T. Balle, Open3DQSAR: a new open-source software aimed athigh-throughput chemometric analysis of molecular interaction fields, J. Mol.Model. 17 201–208 (2011).
[16] M. Pastor, G. Cruciani, S. Clementi, Smart region definition: a new way toimprove the predictive ability and interpretability of three-dimensional quantitative structure-activity relationships, J. Med. Chem. 40 1455–1464 (1997).
[17] S.C. Massimo Baroni, Gabriele Costantino, Gabriele Cruciani, Daniela Riganelli,Roberta Valigi, Generating optimal linear PLS estimations (GOLPE): anadvanced chemometric tool for handling 3D-QSAR problems, Quant. Struct. Relat. 12 9–20 (1993).
[18] S. Wold, M. Sjöström, L. Eriksson, PLS-regression: a basic tool ofchemometrics, Chemometr. Intell. Lab. Syst. 58 (2001) 109–130.
[19] G. Morris, R. Huey, AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility, J .Comput. Chem. 30 (2009) 2785–2791.
[20] W. Humphrey, A. Dalke, K. Schulten, VMD: Visual Molecular Dynamics. J Mol Graph 7855 (1996) 33–38.
[21] J.C. Phillips, R. Braun, W. Wang, J. Gumbart, E. Tajkhorshid, E. Villa, C. Chipot, R.D. Skeel, L. Kale, K Schulten, Scalable molecular dynamics with NAMD. J. Comput. Chem. 26 (2005) 1781–1802, doi.org/10.1002/jcc.20289. A.D. MacKerell, D. Bashford, M. Bellott, R.L. Dunbrack, J.D. Evanseck, M.J. Field, S.
[22] J. Fischer, H. Gao, S. Guo, D. Ha, L. Joseph-McCarthy, L. Kuchnir, K. Kuczera, F.T. Lau, C. Mattos, S. Michnick, T. Ngo, D.T. Nguyen, B. Prodhom, W.E. Reiher, B. Roux, M. Schlenkrich, J.C. Smith, R. Stote, J. Straub, M. Watanabe, J. Wiórkiewicz-Kuczera, D. Yin, M. Karplus, All atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B. 102(18) (1998) 3586-616.
[23] A.K.L. Wong, A. M. Goscinski, A VMD plugin for NAMD simulations on Amazon EC2. Procedia Computer Science, 9, (2012) 136-145.
[24] A. K. L. Wong, A.M. Goscinski, The design and implementation of the VMD plugin for NAMD simulations on the Amazon cloud. International Journal of Cloud Computing and Services Science, 1(4), (2012) 155.
[25] A. Golbraikh, A. Tropsha, Beware of q2! J Mol Graph Model. 20 (2002) 269-276. [26] S.E. Abechi, and E.I. Edache, Application of genetic algorithm-multiple linear regression (GA-MLR) for prediction of anti-fungal activity. International Journal of Pharma Sciences and Research (IJPSR) 7 (2016) 204-220.
[27] P.P. Roy, K. Roy, On some aspects of variable selection for partial least squares regression models. QSAR Comb Sci. 27 (2008) 302-313
[28] P. Gramatica and E. Papa, Screening and Ranking of POPs for Global Half-Life: QSAR Approaches for Prioritization Based on Molecular Structure. Environ. Sci. Technol. 41, (2007) 2833-2839. doi.org/10.1021/es061773b.
[29] S. Cassani, P. Gramatica, Identification of potential PBT behavior of personal care products by structural approaches. Sustainable Chemistry and Pharmacy 1 (2015) 19-27.
[30] E.I. Edache, A.J. Uttu, A. Oluwaseye, H. Samuel, and A. Abduljelil, A Semi-empirical based QSAR study of indole 𝜷- Diketo acid, Diketo acid and Carboxamide Derivatives as potent HIV-1 agent Using Quantum Chemical descriptors. IOSR Journal of Applied Chemistry (IOSR-JAC), 8 (2015) 12-20
[31] S. Shapiro, B. Guggenheim, Inhibition of oral bacteria by phenolic compounds: Part 1. QSAR analysis using molecular connectivity. Quant. Struct. Act Relat. 17 (1998) 327-337.
[32] B. Wendt, R.D. Cramer, Challenging the gold standard for 3DQSAR: template CoMFA versus X-ray alignment. J Comput Aided Mol Des 28(8) (2014) 803–824.
[33] I. Muegge, S. L. Heald, and D. Brittelli, Simple Selection Criteria for Drug-like Chemical Matter. Journal of Medicinal Chemistry 44 (12) (2001) 1841-1846.
[34] T.W. Johnson, K.R. Dress, M. Edwards, Using the Golden Triangle to optimize clearance and oral absorption. Bioorg. Med. Chem. Lett. 19 (2009) 5560–5564.
Journal of Chemistry Letters
Volume 1, Issue 3 - Serial Number 3
November 2020
Pages 123-138

Files

Share

How to cite

Statistics