Exploring the inhibitory mechanisms of indazole compounds against SAH/MTAN-mediated quorum sensing utilizing QSAR and docking
DOI:
https://doi.org/10.33393/dti.2022.2512Keywords:
Antimicrobial resistance, Indazole compounds, Molecular docking, QSAR, Quorum sensing,, SAH/MTANAbstract
The world is under the great threat of antimicrobial resistance (AMR) leading to premature deaths. Microorganisms can produce AMR via quorum sensing mechanisms utilizing S-adenosyl homocysteine/methylthioadenosine nucleosidase (SAH/MTAN) biosynthesis. But there is no specific drug developed to date to stop SAH/MTAN, which is a crucial target for the discovery of anti-quorum sensing compound. It has been shown that indazole compounds cause inhibition of SAH/MTAN-mediated quorum sensing, but the biochemical mechanisms have not yet been explored. Therefore, in this original research, an attempt has been made to explore essential structural features of these compounds by quantitative structure-activity relationship (QSAR) and molecular docking of indazole compounds having inhibition of SAH/MTAN-mediated quorum sensing. The validated QSAR predicted five essential descriptors and molecular docking helps to identify the active binding amino acid residues involved in ligand-receptor interactions that are responsible for producing the quorum sensing inhibitory mechanisms of indazole compounds against SAH/MTAN-mediated AMR.
Downloads
References
Whiteley M, Diggle SP, Greenberg EP. Progress in and promise of bacterial quorum sensing research. Nature. 2017;551(7680):313-320. https://doi.org/10.1038/nature24624 PMID:29144467 DOI: https://doi.org/10.1038/nature24624
Jiang Q, Chen J, Yang C, Yin Y, Yao K. Quorum Sensing: A Prospective Therapeutic Target for Bacterial Diseases. BioMed Res Int. 2019;2019:2015978. https://doi.org/10.1155/2019/2015978PMID:31080810 DOI: https://doi.org/10.1155/2019/2015978
Moré MI, Finger LD, Stryker JL, Fuqua C, Eberhard A, Winans SC. Enzymatic synthesis of a quorum-sensing autoinducer through use of defined substrates. Science. 1996;272(5268):1655-1658. https://doi.org/10.1126/science.272.5268.1655 PMID:8658141 DOI: https://doi.org/10.1126/science.272.5268.1655
Nandi S. Recent Advances in Ligand and Structure Based Screening of Potent Quorum Sensing Inhibitors Against Antibiotic Resistance Induced Bacterial Virulence. Recent Pat Biotechnol. 2016;10(2):195-216. https://doi.org/10.2174/1872208310666160728104450 PMID:27468815 DOI: https://doi.org/10.2174/1872208310666160728104450
Parveen N, Cornell KA. Methylthioadenosine/S-adenosylhomocysteine nucleosidase, a critical enzyme for bacterial metabolism. Mol Microbiol. 2011;79(1):7-20. https://doi.org/10.1111/j.1365-2958.2010.07455.x PMID:21166890 DOI: https://doi.org/10.1111/j.1365-2958.2010.07455.x
Kumar M, Saxena M, Saxena AK, Nandi S. Recent Breakthroughs in Various Antimicrobial Resistance Induced Quorum Sensing Biosynthetic Pathway Mediated Targets and Design of their Inhibitors. Comb Chem High Throughput Screen. 2020;23(6):458-476. https://doi.org/10.2174/1386207323666200425205808 PMID:32334498 DOI: https://doi.org/10.2174/1386207323666200425205808
Schramm VL. Methods and compositions for treatingbacterial infections by inhibiting quorum sensing.US20110190265;2011. https://patents.google.com/patent/US20110190265A1/en
Tedder ME, Nie Z, Margosiak S, et al. Structure-based design, synthesis, and antimicrobial activity of purine derived SAH/MTA nucleosidase inhibitors. Bioorg Med Chem Lett. 2004;14(12):3165-3168. https://doi.org/10.1016/j.bmcl.2004.04.006 PMID:15149667 DOI: https://doi.org/10.1016/j.bmcl.2004.04.006
Li X, Chu S, Feher VA, et al. Structure-based design, synthesis, and antimicrobial activity of indazole-derived SAH/MTA nucleosidase inhibitors. J Med Chem. 2003;46(26):5663-5673. https://doi.org/10.1021/jm0302039 PMID:14667220 DOI: https://doi.org/10.1021/jm0302039
Halgren TA. Merck molecular force field. III. Molecular geometries and vibrational frequencies for MMFF94. J Comput Chem. 1996;17(5-6):553-586. https://doi.org/10.1002/(SICI)1096-987X(199604)17:5/6<553::AID-JCC3>3.0.CO;2-T DOI: https://doi.org/10.1002/(SICI)1096-987X(199604)17:5/6<553::AID-JCC3>3.0.CO;2-T
Mills N. ChemDraw ultra 10.0. J Am Chem Soc. 2006;128(41):13649-13650. https://doi.org/10.1021/ja0697875 DOI: https://doi.org/10.1021/ja0697875
Yap CW. PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem. 2011;32(7):1466-1474. https://doi.org/10.1002/jcc.21707PMID:21425294 DOI: https://doi.org/10.1002/jcc.21707
Ballabio D, Consonni V, Mauri A, Claeys-Bruno M, Sergent M, Todeschini R. A novel variable reduction method adapted from space-filling designs. Chemom Intell Lab Syst. 2014;136:147-154. https://doi.org/10.1016/j.chemolab.2014.05.010 DOI: https://doi.org/10.1016/j.chemolab.2014.05.010
Hoffman BT, Kopajtic T, Katz JL, Newman AH. 2D QSAR modeling and preliminary database searching for dopamine transporter inhibitors using genetic algorithm variable selection of Molconn Z descriptors. J Med Chem. 2000;43(22):4151-4159. https://doi.org/10.1021/jm990472s PMID:11063611 DOI: https://doi.org/10.1021/jm990472s
de Campos LJ, de Melo EB. Modeling structure-activity relationships of prodiginines with antimalarial activity using GA/MLR and OPS/PLS. J Mol Graph Model. 2014;54:19-31. https://doi.org/10.1016/j.jmgm.2014.08.004 PMID:25244636 DOI: https://doi.org/10.1016/j.jmgm.2014.08.004
Akaike H. Fitting autoregressive models for prediction. Ann Inst Stat Math. 1969;21(1):243-247. https://doi.org/10.1007/BF02532251 DOI: https://doi.org/10.1007/BF02532251
Ambure P, Aher RB, Gajewicz A, Puzyn T, Roy K. “NanoBRIDGES” software: open access tools to perform QSAR and nano-QSAR modeling. Chemom Intell Lab Syst. 2015;147:1-13. https://doi.org/10.1016/j.chemolab.2015.07.007 DOI: https://doi.org/10.1016/j.chemolab.2015.07.007
Golbraikh A, Tropsha A. Beware of q2! J Mol Graph Model. 2002;20(4):269-276. https://doi.org/10.1016/S1093-3263(01)00123-1 PMID:11858635 DOI: https://doi.org/10.1016/S1093-3263(01)00123-1
Roy K, Kar S, Ambure P. On a simple approach for determining applicability domain of QSAR models. Chemom Intell Lab Syst. 2015;145:22-29. https://doi.org/10.1016/j.chemolab.2015.04.013 DOI: https://doi.org/10.1016/j.chemolab.2015.04.013
Stahl M, Rarey M. Detailed analysis of scoring functions for virtual screening. J Med Chem. 2001;44(7):1035-1042. https://doi.org/10.1021/jm0003992 PMID:11297450 DOI: https://doi.org/10.1021/jm0003992
Nandi S, Bagchi MC. 3D-QSAR and molecular docking studies of 4-anilinoquinazoline derivatives: a rational approach to anticancer drug design. Mol Divers. 2010;14(1):27-38. https://doi.org/10.1007/s11030-009-9137-9 PMID:19330460 DOI: https://doi.org/10.1007/s11030-009-9137-9
Lee JE, Cornell KA, Riscoe MK, Howell PL. Structure of E. coli 5′-methylthioadenosine/S-adenosylhomocysteine nucleosidase reveals similarity to the purine nucleoside phosphorylases. Structure. 2001;9(10):941-953. https://doi.org/10.1016/S0969-2126(01)00656-6 PMID:11591349 DOI: https://doi.org/10.1016/S0969-2126(01)00656-6
Dey R, Nandi S, Samadder A. “Pelargonidin mediated selective activation of p53 and parp proteins in preventing food additive induced genotoxicity: an in vivo coupled in silico molecular docking study”. Eur J Pharm Sci. 2021;156:105586. https://doi.org/10.1016/j.ejps.2020.105586PMID:33039567 DOI: https://doi.org/10.1016/j.ejps.2020.105586
Nandi S, Naaz A, Saxena M. Repurposing of Potent Mtase Inhibitors Against ZIKV Utilizing Structure-Based Molecular Docking. International Journal of Quantitative Structure-Property Relationships. 2020;5(4):53-68. https://doi.org/10.4018/IJQSPR.2020100103 DOI: https://doi.org/10.4018/IJQSPR.2020100103
Nandi S, Kumar M, Saxena M, Saxena AK. The Antiviral and Antimalarial Drug Repurposing in Quest of Chemotherapeutics to Combat COVID-19 Utilizing Structure-Based Molecular Docking. Comb Chem High Throughput Screen. 2021;24(7):1055-1068. https://doi.org/10.2174/1386207323999200824115536 PMID:32838713 DOI: https://doi.org/10.2174/1386207323999200824115536
Thompson MA, Zerner MC. A theoretical examination of the electronic structure and spectroscopy of the photosynthetic reaction center from Rhodopseudomonasviridis. J Am Chem Soc. 1991;113(22):8210-8215. https://doi.org/10.1021/ja00022a003 DOI: https://doi.org/10.1021/ja00022a003
Golbraikh A, Tropsha A. Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. J Comput Aided Mol Des. 2002;16(5-6):357-369. https://doi.org/10.1023/A:1020869118689 PMID:12489684 DOI: https://doi.org/10.1023/A:1020869118689
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 Model. 2012;52(2):396-408. https://doi.org/10.1021/ci200520g PMID:22201416 DOI: https://doi.org/10.1021/ci200520g
Jaworska J, Nikolova-Jeliazkova N, Aldenberg T. QSAR applicabilty domain estimation by projection of the training set descriptor space: a review. Altern Lab Anim. 2005;33(5):445-459. https://doi.org/10.1177/026119290503300508 PMID:16268757 DOI: https://doi.org/10.1177/026119290503300508
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2022 The Authors
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Accepted 2022-11-28
Published 2022-12-22