Dynamic ligand-based pharmacophore modeling and virtual screening to identify mycobacterial cyclopropane synthase inhibitors

Abstract

Multidrug resistance in Mycobacterium tuberculosis (M. Tb) and its coexistence with HIV are the biggest therapeutic challenges in anti-M. Tb drug discovery. The current study reports a Virtual Screening (VS) strategy to identify potential inhibitors of Mycobacterial cyclopropane synthase (CmaA1), an important M. Tb target considering the above challenges. Five ligand-based pharmacophore models were generated from 40 different conformations of the cofactors of CmaA1 taken from molecular dynamics (MD) simulations trajectories of CmaA1. The screening abilities of these models were validated by screening 23 inhibitors and 1398 non-inhibitors of CmaA1. A VS protocol was designed with four levels of screening i.e., ligand-based pharmacophore screening, structure-based pharmacophore screening, docking and absorption, distribution, metabolism, excretion and the toxicity (ADMET) filters. In an attempt towards repurposing the existing drugs to inhibit CmaA1, 6,429 drugs reported in DrugBank were considered for screening. To find compounds that inhibit multiple targets of M. Tb as well as HIV, we also chose 701 and 11,109 compounds showing activity below 1 μM range on M. Tb and HIV cell lines, respectively, collected from ChEMBL database. Thus, a total of 18,239 compounds were screened against CmaA1, and 12 compounds were identified as potential hits for CmaA1 at the end of the fourth step. Detailed analysis of the structures revealed these compounds to interact with key active site residues of CmaA1.

Molecular Property Diagnostic Suite for COVID-19 (MPDSCOVID-19): An open access disease specific drug discovery portal

“…Besides, here we provide a brief description of the selected data analysis tools that may be of high practical utility for the people desirous of using our web portal. The modules include a) Pharmacophore (Choudhury et al, 2016), b) Scaffold analysis, c) Active site analysis, d) Docking, e) Screening, f) Drug repurposing tool (Lagunin et al, 2000), g) Virtual screening, h) Visualization, i) Sequence alignment, and the advanced module contains various machine learning tools along with the inhouse developed machine learning antiviral prediction model (John et al, 2022). The disease independent modules are depicted in Fig.…”

Structure-based drug repurposing: Traditional and advanced AI/ML-aided methods

“…These methods mostly comprise high-throughput VS 6 of the RCS using molecular docking and/or pharmacophore models. [7] , [8] …”

Fragment tailoring strategy to design novel chemical entities as potential binders of novel corona virus main protease

“…However, successful discovery of NCEs will hugely depend on proper understanding of the structure, interactions and dynamics of validated targets and the unexplored potential of their binding sites to bind new chemotypes (Boopathi et al, 2020). Computational methods have become indispensable for infectious disease drug discovery in last few decades (Njogu et al, 2016) not only to understand the drug-target interactions (Choudhury et al, 2014;Njogu et al, 2016;Schuler et al, 2017) and delineate the structure activity relationship of small druglike molecules (Gahtori et al, 2019;Srivastava et al, 2012), but also for screening huge chemical libraries providing a fast and less expensive alternative to the traditional high throughput screening (Choudhury et al, 2015(Choudhury et al, , 2016Murgueitio et al, 2012). The recent literature reports several interesting computational approaches including computational drug repurposing on the TMPRSS2 (Elmezayen et al, 2020), reverse vaccinology (Hasan et al, 2019), in silico screening of novel guanosine derivatives against MERS CoV polymerase ( (Elfiky & Azzam 2020, Elfiky, 2020a, ayurvedic anti-tussive medicines, anti-viral phytochemicals and synthetic anti-virals against SARS-CoV-2 M Pro , ACE-2 and RNA dependent RNA polymerase (RdRp) (Joshi et al, 2020;Elfiky, 2020b), in silico investigation of natural product compounds against the substrate-binding domain b of cell-surface heat shock protein A5, which reported to be the recognition site for the SARS-CoV-2 spike (Elfiky, 2020), in silico study of binding potency of different Saikosaponins with targets NSP15 and fusion spike glycoprotein (Sinha et al, 2020) and computational evaluation of stilbene based compounds such as resveratrol, as anti-COVID-19 drug candidates acting through disruption of the spike proteins (Wahedi et al, 2020).…”