Pharmacophore screening, denovo designing, retrosynthetic analysis, and combinatorial synthesis of a novel lead VTRA1.1 against RecA protein of Acinetobacter baumannii

Abstract

Antibiotics and disinfectants resistance is acquired by activating RecA-mediated DNA repair, which maintains ROS-dependent DNA damage caused by the antimicrobial molecules. To increase the efficacy of different antimicrobials, an inhibitor can be developed against RecA protein. The present study aims to design a denovo inhibitor against RecA protein of Acinetobacter baumannii.Pharmacophore-based screening, molecular mechanics, molecular dynamics simulation (MDS), retrosynthetic analysis, and combinatorial synthesis were used to design lead VTRA1.1 against RecA of A. baumannii. Pharmacophore models (structure-based and ligand-based) were created, and a phase library of FDA-approved drugs was prepared. Screening of the phase library against these pharmacophore models selected thirteen lead molecules. These filtered leads were used for the denovo fragment-based design, which produced 253 combinations. These designed molecules were further analyzed for its interaction with active site of RecA that selected a hybrid VTRA1. Further, retrosynthetic analysis and combinatorial synthesis produced 1000 analogs of VTRA1 by more than 100 modifications. These analogs were used for XP docking, binding free energy calculation, and MDS analysis which finally select lead VTRA1.1 against RecA protein. Further, mutations at the interacting residues of RecA with VTRA1.1, alter the unfolding rate of RecA, which suggests the binding of VTRA1.1 to these residues may alter the stability of RecA. It is also found that VTRA1.1 had reduced interaction of RecA with LexA and ssDNA polydT, showing the lead's efficacy in controlling the SOS response. Further, it was also observed that VTRA1.1 does not contain any predicted human off-targets and no cytotoxicity to cell lines.As functional RecA is involved in antimicrobial resistance, denovo designed lead VTRA1.1 against RecA may be further developed as a significant combination for therapeutic uses against A. baumannii.

Applications of machine learning in computer-aided drug discovery

“…KDEEP is a highly successful program and has been used as a tool in numerous studies. A few examples where KDEEP has been successfully utilized are: to engineer maltooligosaccharide-forming amylases and optimize the biosynthesis of maltooligosaccharides (Ding et al, 2022), to determine the inhibitory properties of antiviral drugs and their structural analogues towards corona virus (Kalamatianos, 2022), during virtual screening (of lead compounds and analogs) to chemically inhibit processes controlled by RecA protein of Acinetobacter baumannii (Tiwari, 2022) etc. While KDEEP is a versatile SF for binding affinity predictions, the authors argued that additional exploration of the network architecture and a thorough featurization of the molecular descriptor could further improve the SF's performance.…”