Synthetic and In Silico strategies toward DprE1 inhibitors: Advancing antitubercular therapies
Abstract
Tuberculosis (TB) caused by Mycobacterium tuberculosis (Mtb) continues to pose a major global health threat, particularly with the rise of multidrug- and extensively drug-resistant strains. This scenario highlights the urgent demand for novel therapeutic agents targeting essential pathways of Mtb survival. Decaprenyl-phosphoryl-β-D-ribose 2′-epimerase (DprE1), a key flavoenzyme involved in the biosynthesis of arabinogalactan and lipoarabinomannan—critical components of the mycobacterial cell wall—has emerged as one of the most promising drug targets. The discovery of DprE1 inhibitors has been significantly accelerated by in silico approaches, including structure-based and ligand-based virtual screening, molecular docking, and pharmacophore modelling, which allow rapid identification and prioritization of potential scaffolds. Furthermore, molecular dynamics (MD) simulations and free-energy calculations (e.g., MM/PBSA, MM/GBSA) have been instrumental in understanding the thermodynamic stability, binding affinity, and conformational dynamics of DprE1–inhibitor complexes. Quantum mechanical studies such as density functional theory (DFT) further provide insights into electronic properties, reactivity, and covalent inhibition mechanisms of nitroaromatic inhibitors. Collectively, these computational strategies have facilitated the rational design and optimization of covalent and non-covalent DprE1 inhibitors with improved potency and selectivity. This review highlights the crucial…
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.