Mechanistic modelling ofMycobacterium Tuberculosisbeta carbonic anhydrase 3 inhibitors

This study demonstrated a novel bioinformatics pipeline to unravel a mechanistic understanding of the physiological and biochemical mechanism of Mycobacterium tuberculosis β-carbonic anhydrase 3, and its role in the key biological processes and metabolic pathways associated with its pathogenesis cycle. We initiated the study by constructing the 3D protein structure of β-CA3 from its sequence with the aid of homology modeling. To thoroughly investigate the essential properties of β-CA3, we further conducted computational genome sequencing, and proteomic analysis to reveal the key biological and structural information. The construction and development of a Python-based custom web server, SEQEclipse ( https://seqeclipse.streamlit.app/ ), which is equipped with integrated work packages, was performed to study and gain insights into the sequence structure-function relationship of β-CA3 through analysis of its physicochemical properties, identification of phosphorylated sites and disordered regions, determination of the functional class of the sequence, BLAST and MSA analysis, determination of the protein-protein interaction mechanism, and development of an ab initio modeling strategy. We also employed a quantitative structural activity relationship (QSAR) model to understand the inhibition mechanism of small molecule modulators of β-CA3 concerning their diverse physiochemical properties. We further extended the QSAR strategy to mechanistic systems biology approach to unravel the key biological processes and metabolic pathways associated with the top β-CA3 modulators and their underlying mechanism of action to halt the pathogenesis cycle of Mtb. Our study outlines the biological significance of β-CA3 functionality and provides a mechanistic understanding of phenotype-based design of small molecule modulators as anti-TB drugs for future prospects. Highlights Homology modeling was applied to decode the intricate dual-domain architecture of β-CA3, which is crucial for pioneering targeted therapies against Mycobacterium tuberculosis . SEQEclipse, a Python-based web server, was developed to centralize bioinformatics analyses, enhancing the drug design workflow. Machine learning QSAR models were utilized to pinpoint potent compounds targeting β-CA3, with descriptors such as ‘nHeavyAtom’ Atsm1, Atsm2, ‘apol’, ‘SubFP287’, ‘SubFP184’, ‘SubFP214’, and ‘SubFP275, showing high correlation and interaction potential. Systems biology was used to illustrate how identified active and inactive compounds interfere with essential metabolic pathways in Mycobacterium tuberculosis , suggesting novel therapeutic approaches. The need for validation of computational findings to confirm the efficacy and safety of identified compounds has been emphasized.