Mechanistic modelling of Mycobacterium Tuberculosis beta carbonic anhydrase 3 inhibitors
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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
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Homology modeling was applied to decode the intricate dual-domain architecture of β-CA3, which is crucial for pioneering targeted therapies against Mycobacterium tuberculosis .
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SEQEclipse, a Python-based web server, was developed to centralize bioinformatics analyses, enhancing the drug design workflow.
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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.
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Systems biology was used to illustrate how identified active and inactive compounds interfere with essential metabolic pathways in Mycobacterium tuberculosis , suggesting novel therapeutic approaches.
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The need for validation of computational findings to confirm the efficacy and safety of identified compounds has been emphasized.
