Date and Time of Event: 28 February 2024, 7 – 8 PM IST

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Speaker Name: Dr. Sourav Chowdhury

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Speaker Affiliation: Senior Post-Doctoral Research Associate (Evolutionary Biophysics), Harvard University, USA

Mode: Online


Antibiotic resistance is a worldwide challenge. A potential approach to address this problem is to analyse the evolutionary trajectory of the evolving bacterial population and develop strategies that simultaneously inhibit wild type (WT) and mutational escape variants of the target bacterial protein. We deployed an integrated multi-scale approach encompassing computational and experimental methods to discover compounds that inhibit both WT and trimethoprim (TMP) resistant mutants of E. coli dihydrofolate reductase (DHFR). We identified a novel compound that inhibits WT DHFR and its TMP resistant escape variants thereby constraining evolvability. Interestingly this novel compound had a second (non-DHFR) cellular target. Elucidating intracellular drug targets has been a difficult problem. While machine learning analysis of omics data has been a promising approach, going from large-scale trends to specific targets remains a challenge. We developed a hierarchic workflow to narrow in on specific targets based on analysis of metabolomics data and growth rescue experiments. We analysed global metabolomics utilizing machine learning, metabolic modelling, and protein structural similarity to prioritize candidate drug targets and identified the off target using experiments. As interest in ‘white-box’ machine learning methods continues to grow, we demonstrate how established machine learning methods can be combined with mechanistic analyses to improve the resolution of drug target finding workflows in general.