Reference-based chemical-genetic interaction profiling to elucidate small molecule mechanism of action in Mycobacterium tuberculosis

Abstract

In an era of increasing resistance, new and effective strategies are needed for antibiotic discovery. Whole-cell active screens yield candidate compounds lacking mechanism-of-action (MOA) information and thus do not provide biological insight for prioritization. We previously reported PROSPECT (PRimary screening Of Strains to Prioritize Expanded Chemistry and Targets), an antimicrobial discovery strategy that measures chemical-genetic interactions between small molecules and a pool of Mycobacterium tuberculosis mutants, each depleted of a different essential protein target. PROSPECT facilitates efficient hit prioritization by simultaneously identifying whole-cell active compounds with high sensitivity and providing early insights into their MOA. Here, we report a reference-based approach to infer MOA from often complex PROSPECT data. For this aim, we curated a reference set of 437 compounds with published, annotated MOA and known or suspected antitubercular activity, and applied PROSPECT to it. We then developed Perturbagen CLass (PCL) analysis, a computational method that predicts MOA by comparing chemical-genetic interaction profiles of unknown compounds to those of this reference set. In leave-one-out cross-validation, PCL analysis correctly predicted MOA with 70% sensitivity and 75% precision. When applied to 75 antitubercular leads with known MOA previously reported by GlaxoSmithKline (GSK), PCL analysis similarly achieved 69% sensitivity and 87% precision. We also analyzed 98 GSK compounds lacking MOA information, predicting 60 of them to act via a reference MOA, and followed up with functional validation of 29 compounds predicted to target respiration-related MOAs. Finally, we applied PROSPECT and PCL analysis to [~]5,000 compounds from larger unbiased libraries that had not been preselected for antitubercular activity. PCL analysis identified a novel scaffold lacking wild-type activity but predicted to inhibit respiration via QcrB, and we confirmed this prediction while chemically optimizing this scaffold to achieve wild-type activity. PCL analysis of PROSPECT data thus enables rapid MOA assignment and hit prioritization, advancing the discovery of new, potent antitubercular compounds.