Drug discovery process is usually focused on a single protein target, and in this process, existing compounds that bind to related proteins are often ignored. We have developed computational tools – ProBiS tools - which enable drug discovery based on protein structures (ProBiS and ProBiS-CHARMMing servers, plugin, and algorithm) as well as ligand structures (LiSiCA plugin and algorithm). We review the most recent example applications of these tools in the early drug discovery process, where they facilitated the discovery of new active compounds. Using ProBiS plugin, we predicted ligands of the InhA query protein, an enoyl reductase enzyme from the Mycobacterium tuberculosis fatty acid biosynthesis pathway, and assessed their inhibitory activity experimentally, resulting in three previously unrecognized inhibitors with novel scaffolds. With ProBiS-CHARMMing, we simulated induced-fit effect upon ligand binding to the Escherichia coli MurA enzyme that catalyzes the first step of bacterial peptidoglycan synthesis, which enabled us to significantly improve docking to this enzyme, and discover three active compounds with promising inhibitory potencies. We applied LiSiCA algorithm to the Toll-like receptor 7, a transmembrane protein which plays a crucial role in the recognition of single-stranded viral RNA and in the subsequent activation of innate immunity, and we discovered novel agonists with higher potencies than known drug imiquimod. Our ProBiS tools are freely available and can be accessed from our new united website at http://insilab.org.

New approaches for binding site and ligand prediction and their use in drug discovery

253rd National Meeting (2017)

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