Comprehensive exploration of chemical space using trisubstituted carboranes.
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ABSTRACT:
SUBMITTER: Asawa Y
PROVIDER: S-EPMC8677773 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Tuberculosis (TB), entrained by Mycobacterium tuberculosis, continues to be an enfeebling disease, killing nearly 1.5 million people in 2019, with 2 billion people worldwide affected by latent TB. The multidrug-resistant and totally drug-resistant emerging strains further exacerbate the TB infection. The cell wall of bacteria provides critical virulence components such as cell surface proteins, regulators, signal transduction proteins, and toxins. The cell wall biosynthesis pathway of Mycobacterium tuberculosis is exhaustively studied to discover novel drug targets. Decaprenylphosphoryl-β-d-ribose-2'-epimerase (DprE1) is an important enzyme involved in the arabinogalactan biosynthetic pathway of Mycobacterium tuberculosis cell wall and is essential for both latent and persistent bacterial infection. We analyzed all known ∼1300 DprE1 inhibitors to gain deep insights into the chemogenomic space of DprE1-ligand complexes. Physicochemical descriptors of the DprE1 inhibitors showed a marked lipophilic character forming a cluster distinct from the existing TB drugs, as revealed by the principal component analysis. Similarity analysis using Murcko scaffolds and rubber band scaling revealed scarce representation of the chemical space. Further, Murcko scaffold analysis uncovered favorable and unfavorable scaffolds, where benzo and pyridine-based core scaffolds exhibit the highest biological activity, as evidenced by their MIC and IC50 values. Automatic SAR and R-group decomposition analysis resulted in the identification of substructures responsible for the inhibitory activity of the DprE1 enzyme. Further, with activity cliff analysis, we observed prominent discontinuity in the SAR of DprE1 inhibitors, where even simple structural modification in the chemical scaffold resulted in significant potency difference, presumably due to the binding orientation and interaction in the active site. Thiophene, 6-membered aromatic rings, and unsubstituted benzene ring-based toxicophores were identified in the DprE1 chemical space using an artificial intelligence approach based on inductive logic programming. This paper, hence, ushers in new insights for the design and development of potent covalent and non-covalent DprE1 inhibitors and guides hit and lead optimization for the development of non-hazardous small molecule therapeutics for Mycobacterium tuberculosis.
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Project description:Drug discovery can be thought of as a search for a needle in a haystack: searching through a large chemical space for the most active compounds. Computational techniques can narrow the search space for experimental follow up, but even they become unaffordable when evaluating large numbers of molecules. Therefore, machine learning (ML) strategies are being developed as computationally cheaper complementary techniques for navigating and triaging large chemical libraries. Here, we explore how an active learning protocol can be combined with first-principles based alchemical free energy calculations to identify high affinity phosphodiesterase 2 (PDE2) inhibitors. We first calibrate the procedure using a set of experimentally characterized PDE2 binders. The optimized protocol is then used prospectively on a large chemical library to navigate toward potent inhibitors. In the active learning cycle, at every iteration a small fraction of compounds is probed by alchemical calculations and the obtained affinities are used to train ML models. With successive rounds, high affinity binders are identified by explicitly evaluating only a small subset of compounds in a large chemical library, thus providing an efficient protocol that robustly identifies a large fraction of true positives.
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