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Machine learning approaches to optimize small-molecule inhibitors for RNA targeting.

ABSTRACT: In the era of data science, data-driven algorithms have emerged as powerful platforms that can consolidate bioisosteric rules for preferential modifications on small molecules with a common molecular scaffold. Here we present complementary data-driven algorithms to minimize the search in chemical space for phenylthiazole-containing molecules that bind the RNA hairpin within the ribosomal peptidyl transferase center (PTC) of Mycobacterium tuberculosis. Our results indicate visual, geometrical, and chemical features that enhance the binding to the targeted RNA. Functional validation was conducted after synthesizing 10 small molecules pinpointed computationally. Four of the 10 were found to be potent inhibitors that target hairpin 91 in the ribosomal PTC of M. tuberculosis and, as a result, stop translation.

SUBMITTER: Grimberg H

PROVIDER: S-EPMC8811966 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Machine learning approaches to optimize small-molecule inhibitors for RNA targeting.

Grimberg Hadar H Tiwari Vinay S VS Tam Benjamin B Gur-Arie Lihi L Gingold Daniela D Polachek Lea L Akabayov Barak B

Journal of cheminformatics 20220202 1

In the era of data science, data-driven algorithms have emerged as powerful platforms that can consolidate bioisosteric rules for preferential modifications on small molecules with a common molecular scaffold. Here we present complementary data-driven algorithms to minimize the search in chemical space for phenylthiazole-containing molecules that bind the RNA hairpin within the ribosomal peptidyl transferase center (PTC) of Mycobacterium tuberculosis. Our results indicate visual, geometrical, an ...[more]

PMID: 35109921

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Project description:The 26S proteasome is the principal protease for regulated intracellular proteolysis. This multi-subunit complex is also pivotal for clearance of harmful proteins that are produced throughout the lifetime of eukaryotes. Recent structural and kinetic studies have revealed a multitude of conformational states of the proteasome in substrate-free and substrate-engaged forms. These conformational transitions demonstrate that proteasome is a highly dynamic machinery during substrate processing that can be also controlled by a number of proteasome-associated factors. Essentially, three distinct family of deubiquitinases-USP14, RPN11, and UCH37-are associated with the 19S regulatory particle of human proteasome. USP14 and UCH37 are capable of editing ubiquitin conjugates during the process of their dynamic engagement into the proteasome prior to the catalytic commitment. In contrast, RPN11-mediated deubiquitination is directly coupled to substrate degradation by sensing the proteasome's conformational switch into the commitment steps. Therefore, proteasome-bound deubiquitinases are likely to tailor the degradation events in accordance with substrate processing steps and for dynamic proteolysis outcomes. Recent chemical screening efforts have yielded highly selective small-molecule inhibitors for targeting proteasomal deubiquitinases, such as USP14 and RPN11. USP14 inhibitors, IU1 and its progeny, were found to promote the degradation of a subset of substrates probably by overriding USP14-imposed checkpoint on the proteasome. On the other hand, capzimin, a RPN11 inhibitor, stabilized the proteasome substrates and showed the anti-proliferative effects on cancer cells. It is highly conceivable that these specific inhibitors will aid to dissect the role of each deubiquitinase on the proteasome. Moreover, customized targeting of proteasome-associated deubiquitinases may also provide versatile therapeutic strategies for induced or repressed protein degradation depending on proteolytic demand and cellular context.

Dataset Information

Machine learning approaches to optimize small-molecule inhibitors for RNA targeting.

Publications

Machine learning approaches to optimize small-molecule inhibitors for RNA targeting.

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