Dataset Information

LIM Kinases, Promising but Reluctant Therapeutic Targets: Chemistry and Preclinical Validation In Vivo.

ABSTRACT: LIM Kinases are important actors in the regulation of cytoskeleton dynamics by controlling microtubule and actin filament turnover. The signaling pathways involving LIM kinases for actin filament remodeling are well established. They are downstream effectors of small G proteins of the Rho-GTPases family and have become promising targets for the treatment of several major diseases because of their position at the lower end of these signaling cascades. Cofilin, which depolymerizes actin filaments, is the best-known substrate of these enzymes. The phosphorylation of cofilin to its inactive form by LIM kinases avoids actin filament depolymerization. The balance between phosphorylated and non-phosphorylated cofilin is thought to play an important role in tumor cell invasion and metastasis. Since 2006, many small molecules have been developed for LIMK inhibition, and in this review article, we will discuss the structure-activity relationships of the few inhibitor families that have been tested in vivo on different pathological models.

SUBMITTER: Berabez R

PROVIDER: S-EPMC9265711 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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LIM Kinases, Promising but Reluctant Therapeutic Targets: Chemistry and Preclinical Validation In Vivo.

Berabez Rayan R Routier Sylvain S Bénédetti Hélène H Plé Karen K Vallée Béatrice B

Cells 20220630 13

LIM Kinases are important actors in the regulation of cytoskeleton dynamics by controlling microtubule and actin filament turnover. The signaling pathways involving LIM kinases for actin filament remodeling are well established. They are downstream effectors of small G proteins of the Rho-GTPases family and have become promising targets for the treatment of several major diseases because of their position at the lower end of these signaling cascades. Cofilin, which depolymerizes actin filaments, ...[more]

PMID: 35805176

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Project description:Predictive approaches such as virtual screening have been used in drug discovery with the objective of reducing developmental time and costs. Current machine learning and network-based approaches have issues related to generalization, usability, or model interpretability, especially due to the complexity of target proteins' structure/function, and bias in system training datasets. Here, we propose a new method "DRUIDom" (DRUg Interacting Domain prediction) to identify bio-interactions between drug candidate compounds and targets by utilizing the domain modularity of proteins, to overcome problems associated with current approaches. DRUIDom is composed of two methodological steps. First, ligands/compounds are statistically mapped to structural domains of their target proteins, with the aim of identifying their interactions. As such, other proteins containing the same mapped domain or domain pair become new candidate targets for the corresponding compounds. Next, a million-scale dataset of small molecule compounds, including those mapped to domains in the previous step, are clustered based on their molecular similarities, and their domain associations are propagated to other compounds within the same clusters. Experimentally verified bioactivity data points, obtained from public databases, are meticulously filtered to construct datasets of active/interacting and inactive/non-interacting drug/compound-target pairs (~2.9M data points), and used as training data for calculating parameters of compound-domain mappings, which led to 27,032 high-confidence associations between 250 domains and 8,165 compounds, and a finalized output of ~5 million new compound-protein interactions. DRUIDom is experimentally validated by syntheses and bioactivity analyses of compounds predicted to target LIM-kinase proteins, which play critical roles in the regulation of cell motility, cell cycle progression, and differentiation through actin filament dynamics. We showed that LIMK-inhibitor-2 and its derivatives significantly block the cancer cell migration through inhibition of LIMK phosphorylation and the downstream protein cofilin. One of the derivative compounds (LIMKi-2d) was identified as a promising candidate due to its action on resistant Mahlavu liver cancer cells. The results demonstrated that DRUIDom can be exploited to identify drug candidate compounds for intended targets and to predict new target proteins based on the defined compound-domain relationships. Datasets, results, and the source code of DRUIDom are fully-available at: https://github.com/cansyl/DRUIDom.

Dataset Information

LIM Kinases, Promising but Reluctant Therapeutic Targets: Chemistry and Preclinical Validation In Vivo.

Publications

LIM Kinases, Promising but Reluctant Therapeutic Targets: Chemistry and Preclinical Validation In Vivo.

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