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Molecular Dynamics and Machine Learning Give Insights on the Flexibility-Activity Relationships in Tyrosine Kinome.

ABSTRACT: Tyrosine kinases are a subfamily of kinases with critical roles in cellular machinery. Dysregulation of their active or inactive forms is associated with diseases like cancer. This study aimed to holistically understand their flexibility-activity relationships, focusing on pockets and fluctuations. We studied 43 different tyrosine kinases by collecting 120 μs of molecular dynamics simulations, pocket and residue fluctuation analysis, and a complementary machine learning approach. We found that the inactive forms often have increased flexibility, particularly at the DFG motif level. Noteworthy, thanks to these long simulations combined with a decision tree, we identified a semiquantitative fluctuation threshold of the DGF+3 residue over which the kinase has a higher probability to be in the inactive form.

SUBMITTER: Majumdar S

PROVIDER: S-EPMC10428216 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Molecular Dynamics and Machine Learning Give Insights on the Flexibility-Activity Relationships in Tyrosine Kinome.

Majumdar Sarmistha S Di Palma Francesco F Spyrakis Francesca F Decherchi Sergio S Cavalli Andrea A

Journal of chemical information and modeling 20230718 15

Tyrosine kinases are a subfamily of kinases with critical roles in cellular machinery. Dysregulation of their active or inactive forms is associated with diseases like cancer. This study aimed to holistically understand their flexibility-activity relationships, focusing on pockets and fluctuations. We studied 43 different tyrosine kinases by collecting 120 μs of molecular dynamics simulations, pocket and residue fluctuation analysis, and a complementary machine learning approach. We found that t ...[more]

PMID: 37462363

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