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

Tyrosine kinases are a subfamily of kinases with criticalrolesin cellular machinery. Dysregulation of their active or inactive formsis associated with diseases like cancer. This study aimed to holisticallyunderstand their flexibility-activity relationships, focusingon pockets and fluctuations. We studied 43 different tyrosine kinasesby collecting 120 & mu;s of molecular dynamics simulations, pocketand residue fluctuation analysis, and a complementary machine learningapproach. We found that the inactive forms often have increased flexibility,particularly at the DFG motif level. Noteworthy, thanks to these longsimulations combined with a decision tree, we identified a semiquantitativefluctuation threshold of the DGF+3 residue over which the kinase hasa higher probability to be in the inactive form.