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

J Chem Inf Model. 2023 Aug 14;63(15):4814-4826. doi: 10.1021/acs.jcim.3c00738. Epub 2023 Jul 18.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Molecular Dynamics Simulation*
  • Protein Kinase Inhibitors / pharmacology
  • Protein-Tyrosine Kinases* / chemistry
  • Protein-Tyrosine Kinases* / metabolism


  • Protein-Tyrosine Kinases
  • Protein Kinase Inhibitors