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

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.

酪氨酸激酶（Tyrosine kinases）是激酶家族的一个亚家族，在细胞生命活动中发挥关键调控作用。其活性与非活性构象的失调与癌症等多种疾病密切相关。本研究旨在全面解析这类激酶的柔性-活性关联机制，重点聚焦于蛋白口袋与构象波动。研究共纳入43种不同的酪氨酸激酶，通过采集120微秒的分子动力学模拟数据、开展蛋白口袋与残基波动分析，并辅以互补性机器学习方法完成相关研究。结果显示，激酶的非活性状态通常伴随更高的构象柔性，尤其在DFG基序（DFG motif）区域表现显著。值得注意的是，结合上述长时程模拟与决策树分析手段，本研究确定了DGF+3残基的半定量波动阈值：当该残基波动超过此阈值时，激酶处于非活性状态的概率显著升高。