Computational & Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, I-16163 Genova, Italy.
Department of Drug Science and Technology, University of Turin, via Giuria 9, I-10125 Turin, Italy.
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.
酪氨酸激酶是一类激酶的亚家族，在细胞机制中起着关键作用。它们的活性或非活性形式的失调与癌症等疾病有关。本研究旨在全面了解它们的灵活性-活性关系，重点研究口袋和波动。我们通过收集 120μs 的分子动力学模拟、口袋和残基波动分析以及补充的机器学习方法，研究了 43 种不同的酪氨酸激酶。我们发现，非活性形式通常具有更高的灵活性，特别是在 DFG 基序水平。值得注意的是，由于这些长模拟结合决策树，我们确定了 DGF+3 残基波动的半定量阈值，超过该阈值激酶更有可能处于非活性形式。
