Rabipour Mina, Hassenrück Floyd, Pallaske Elena, Röhrig Fernanda, Hallek Michael, Alvarez-Idaboy Juan Raul, Kramer Oliver, Rebollido-Rios Rocio
Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Düsseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany.
Center for Molecular Medicine Cologne, 50931 Cologne, Germany.
Int J Mol Sci. 2025 Jun 18;26(12):5835. doi: 10.3390/ijms26125835.
Lyn is a multifunctional Src-family kinase (SFK) that regulates immune signaling and has been implicated in diverse types of cancer. Unlike other SFKs, its full-length structure and regulatory dynamics remain poorly characterized. In this study, we present the first long-timescale molecular dynamics analysis of full-length Lyn, including the SH3, SH2, and SH1 domains, across wildtype, ligand-bound, and cancer-associated mutant states. Using principal component analysis, dynamic cross-correlation matrices, and network-based methods, we show that ATP binding stabilizes the kinase core and promotes interdomain coordination, while the ATP-competitive inhibitor dasatinib and specific mutations (e.g., E290K, I364N) induce conformational decoupling and weaken long-range communication. We identify integration modules and develop an interface-weighted scoring scheme to rank dynamically central residues. This analysis reveals 44 allosteric hubs spanning SH3, SH2, SH1, and interdomain regions. Finally, a random forest classifier trained on 16 MD-derived features highlights key interdomain descriptors, distinguishing functional states with an AUC of 0.98. Our results offer a dynamic and network-level framework for understanding Lyn regulation and identify potential regulatory hotspots for structure-based drug design. More broadly, our approach demonstrates the value of integrating full-length MD simulations with network and machine learning techniques to probe allosteric control in multidomain kinases.
Lyn是一种多功能的Src家族激酶(SFK),可调节免疫信号传导,并与多种类型的癌症有关。与其他SFK不同,其全长结构和调节动力学仍未得到充分表征。在本研究中,我们首次对全长Lyn进行了长时间尺度的分子动力学分析,包括野生型、配体结合型和癌症相关突变状态下的SH3、SH2和SH1结构域。使用主成分分析、动态交叉相关矩阵和基于网络的方法,我们表明ATP结合可稳定激酶核心并促进结构域间的协调,而ATP竞争性抑制剂达沙替尼和特定突变(如E290K、I364N)会诱导构象解耦并削弱远程通讯。我们识别了整合模块并开发了一种界面加权评分方案来对动态核心残基进行排名。该分析揭示了跨越SH3、SH2、SH1和结构域间区域的44个变构中心。最后,基于16个MD衍生特征训练的随机森林分类器突出了关键的结构域间描述符,以0.98的AUC区分功能状态。我们的结果为理解Lyn调节提供了一个动态和网络层面的框架,并为基于结构的药物设计识别了潜在的调节热点。更广泛地说,我们的方法证明了将全长MD模拟与网络和机器学习技术相结合以探究多结构域激酶变构控制的价值。