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Allosteric Coupling in Full-Length Lyn Kinase Revealed by Molecular Dynamics and Network Analysis
Allosteric Coupling in Full-Length Lyn Kinase Revealed by Molecular Dynamics and Network Analysis
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Allosteric Coupling in Full-Length Lyn Kinase Revealed by Molecular Dynamics and Network Analysis
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Allosteric Coupling in Full-Length Lyn Kinase Revealed by Molecular Dynamics and Network Analysis
Allosteric Coupling in Full-Length Lyn Kinase Revealed by Molecular Dynamics and Network Analysis

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Allosteric Coupling in Full-Length Lyn Kinase Revealed by Molecular Dynamics and Network Analysis
Allosteric Coupling in Full-Length Lyn Kinase Revealed by Molecular Dynamics and Network Analysis
Journal Article

Allosteric Coupling in Full-Length Lyn Kinase Revealed by Molecular Dynamics and Network Analysis

2025
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Overview
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.