A molecular dynamics protocol for rapid prediction of EGFR overactivation and its application to the rare mutations S768I, S768N, D761N

Hyperactivation caused by mutations in the Epidermal Growth Factor Receptor (EGFR) kinase domain is implicated in various diseases, including cancer. However, the structural mechanisms underlying overactivation in many EGFR mutations remain poorly understood, and exploring these mechanisms through conventional experiments or <i>in silico</i> simulations is often labor- and cost-intensive. Here, we establish a Molecular Dynamics (MD) protocol capable of rapidly revealing EGFR mutant modes of action using multiple short simulations. We first simulated wild-type EGFR and the well-studied oncogenic mutations L858R and T790M/L858R under different simulation conditions, to derive a protocol which could recapitulate their experimentally established behavior. We then applied this protocol to three rare EGFR mutations: S768I, S768N, and D761N. Experimental studies have suggested that S768I and D761N are oncogenic, whereas S768N is likely a neutral mutation that does not significantly alter EGFR activity. Our simulation results were consistent with these functional indications and provided the corresponding molecular bases - S768I and S768N affect the orientation and stability of the catalytically important αC-helix, while D761N introduces a new hydrogen bonding network between the αC-helix and activation loop. Collectively, the protocol presented here provides a robust and rapid framework for characterizing EGFR mutation mechanisms and is readily adaptable to novel or uncharacterized variants.

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Publication Details

Journal
Computational and Structural Biotechnology Journal
Published
2025-01-01
DOI
https://doi.org/10.1016/j.csbj.2025.07.046
Primary Topic
Lung Cancer Treatments and Mutations
Type
article
Field-Weighted Citation Impact
0.00

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article

A molecular dynamics protocol for rapid prediction of EGFR overactivation and its application to the rare mutations S768I, S768N, D761N

Julian Behn, Hao Fan, R. N. V. Krishna Deepak, Jiancheng Hu
Computational and Structural Biotechnology Journal
Lung Cancer Treatments and Mutations
article

A molecular dynamics protocol for rapid prediction of EGFR overactivation and its application to the rare mutations S768I, S768N, D761N

Julian Behn, Hao Fan, R. N. V. Krishna Deepak, Jiancheng Hu
article en

Abstract

Hyperactivation caused by mutations in the Epidermal Growth Factor Receptor (EGFR) kinase domain is implicated in various diseases, including cancer. However, the structural mechanisms underlying overactivation in many EGFR mutations remain poorly understood, and exploring these mechanisms through conventional experiments or <i>in silico</i> simulations is often labor- and cost-intensive. Here, we establish a Molecular Dynamics (MD) protocol capable of rapidly revealing EGFR mutant modes of action using multiple short simulations. We first simulated wild-type EGFR and the well-studied oncogenic mutations L858R and T790M/L858R under different simulation conditions, to derive a protocol which could recapitulate their experimentally established behavior. We then applied this protocol to three rare EGFR mutations: S768I, S768N, and D761N. Experimental studies have suggested that S768I and D761N are oncogenic, whereas S768N is likely a neutral mutation that does not significantly alter EGFR activity. Our simulation results were consistent with these functional indications and provided the corresponding molecular bases - S768I and S768N affect the orientation and stability of the catalytically important αC-helix, while D761N introduces a new hydrogen bonding network between the αC-helix and activation loop. Collectively, the protocol presented here provides a robust and rapid framework for characterizing EGFR mutation mechanisms and is readily adaptable to novel or uncharacterized variants.

Computational and Structural Biotechnology JournalVol. 27
Agency for Science, Technology and Research (SG), Azim Premji University (IN), National University of Singapore (SG), Duke-NUS Medical School (SG), National Cancer Centre Singapore (SG), Bioinformatics Institute (SG)
Agency for Science, Technology and Research, National Supercomputing Centre Singapore, Bioinformatics Institute
Openalex Percentile: Top 71%
Lung Cancer Treatments and Mutations
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