Julian Behn, R N V Krishna Deepak, Jiancheng Hu, Hao Fan
{"title":"一种快速预测EGFR过度激活的分子动力学方法及其在罕见突变S768I, S768N, D761N中的应用","authors":"Julian Behn, R N V Krishna Deepak, Jiancheng Hu, Hao Fan","doi":"10.1016/j.csbj.2025.07.046","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3370-3378"},"PeriodicalIF":4.1000,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12341517/pdf/","citationCount":"0","resultStr":"{\"title\":\"A molecular dynamics protocol for rapid prediction of EGFR overactivation and its application to the rare mutations S768I, S768N, D761N.\",\"authors\":\"Julian Behn, R N V Krishna Deepak, Jiancheng Hu, Hao Fan\",\"doi\":\"10.1016/j.csbj.2025.07.046\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":10715,\"journal\":{\"name\":\"Computational and structural biotechnology journal\",\"volume\":\"27 \",\"pages\":\"3370-3378\"},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2025-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12341517/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational and structural biotechnology journal\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1016/j.csbj.2025.07.046\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational and structural biotechnology journal","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.csbj.2025.07.046","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
表皮生长因子受体(EGFR)激酶结构域突变引起的过度激活与包括癌症在内的多种疾病有关。然而,许多EGFR突变过度激活的结构机制仍然知之甚少,通过传统实验或计算机模拟探索这些机制通常是劳动和成本高的。在这里,我们建立了一个分子动力学(MD)协议,能够通过多个短模拟快速揭示EGFR突变的作用模式。我们首先在不同的模拟条件下模拟野生型EGFR和已经得到充分研究的致癌突变L858R和T790M/L858R,以得出一个可以概括其实验建立的行为的方案。然后,我们将该方案应用于三种罕见的EGFR突变:S768I, S768N和D761N。实验研究表明,S768I和D761N具有致癌性,而S768N可能是一种中性突变,不会显著改变EGFR活性。我们的模拟结果与这些功能指示一致,并提供了相应的分子碱基- S768I和S768N影响催化重要的α c -螺旋的取向和稳定性,而D761N在α c -螺旋和活化环之间引入了新的氢键网络。总的来说,本文提出的方案为表征EGFR突变机制提供了一个强大而快速的框架,并且很容易适用于新的或未表征的变异。
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 in silico 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.
期刊介绍:
Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to:
Structure and function of proteins, nucleic acids and other macromolecules
Structure and function of multi-component complexes
Protein folding, processing and degradation
Enzymology
Computational and structural studies of plant systems
Microbial Informatics
Genomics
Proteomics
Metabolomics
Algorithms and Hypothesis in Bioinformatics
Mathematical and Theoretical Biology
Computational Chemistry and Drug Discovery
Microscopy and Molecular Imaging
Nanotechnology
Systems and Synthetic Biology