气候变化领域集成服务门户(CCPortal): Computational studies of anaplastic lymphoma kinase mutations reveal common mechanisms of oncogenic activation

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DOI	10.1073/pnas.2019132118
	Computational studies of anaplastic lymphoma kinase mutations reveal common mechanisms of oncogenic activation
	Patil K.; Jordan E.J.; Park J.H.; Suresh K.; Smith C.M.; Lemmon A.A.; Mossé Y.P.; Lemmon M.A.; Radhakrishnan R.
发表日期	2021
ISSN	00278424
卷号	118 期号:10
英文摘要	Kinases play important roles in diverse cellular processes, including signaling, differentiation, proliferation, and metabolism. They are frequently mutated in cancer and are the targets of a large number of specific inhibitors. Surveys of cancer genome atlases reveal that kinase domains, which consist of 300 amino acids, can harbor numerous (150 to 200) single-point mutations across different patients in the same disease. This preponderance of mutations—some activating, some silent—in a known target protein make clinical decisions for enrolling patients in drug trials challenging since the relevance of the target and its drug sensitivity often depend on the mutational status in a given patient. We show through computational studies using molecular dynamics (MD) as well as enhanced sampling simulations that the experimentally determined activation status of a mutated kinase can be predicted effectively by identifying a hydrogen bonding fingerprint in the activation loop and the αC-helix regions, despite the fact that mutations in cancer patients occur throughout the kinase domain. In our study, we find that the predictive power of MD is superior to a purely data-driven machine learning model involving biochemical features that we implemented, even though MD utilized far fewer features (in fact, just one) in an unsupervised setting. Moreover, the MD results provide key insights into convergent mechanisms of activation, primarily involving differential stabilization of a hydrogen bond network that engages residues of the activation loop and αC-helix in the active-like conformation (in >70% of the mutations studied, regardless of the location of the mutation). © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Driver mutations; Focus formation assay; Kinase activation; Machine learning; Molecular dynamics
语种	英语
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/180349
作者单位	Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA 19104-6315, United States; Graduate Group in Biochemistry and Molecular Biology, University of Pennsylvania, Philadelphia, PA 19104-6073, United States; Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA 19104-6073, United States; Department of Pharmacology, Yale University, New Haven, CT 06520, United States; Cancer Biology Institute, Yale University, West Haven, CT 06516, United States; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104-6321, United States; Children’s Hospital of Philadelphia, Philadelphia, PA 19104, United States; Department of Pediatrics, University of Pennsylvania, Philadelphia, PA 19104, United States
推荐引用方式 GB/T 7714	Patil K.,Jordan E.J.,Park J.H.,et al. Computational studies of anaplastic lymphoma kinase mutations reveal common mechanisms of oncogenic activation[J],2021,118(10).
APA	Patil K..,Jordan E.J..,Park J.H..,Suresh K..,Smith C.M..,...&Radhakrishnan R..(2021).Computational studies of anaplastic lymphoma kinase mutations reveal common mechanisms of oncogenic activation.Proceedings of the National Academy of Sciences of the United States of America,118(10).
MLA	Patil K.,et al."Computational studies of anaplastic lymphoma kinase mutations reveal common mechanisms of oncogenic activation".Proceedings of the National Academy of Sciences of the United States of America 118.10(2021).