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Discovering functionally important sites in proteins
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AI
DOI：10.1038/s41467-023-39909-0.png)
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摘要
En 中文
An important step in understanding and using proteins is to identify the residues that are important for function. The authors present a machine-learning based method to predict functional sites that leverages and combines the information available in protein sequences and structures. Proteins play important roles in biology, biotechnology and pharmacology, and missense variants are a common cause of disease. Discovering functionally important sites in proteins is a central but difficult problem because of the lack of large, systematic data sets. Sequence conservation can highlight residues that are functionally important but is often convoluted with a signal for preserving structural stability. We here present a machine learning method to predict functional sites by combining statistical models for protein sequences with biophysical models of stability. We train the model using multiplexed experimental data on variant effects and validate it broadly. We show how the model can be used to discover active sites, as well as regulatory and binding sites. We illustrate the utility of the model by prospective prediction and subsequent experimental validation on the functional consequences of missense variants in HPRT1 which may cause Lesch-Nyhan syndrome, and pinpoint the molecular mechanisms by which they cause disease.
Keyword:
GENOTYPE-PHENOTYPE CORRELATIONS
SIGMA FACTOR SPOIIAB
LESCH-NYHAN DISEASE
MOLECULAR-DYNAMICS
STABILITY
MECHANISM
EVOLUTION
SEQUENCE
PREDICTION
FRAMEWORK
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
15.7
论文数:
9.4W
被引数:
91.2W
机构
引用论文
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