Protein Variant Interpretation

蛋白质变异解释

• 批准号: MC_UU_00035/9
• 负责人: Joe Marsh
• 金额: $ 187.69万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Intramural
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MC_UU_00035%2F9
• 关键词: Protein; Variant; Interpretation

We study how mutations affect proteins, focusing on two closely related questions: 1) How likely is it that a given protein variant has a clinically relevant effect; and 2) What is the protein-level molecular mechanism by which a variant causes disease? To do this, we employ three highly complementary strategies. Computational variant effect predictors, driven primarily by evolutionary information, are very good at identifying pathogenic mutations in certain genes, but tell us nothing about why they are damaging. In contrast, using structural bioinformatics to investigate the 3D protein context of mutations can provide great insight into the molecular mechanisms underlying disease mutations, but has historically been less useful for identifying deleterious mutations. Finally, deep mutational scanning, which allows direct high-throughput measurement of variant effects, is proving tremendously valuable for the identification disease mutations, and can also clarify molecular mechanisms given the correct experimental design. Our focus is on developing optimal strategies for utilising all three approaches to most efficiently and effectively identify damaging protein variants and elucidate their molecular mechanisms, ultimately leading to improved diagnosis and treatment of human genetic disease.

我们研究突变如何影响蛋白质，重点是两个密切相关的问题：1）一个给定的蛋白质变体有多大可能具有临床相关的影响; 2）一个变体导致疾病的蛋白质水平的分子机制是什么？为此，我们采取了三种高度互补的战略。主要由进化信息驱动的计算变异效应预测器非常擅长识别某些基因中的致病突变，但无法告诉我们它们为什么具有破坏性。相比之下，使用结构生物信息学来研究突变的3D蛋白背景可以提供对疾病突变潜在的分子机制的深入了解，但在历史上对于识别有害突变不太有用。最后，深度突变扫描允许直接高通量测量变异效应，对于识别疾病突变具有巨大价值，并且还可以在正确的实验设计下阐明分子机制。我们的重点是开发利用所有三种方法的最佳策略，以最有效地识别破坏性蛋白质变体并阐明其分子机制，最终改善人类遗传疾病的诊断和治疗。

项目成果

Updated benchmarking of variant effect predictors using deep mutational scanning.

• DOI: 10.15252/msb.202211474
• 发表时间: 2023-08-08
• 期刊: MOLECULAR SYSTEMS BIOLOGY
• 影响因子: 9.9
• 作者: Livesey, Benjamin J.;Marsh, Joseph A.
• 通讯作者: Marsh, Joseph A.