Protein Variant Interpretation

我们研究突变如何影响蛋白质，集中在两个密切相关的问题上：1)给定的蛋白质变体具有临床相关影响的可能性有多大；以及2)变异导致疾病的蛋白质水平的分子机制是什么？为了做到这一点，我们采用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的致病突变，但却没有告诉我们为什么它们是有害的。相比之下，使用结构生物信息学来研究突变的3D蛋白质背景可以提供对疾病突变背后的分子机制的很大洞察，但从历史上看，它在识别有害突变方面用处较小。最后，允许直接高通量测量变异效应的深度突变扫描对于识别疾病突变非常有价值，并且还可以在正确的实验设计的情况下阐明分子机制。我们的重点是开发最佳策略，利用所有这三种方法，最有效和最有效地识别破坏性蛋白质变异，并阐明其分子机制，最终改进人类遗传病的诊断和治疗。

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.