MRC Human Genetics Unit, Institute of Genetics & Cancer, University of Edinburgh, Edinburgh, UK.
Nat Commun. 2022 Jul 6;13(1):3895. doi: 10.1038/s41467-022-31686-6.
Most known pathogenic mutations occur in protein-coding regions of DNA and change the way proteins are made. Taking protein structure into account has therefore provided great insight into the molecular mechanisms underlying human genetic disease. While there has been much focus on how mutations can disrupt protein structure and thus cause a loss of function (LOF), alternative mechanisms, specifically dominant-negative (DN) and gain-of-function (GOF) effects, are less understood. Here, we investigate the protein-level effects of pathogenic missense mutations associated with different molecular mechanisms. We observe striking differences between recessive vs dominant, and LOF vs non-LOF mutations, with dominant, non-LOF disease mutations having much milder effects on protein structure, and DN mutations being highly enriched at protein interfaces. We also find that nearly all computational variant effect predictors, even those based solely on sequence conservation, underperform on non-LOF mutations. However, we do show that non-LOF mutations could potentially be identified by their tendency to cluster in three-dimensional space. Overall, our work suggests that many pathogenic mutations that act via DN and GOF mechanisms are likely being missed by current variant prioritisation strategies, but that there is considerable scope to improve computational predictions through consideration of molecular disease mechanisms.
大多数已知的致病突变发生在 DNA 的蛋白质编码区域，并改变了蛋白质的产生方式。因此，考虑蛋白质结构为我们深入了解人类遗传疾病的分子机制提供了很大的帮助。虽然人们已经关注了突变如何破坏蛋白质结构，从而导致功能丧失（LOF），但其他机制，特别是显性负（DN）和功能获得（GOF）效应，人们的了解较少。在这里，我们研究了与不同分子机制相关的致病性错义突变对蛋白质水平的影响。我们观察到隐性与显性、LOF 与非 LOF 突变之间存在显著差异，显性、非 LOF 疾病突变对蛋白质结构的影响要温和得多，而 DN 突变在蛋白质界面上高度富集。我们还发现，即使是仅基于序列保守性的计算变异效应预测器，在非 LOF 突变上的表现也很差。然而，我们确实表明，非 LOF 突变可能可以通过它们在三维空间中聚类的趋势来识别。总的来说，我们的工作表明，许多通过 DN 和 GOF 机制起作用的致病性突变可能被当前的变异优先级策略所遗漏，但通过考虑分子疾病机制，对计算预测进行改进还是有很大的空间的。
