Pathogenic missense protein variants affect different functional pathways and proteomic features than healthy population variants.
Pathogenic missense protein variants affect different functional pathways and proteomic features than healthy population variants.
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DOI：
10.1371/journal.pbio.3001207
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发表时间：
2021-04
期刊：
影响因子：
9.8
通讯作者：
Fraternali F
中科院分区：
文献类型：
--
作者：
Laddach A;Ng JCF;Fraternali F
错义变体存在于健康人群中，但其中一些是人类疾病的病因。因此，与“健康”或“患病”状态相关的变体的分类并不总是简单的。更深入地了解健康和疾病中错义变异的性质，它们可能影响的细胞过程，以及这些差异背后的一般分子原理，对于提供致病变异的真正影响的机制解释至关重要。在这里，我们已经形成了一个统计框架，该框架能够对全长蛋白质、其结构域和3D结构定义区域的变体富集进行稳健的概率量化。使用这个框架，我们验证和扩展以前报道的趋势的变体富集在不同的蛋白质结构区域（表面/核心/接口）。通过检查变体富集与可用功能途径以及转录组学和蛋白质组学（蛋白质半衰期、热稳定性、丰度）数据的关联，我们挖掘了一组丰富的分子特征，可以区分致病性和群体变体：致病性变体主要影响参与细胞增殖和核苷酸加工的蛋白质，并且富含更丰富的蛋白质。此外，罕见的群体变异显示出比致病性变异更接近常见的特征。我们通过与现有的计算机模拟变体影响注释进行比较，验证了这些分子特征与变体致病性之间的关联。这项研究提供了不同蛋白质如何对错义变体表现出弹性和/或敏感性的分子细节，并提供了优先考虑变体富集蛋白质和蛋白质结构域用于治疗靶向和开发的基本原理。我们为此研究创建的ZoomVar数据库可在fraternalilab.kcl.ac.uk/ZoomVar上获得。它允许用户以编程方式用蛋白质结构信息注释错义变体，并计算不同蛋白质结构区域中的变体富集。如何改进遗传变异的分类，将其分为有害或无害？这项研究使用了强大的统计分析来利用蛋白质结构，蛋白质组学测量和功能途径之间的相互作用，以更好地区分健康和疾病中的错义变体。
Missense variants are present amongst the healthy population, but some of them are causative of human diseases. A classification of variants associated with “healthy” or “diseased” states is therefore not always straightforward. A deeper understanding of the nature of missense variants in health and disease, the cellular processes they may affect, and the general molecular principles which underlie these differences is essential to offer mechanistic explanations of the true impact of pathogenic variants. Here, we have formalised a statistical framework which enables robust probabilistic quantification of variant enrichment across full-length proteins, their domains, and 3D structure-defined regions. Using this framework, we validate and extend previously reported trends of variant enrichment in different protein structural regions (surface/core/interface). By examining the association of variant enrichment with available functional pathways and transcriptomic and proteomic (protein half-life, thermal stability, abundance) data, we have mined a rich set of molecular features which distinguish between pathogenic and population variants: Pathogenic variants mainly affect proteins involved in cell proliferation and nucleotide processing and are enriched in more abundant proteins. Additionally, rare population variants display features closer to common than pathogenic variants. We validate the association between these molecular features and variant pathogenicity by comparing against existing in silico variant impact annotations. This study provides molecular details into how different proteins exhibit resilience and/or sensitivity towards missense variants and provides the rationale to prioritise variant-enriched proteins and protein domains for therapeutic targeting and development. The ZoomVar database, which we created for this study, is available at fraternalilab.kcl.ac.uk/ZoomVar. It allows users to programmatically annotate missense variants with protein structural information and to calculate variant enrichment in different protein structural regions. How do can one improve the classification of genetic variants as harmful or harmless? This study uses a robust statistical analysis to exploit the interplay between protein structure, proteomic measurements and functional pathways to enable better discrimination between missense variants in health and disease.
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通讯作者：
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