The Loss and Gain of Functional Amino Acid Residues Is a Common Mechanism Causing Human Inherited Disease

Elucidating the precise molecular events altered by disease-causing genetic variants represents a major challenge in translational bioinformatics. To this end, many studies have investigated the structural and functional impact of amino acid substitutions. Most of these studies were however limited in scope to either individual molecular functions or were concerned with functional effects (e.g. deleterious vs. neutral) without specifically considering possible molecular alterations. The recent growth of structural, molecular and genetic data presents an opportunity for more comprehensive studies to consider the structural environment of a residue of interest, to hypothesize specific molecular effects of sequence variants and to statistically associate these effects with genetic disease. In this study, we analyzed data sets of disease-causing and putatively neutral human variants mapped to protein 3D structures as part of a systematic study of the loss and gain of various types of functional attribute potentially underlying pathogenic molecular alterations. We first propose a formal model to assess probabilistically function-impacting variants. We then develop an array of structure-based functional residue predictors, evaluate their performance, and use them to quantify the impact of disease-causing amino acid substitutions on catalytic activity, metal binding, macromolecular binding, ligand binding, allosteric regulation and post-translational modifications. We show that our methodology generates actionable biological hypotheses for up to 41% of disease-causing genetic variants mapped to protein structures suggesting that it can be reliably used to guide experimental validation. Our results suggest that a significant fraction of disease-causing human variants mapping to protein structures are function-altering both in the presence and absence of stability disruption.

阐明致病遗传变异所介导的精确分子事件，是转化生物信息学（translational bioinformatics）领域的一项重大挑战。为此，诸多研究已围绕氨基酸替换（amino acid substitutions）的结构与功能效应展开探究。然而，此类研究大多存在范围局限：要么仅聚焦于单一分子功能，要么仅关注功能效应（例如有害vs.中性），却未专门考量潜在的分子改变。近年来结构、分子与遗传数据的爆发式增长，为开展更全面的研究提供了契机：可通过聚焦目标残基的结构环境，对序列变异的特定分子效应提出假说，并将这些效应与遗传疾病进行统计学关联。本研究中，我们针对映射至蛋白质三维结构（protein 3D structures）的致病型与推定中性型人类变异数据集展开分析，作为系统性研究潜在致病分子改变背后各类功能属性得失的组成部分。我们首先提出一套形式化模型，以概率方法评估对功能产生影响的变异。随后，我们开发了一系列基于结构的功能残基预测工具，对其性能进行评估，并利用这些工具量化致病氨基酸替换对催化活性（catalytic activity）、金属结合（metal binding）、大分子结合（macromolecular binding）、配体结合（ligand binding）、变构调控（allosteric regulation）以及翻译后修饰（post-translational modifications）的影响。研究结果表明，针对映射至蛋白质结构的致病遗传变异，我们的方法可为其中多达41%的变异生成可指导实践的生物学假说，这提示该方法可被可靠用于指导实验验证。我们的研究结果显示，在映射至蛋白质结构的致病人类变异中，有相当一部分在存在或不存在稳定性破坏的情况下，均会对功能产生改变。