Docking-based modeling of protein-protein interfaces for extensive structural and functional characterization of missense mutations

Next-generation sequencing (NGS) technologies are providing genomic information for an increasing number of healthy individuals and patient populations. In the context of the large amount of generated genomic data that is being generated, understanding the effect of disease-related mutations at molecular level can contribute to close the gap between genotype and phenotype and thus improve prevention, diagnosis or treatment of a pathological condition. In order to fully characterize the effect of a pathological mutation and have useful information for prediction purposes, it is important first to identify whether the mutation is located at a protein-binding interface, and second to understand the effect on the binding affinity of the affected interaction/s. Computational methods, such as protein docking are currently used to complement experimental efforts and could help to build the human structural interactome. Here we have extended the original pyDockNIP method to predict the location of disease-associated nsSNPs at protein-protein interfaces, when there is no available structure for the protein-protein complex. We have applied this approach to the pathological interaction networks of six diseases with low structural data on PPIs. This approach can almost double the number of nsSNPs that can be characterized and identify edgetic effects in many nsSNPs that were previously unknown. This can help to annotate and interpret genomic data from large-scale population studies, and to achieve a better understanding of disease at molecular level.

下一代测序技术（Next-generation sequencing, NGS）正为日益增多的健康个体与患者人群提供基因组信息。在当前海量生成的基因组数据背景下，解析疾病相关突变的分子层面效应，有助于弥合基因型与表型之间的认知鸿沟，进而优化疾病的预防、诊断与治疗策略。为全面表征病理性突变的效应并获取可用于预测的有效信息，需首先明确该突变是否位于蛋白质结合界面，其次厘清其对受影响相互作用的结合亲和力的影响。当前，蛋白质对接（protein docking）等计算方法被用于补充实验研究，或可助力构建人类结构相互作用组。本研究对原始pyDockNIP方法进行了扩展，可在缺乏蛋白质-蛋白质复合物可用结构的情况下，预测疾病相关错义单核苷酸多态性（non-synonymous single nucleotide polymorphisms, nsSNPs）在蛋白质-蛋白质界面的定位。我们将该方法应用于6种蛋白质-蛋白质相互作用（protein-protein interactions, PPIs）结构数据匮乏的疾病的病理相互作用网络。该方法可使可表征的nsSNPs数量提升近一倍，并能识别此前未被发现的大量nsSNPs的边缘效应（edgetic effects）。这将有助于注释和解读大规模人群研究产生的基因组数据，从而在分子层面更深入地理解疾病的发病机制。