Kumar Avishek, Butler Brandon M, Kumar Sudhir, Ozkan S Banu
Department of Physics and Center for Biological Physics, Arizona State University, Tempe, AZ 85281, United States.
Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, United States; Department of Biology, Temple University, Philadelphia, PA 19122, United States; Center for Genomic Medicine and Research, King Abdulaziz University, Jeddah, Saudi Arabia.
Curr Opin Struct Biol. 2015 Dec;35:135-42. doi: 10.1016/j.sbi.2015.11.002. Epub 2015 Dec 9.
Sequencing technologies are revealing many new non-synonymous single nucleotide variants (nsSNVs) in each personal exome. To assess their functional impacts, comparative genomics is frequently employed to predict if they are benign or not. However, evolutionary analysis alone is insufficient, because it misdiagnoses many disease-associated nsSNVs, such as those at positions involved in protein interfaces, and because evolutionary predictions do not provide mechanistic insights into functional change or loss. Structural analyses can aid in overcoming both of these problems by incorporating conformational dynamics and allostery in nSNV diagnosis. Finally, protein-protein interaction networks using systems-level methodologies shed light onto disease etiology and pathogenesis. Bridging these network approaches with structurally resolved protein interactions and dynamics will advance genomic medicine.
测序技术正在揭示每个人外显子组中许多新的非同义单核苷酸变异(nsSNV)。为了评估它们的功能影响,经常采用比较基因组学来预测它们是否为良性。然而,仅靠进化分析是不够的,因为它会误诊许多与疾病相关的nsSNV,比如那些位于蛋白质界面的nsSNV,而且进化预测无法提供功能变化或丧失的机制性见解。结构分析可以通过在nsSNV诊断中纳入构象动力学和变构作用来帮助克服这两个问题。最后,使用系统水平方法的蛋白质-蛋白质相互作用网络能够揭示疾病的病因和发病机制。将这些网络方法与结构解析的蛋白质相互作用和动力学联系起来将推动基因组医学的发展。