Modeling protein interactions to interpret genetic variation

对生物学来说，一个巨大的挑战是最大限度地从高通量测序中获得基本的见解，这已经变得快速和廉价。蛋白质及其相互作用的结构信息对于理解遗传变异的影响至关重要。关于单氨基酸变异（SAV）的大量信息将从真核生物和原核生物中获得。我们将开发一种综合方法，用于大规模预测蛋白质结构及其关联。将建立模型生物的预测结构和复合体数据库，在此基础上绘制遗传变异图并评估其表型效应。提出的研究目标是：(1)开发基于高通量结构的方法来预测实验确定和建模的蛋白质的相互作用；(2)开发用于单个蛋白质高通量建模的先进方法；(3)建立模式生物蛋白质结构和蛋白-蛋白复合物全基因组数据库；(4)评估遗传变异的表型效应。将开发方法来区分非相互作用和相互作用的蛋白质，并基于与实验确定的蛋白质-蛋白质复合物的相似性和分子间能量景观的特性来模拟蛋白质-蛋白质复合物的结构。一种新的折叠检测方法将扩展可以建模的蛋白质的数量。将开发一个将蛋白质结构预测与蛋白质-蛋白质复合物预测相结合的管道，并使用基于结构的方法预测sav的效果。该合作提案结合了美国团队在蛋白质-蛋白质相互作用高通量建模方面的高度互补的专业领域，以及英国团队在蛋白质结构预测和SAV效应方面的专业领域。

A grand challenge for biology is to maximize the fundamental insights from high-throughput sequencing, which has become rapid and inexpensive. Structural information on proteins and their interactions is essential for understanding the effects of genetic variation. A vast amount of information on single amino acid variants (SAV) will be available from eukaryotic and prokaryotic organisms. We will develop an integrated approach for large-scale prediction of protein structures and their association. A database of predicted structures and complexes for model organisms will be established upon which genetic variants will be mapped and their phenotypic effect assessed. The Objectives of the proposed research are: (1) to develop high-throughput structure-based methods to predict interactions of experimentally determined and modeled proteins; (2) to develop advanced methodology for high-throughput modeling of individual proteins; (3) to generate genome-wide database of protein structures and protein-protein complexes for model organisms; and (4) to assess phenotypic effects of genetic variation. Approaches will be developed to discriminate non-interacting from interacting proteins, and to model the structures of protein-protein complexes, based on similarity to experimentally determined protein-protein complexes and on properties of the intermolecular energy landscape. A novel approach for fold detection will extend the number of proteins that can be modeled. A pipeline will be developed to integrate protein structure prediction with the prediction of protein-protein complexes, and use structure-based approaches to predict the effects of SAVs. This collaborative proposal combines highly complementary areas of expertise of the US team, on high-throughput modeling of protein-protein interactions, and the UK team, on protein structure prediction and SAV effects.