Modeling protein interactions to interpret genetic variation

生物学面临的一个重大挑战是最大限度地从高通量测序中获得最基本的见解，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是必不可少的。有关单一氨基酸变体(SAV)的大量信息将从真核生物和原核生物中获得。我们将开发一种用于大规模预测蛋白质结构及其相互作用的综合方法。将建立一个模式生物预测结构和复合体的数据库，在此基础上绘制遗传变异图并评估其表型效应。拟议研究的目标是：(1)开发基于结构的高通量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的高通量建模的先进方法；(3)为模式生物生成蛋白质结构和蛋白质-蛋白质复合体的全基因组数据库；以及(4)评估遗传变异的表型效应。将开发区分不相互作用的蛋白质和相互作用的蛋白质的方法，并根据与实验确定的蛋白质-蛋白质复合体的相似性和分子间能量格局的性质来模拟蛋白质-蛋白质复合体的结构。一种新的折叠检测方法将扩大可以建模的蛋白质的数量。将开发一条管道，将蛋白质结构预测与蛋白质-蛋白质复合体预测结合起来，并使用基于结构的方法来预测SAV的效果。这项合作提案结合了美国团队和英国团队在蛋白质结构预测和SAV效应方面的高度互补的专业知识领域，前者致力于高通量蛋白质相互作用建模。

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.