Bilateral BBSRC-NSF/BIO: ABI Innovation: Modeling protein interactions to interpret genetic variation

该项目将把有关基因DNA序列的信息与基因所产生的蛋白质的功能联系起来。蛋白质的结构信息和蛋白质如何相互作用对于理解遗传变异对细胞功能的影响至关重要。由于所有的大型DNA测序项目，我们拥有大量关于DNA变化的信息，这些变化会导致所有类型生物的蛋白质(氨基酸)的构建块发生变化，但要了解这如何影响蛋白质和细胞功能还需要更长的时间。该项目将提供新的和改进的方法来确定蛋白质如何折叠，以及它们如何与影响细胞行为的其他蛋白质相互作用。这项研究将使研究人员能够将单个有机体中出现的所有蛋白质的结构特性与该有机体的外观、行为和对环境的反应的变化联系起来。通过网络数据库公布结果，结构和相互作用变化对生物体的总影响将提供给其他研究人员和公众。长期的研究目标是了解驱动生命过程的分子特性，并选择将导致预期产品在生物技术应用中具有预期功能的变化。研究成果将包括在研究生和本科课程中。少数民族学生、女性、本科生，包括主要教学机构的学生，以及高中生将参与研究的不同部分。该项目的参与者在科学会议上展示了他们的成果。该项目将产生一种大规模预测蛋白质结构及其联系的综合方法。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。研究目标是：(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)开发用于高通量建模单个蛋白质的先进方法；(3)为模式生物生成全基因组的蛋白质结构和蛋白质-蛋白质复合体数据库；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。这个合作项目结合了美国团队和英国团队在蛋白质结构预测和SAV效应方面高度互补的专业领域，前者致力于蛋白质相互作用的高通量建模。该项目的结果将在http://vakser.compbio.ku.edu和http://www.sbg.bio.ic.ac.uk.上公布。

This project will connect information about the DNA sequence of genes to what that means for the function of the proteins that the genes make. Structural information on proteins and how proteins interact is essential for understanding the effects of genetic variation on the function of cells. Because of all the large DNA sequencing projects, we have huge amounts of information on DNA changes that lead to changes in the building blocks of proteins (the amino acids) for all types of organisms, but understanding how this affects protein and cell function is much farther behind. This project will provide new and improved methods for determining how proteins are folded, and how they interact with other proteins, which affects how cells behave. This research will allow researchers to connect structural properties of all the proteins occurring in a single organism to changes in appearance, behavior, and responses to the environment of that organism. By publishing the results through Web-available databases the total effect on the organism from the structural and interaction changes will be available to other researchers and the public. The long-term research goals are to understand the molecular properties that drive life processes, and to choose changes that will result in making the intended product having the expected function in biotechnology applications. The research outcomes will be included in graduate and undergraduate courses. Minority students, women, undergraduates, including those at primarily teaching institutions, and high-school students will be involved in different parts of the research. The participants of the project present their results at scientific conferences.The project will result in 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 research objectives 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 to 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 complexes, and use structure-based approaches to predict the effects of SAVs. This collaborative project combines highly complementary areas of expertise of the US team, on high-throughput modeling of protein interactions, and the UK team, on protein structure prediction and SAV effects. The results of the project will be available athttp://vakser.compbio.ku.edu and http://www.sbg.bio.ic.ac.uk.