UKRI/BBSRC - NSF/BIO: Modeling of protein interactions to predict phenotypic effects of genetic mutations

UKRI/BBSRC - NSF/BIO：蛋白质相互作用建模以预测基因突变的表型效应

• 批准号: 2224122
• 负责人: Ilya Vakser
• 金额: $ 93.02万
• 依托单位: University of Kansas Center for Research Inc
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-15 至 2025-11-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2224122&HistoricalAwards=false
• 关键词: UKRI; BBSRC; NSF; BIO; Modeling

Structures of protein complexes are essential for interpretation of genetic mutations. Approaches to structural modeling of proteins and their interactions are rapidly developing, powered by advances in knowledge-based methods and better understanding of physical principles of protein structure and function. A vast amount of information on human genetic variation, including numerous single amino acid changes, is available from high-throughput sequencing. The project focuses on application of modeling techniques to the development of a public resource incorporating structural modeling with genetic amino acid variants and assessment of their functional impact. The long-term goals of this research are to gain insights into fundamental principles of molecular processes in living systems and the effect of genetic mutations on cellular mechanisms. This collaborative project combines highly complementary areas of expertise of the US team on modeling of protein interactions, and the UK team on protein structure prediction and missense variant effects. The teams will continue coordinating research and training activities, based on the project, with graduate and undergraduate programs locally, nationally and internationally. Minority students and women will be involved in different parts of the research. The project will enhance infrastructure for research and education. The US and UK teams have an extensive record of developing and disseminating public resources, used by many researchers worldwide. The project participants will be actively presenting their results at various multidisciplinary conferences and workshops.The approach incorporating predicted complexes in model organisms with genetic variants and their phenotypic effect assessment will be further developed. The objectives of the project are: (1) structural modeling of protein interactome; (2) assessment of phenotypic effects of genetic variation; and (3) public resource for structural characterization of protein interactome and phenotypic effects of genetic mutations. Computational approaches to structural characterization of protein complexes will incorporate recent advances in application of Deep Learning to prediction of protein structures and assemblies. The stereochemical approach to prediction of the mutation effect will involve modeling acceptable conformations for the variant side chains and evaluating the change in interactions. The GWYRE resource will incorporate protein complexes for model organisms, annotated by the phenotypic effects of missense variants. The project outcome will have a significant impact on the research field by development of the integrated public resource for structural characterization of protein interactomes and assessment of the phenotypic effects of genetic mutations. The results of the project will be available at http://www.gwyre.org and also at http://vakser.compbio.ku.edu and http://www.sbg.bio.ic.ac.uk.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

蛋白质复合物的结构对于解释基因突变至关重要。蛋白质及其相互作用的结构建模方法正在迅速发展，这得益于基于知识的方法的进步以及对蛋白质结构和功能的物理原理的更好理解。大量关于人类遗传变异的信息，包括许多单个氨基酸的变化，可从高通量测序中获得。该项目的重点是应用建模技术开发一个公共资源，将结构建模与遗传氨基酸变异和评估其功能的影响。这项研究的长期目标是深入了解生命系统中分子过程的基本原理以及基因突变对细胞机制的影响。该合作项目结合了美国团队在蛋白质相互作用建模方面的专业知识和英国团队在蛋白质结构预测和错义变体效应方面的专业知识，具有高度互补性。这些团队将继续协调研究和培训活动，根据该项目，与本地，国内和国际的研究生和本科生课程。少数民族学生和妇女将参与研究的不同部分。该项目将加强研究和教育的基础设施。美国和英国团队在开发和传播公共资源方面有着广泛的记录，被世界各地的许多研究人员使用。项目参与者将在各种多学科会议和讲习班上积极介绍他们的成果，并将进一步发展将预测的复合物纳入具有遗传变异的模式生物及其表型效应评估的方法。该项目的目标是：（1）蛋白质相互作用组的结构建模;（2）遗传变异的表型效应评估;（3）蛋白质相互作用组结构表征和遗传突变表型效应的公共资源。蛋白质复合物结构表征的计算方法将结合深度学习应用于蛋白质结构和组装预测的最新进展。预测突变效应的立体化学方法将涉及对变体侧链的可接受构象进行建模并评估相互作用的变化。GWYRE资源将纳入模式生物的蛋白质复合物，并通过错义变体的表型效应进行注释。通过开发用于蛋白质相互作用组结构表征和评估基因突变表型效应的综合公共资源，该项目成果将对研究领域产生重大影响。该项目的结果将在http://www.gwyre.org和http://vakser.compbio.ku.edu上提供，http://www.sbg.bio.ic.ac.uk.This奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。