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

University Of Kansas Center For Research Inc, Lawrence KS

Investigators

Abstract

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.