Abstract

The coming of age of in silico PPI screening

Protein-protein interactions (PPI) play critical roles in biology, but the structures of many eukaryotic protein complexes are unknown, and there are likely many interactions not yet identified. High-throughput experimental methods have been used to identify interactions, but there are considerable inconsistencies between datasets, and the methods do not provide high resolution structural information. We developed a proteome wide coevolution-guided PPI identification pipeline that incorporates a rapidly computable version of RoseTTAFold with the slower but more accurate AlphaFold to systematically identify and build structures for the protein complexes which mediate key processes in Eukaryotes. Our approach extends the range of large scale deep learning based structure modeling from monomeric proteins to protein assemblies. Following up on the many new interactions and complex structures should advance the understanding of a wide range of eukaryotic cellular processes and provide new targets for therapeutic intervention. Our results herald a new era of structural biology in which computation plays a fundamental role in both interaction discovery and structure determination.  

(Passcode: Q$Sx7+=C)