Whole-proteome structure/function prediction in uropathogenic E. coli reveals previously missed host-microbe and microbe-phage interaction pathways

The rapid advancement of high-throughput sequencing technologies has vastly increased the number of known protein sequences, but the experimental characterization of their structures and functions lags behind. This gap in knowledge impedes our understanding of biological mechanisms of these proteins, hinders the interpretation of high-throughput experiments, and exposes a significant challenge in modern biology: deducing the structural and functional information of proteins based on their sequences. Most computational approaches rely on homology with well-annotated proteins, yet many proteins lack identifiable homologues, reducing the power of this approach. Here, we integrated cutting-edge protein structure and function prediction methods to develop a complete sequence-structure-function pipeline that predicts structures and functions based on primary sequences. We applied this pipeline to predict the structure and function of all proteins in Escherichia coli UTI89, a model strain of uropathogenic E. coli. Based on the predicted functions, we performed enrichment analysis on the whole genome and revealed the possible roles and related biological mechanisms of poorly annotated proteins in this organism. Moreover, the performance of our pipeline was further validated through detailed case studies of the UTI89_C0931 and ybtS genes. Finally, we compiled the UTI89 structure and function database (https://seq2fun.dcmb.med.umich.edu/UTI89), offering it as a crucial resource to aid researchers in elucidating the roles of unannotated proteins. This database aims to bridge critical knowledge gaps in microbial pathogenicity and resistance, enhancing our capacity to tackle emerging health threats.Author summary: Each bacterial genome encodes thousands of different proteins, but for most of them we still do not know what they actually do. This is true even for the bacteria that make us sick: in UTI89, a strain of uropathogenic Escherichia coli used worldwide to study urinary tract infections, nearly 90% of proteins are poorly described in public databases. Because a protein’s three-dimensional shape defines the job it can perform, we built an automated pipeline that predicts protein structures based on their sequences as efficiently as possible, and then uses those shapes, along with other evidence, to predict the proteins’ functions. We applied our pipeline to all 5,192 UTI89 proteins, adding more than 31,000 new functional labels. The additions revealed a clear pattern: the proteins that databases had overlooked are disproportionately those used to interact with the human host, such as sticking to bladder cells and scavenging the iron the host withholds, and those belonging to viruses embedded in the bacterial genome. All of our predictions are freely searchable online, and our pipeline can be freely applied to other organisms.