Supplemental Tables S1 and S2 for Combining structural modeling and deep learning to calculate the E. coli protein interactome and functional networks

We report on the integration of three methods that are computationally efficient enough to predict, on a proteome-wide scale, whether two proteins are likely to form a binary complex. The methods include PrePPI, which uses three-dimensional structure information as a basis for predictions, Topsy-Turvy, which analyzes sequences using a protein language model, and ZEPPI, which uses evolutionary information to evaluate protein-protein interfaces. We demonstrate how these methods can be integrated and validate the performance of the integrated method and its separate components at predicting E. coli protein-protein interactions (PPIs) through testing on the HINT high-quality literature-curated database of binary PPIs. The integrated method has better performance and identifies more high-confidence interactions than any of the component methods. The AF3Complex algorithm was used to predict the structures of 374 PPIs with a large fraction having at least partially overlapping interfaces with PrePPI models of the same complex. Finally, we clustered the high-confidence E. coli interactome and obtained 385 subnetworks which have high functional coherence defined by enrichment of Gene Ontology Biological Process terms, thus, illustrating that our methods, which contain no explicit functional information, provide biologically meaningful PPIs. Biological insights derived from the subnetworks, including the annotation of proteins of unknown function, are discussed in detail. The functional insights obtained from structure-based PPI predictions highlight the applicability of the comprehensive E. coli interactome presented here.