Artificial protein sequences enable recognition of vicinal and distant protein functional relationships

High divergence in protein sequences makes the detection of distant protein relationships through homology-based approaches challenging. Grouping protein sequences into families, through similarities in either sequence or 3-D structure, facilitates in the improved recognition of protein relationships. In addition, strategically designed protein-like sequences have been shown to bridge distant structural domain families by serving as artificial linkers. In this study, we have augmented a search database of known protein domain families with such designed sequences, with the intention of providing functional clues to domain families of unknown structure. When assessed using representative query sequences from each family, we obtain a success rate of 94% in protein domain families of known structure. Further, we demonstrate that the augmented search space enabled fold recognition for 582 families with no structural information available a priori. Additionally, we were able to provide reliable functional relationships for 610 orphan families. We discuss the application of our method in predicting functional roles through select examples for DUF4922, DUF5131, and DUF5085. Our approach also detects new associations between families that were previously not known to be related, as demonstrated through new sub-groups of the RNA polymerase domain among three distinct RNA viruses. Taken together, designed sequences-augmented search databases direct the detection of meaningful relationships between distant protein families. In turn, they enable fold recognition and offer reliable pointers to potential functional sites that may be probed further through direct mutagenesis studies.