Functional domain annotation by structural similarity

Abstract

Traditional automated in silico functional annotation uses tools like Pfam that rely on sequence similarities for domain annotation. However, structural conservation often exceeds sequence conservation, suggesting an untapped potential for improved annotation through structural similarity. This approach was previously overlooked before the AlphaFold2 introduction due to the need for more high-quality protein structures. Leveraging structural information especially holds significant promise to enhance accurate annotation in diverse proteins across phylogenetic distances. In our study, we evaluated the feasibility of annotating Pfam domains based on structural similarity. To this end, we created a database from segmented full-length protein structures at their domain boundaries, representing the structure of Pfam seeds. We used Trypanosomabrucei, a phylogenetically distant protozoan parasite as our model organism. Its structome was aligned with our database using Foldseek, the ultra-fast structural alignment tool, and the top non-overlapping hits were annotated as domains. Our method identified over 400 new domains in the T. brucei proteome, surpassing the benchmark set by sequence-based tools, Pfam and Pfam-N, with some predictions validated manually. We have also addressed limitations and suggested avenues for further enhancing structure-based domain annotation.