Using deep learning to annotate the protein universe

A deep learning model predicts protein functional annotations for unaligned amino acid sequences. Understanding the relationship between amino acid sequence and protein function is a long-standing challenge with far-reaching scientific and translational implications. State-of-the-art alignment-based techniques cannot predict function for one-third of microbial protein sequences, hampering our ability to exploit data from diverse organisms. Here, we train deep learning models to accurately predict functional annotations for unaligned amino acid sequences across rigorous benchmark assessments built from the 17,929 families of the protein families database Pfam. The models infer known patterns of evolutionary substitutions and learn representations that accurately cluster sequences from unseen families. Combining deep models with existing methods significantly improves remote homology detection, suggesting that the deep models learn complementary information. This approach extends the coverage of Pfam by >9.5%, exceeding additions made over the last decade, and predicts function for 360 human reference proteome proteins with no previous Pfam annotation. These results suggest that deep learning models will be a core component of future protein annotation tools.

Automated summary

This article describes a method that uses deep learning models to predict functional annotations for unaligned protein amino acid sequences. The models are trained on rigorous benchmark assessments and can accurately predict the function of sequences across different protein families. The results show that deep learning models can significantly improve remote homology detection and expand the coverage of existing annotation tools. These models are expected to be a core component of future protein annotation tools.