Annotating the microbial dark matter with HiFi-NN

Abstract

The accurate computational annotation of protein sequences with enzymatic function remains a fundamental challenge in bioinformatics. Here, we present HiFi-NN (Hierarchically-Finetuned Nearest Neighbor search) which annotates protein sequences to the 4th level of Enzyme Commission (EC) number with greater precision and recall than state-of-the-art deep learning methods. Furthermore, we show that this method can correctly identify the EC number of a given sequence to lower identities than BLASTp. We show that performance can be improved by increasing the diversity of the lookup set in both sequence space and the environment the sequence has been sampled from. We proceed to show that we can correct specific mis-annotations in the BRENDA enzymes database reproducing results found by others. Finally, we use HiFi-NN to annotate functional dark-matter protein sequences from NMPFamDB. Our findings pave the way for more accurate functional annotation in silico, especially for proteins from distant sequence space.