Large language models enhance annotation of enzymes in metagenomes.

Abstract

Metagenomic data have notable biological potential, but their functional interpretation is frequently impeded by incomplete protein function annotations. Accurate enzyme annotation is essential for elucidating the metabolic capabilities of microbial communities within metagenomic datasets. To address this challenge, we developed FEDKEA, an enzyme annotation tool leveraging protein language models, and provided a web platform for its use. In addition, we designed a user-friendly, FEDKEA-based metagenomic pipeline, MEnzMap, which encompasses the entire analysis workflow-from raw data quality control to function prediction and downstream analyses. Applying MEnzMap to human gut metagenomic data from the iHMP2 project, we generated a comprehensive enzyme profile landscape for both healthy individuals and patients with inflammatory bowel diseases. These tools provide an efficient method for the functional annotation of microbial dark matter and facilitate the identification of disease-associated enzymes.