Work

Large language models enhance annotation of enzymes in metagenomes

Year: 2026

Type: article

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.

Source: Science Advances

DOI: 10.1126/sciadv.aee4389

Authors: Lei Zheng, Bowen Li, Siqi Xu, Junnan Chen, Guanxiang Liang

Institutions: Center for Life Sciences, Tsinghua University, Shanxi Medical University

Language: English

Topic: Genomics and Phylogenetic Studies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $4,500

Funder: National Natural Science Foundation of China