Protein Language Models Uncover Carbohydrate-Active Enzyme Function in Metagenomics

Abstract

In metagenomics, the pool of uncharacterized microbial enzymes presents a challenge for functional annotation. Among these, carbohydrate-active enzymes (CAZymes) stand out due to their pivotal roles in various biological processes related to host health and nutrition. Here, we present CAZyLingua, the first tool that harnesses protein language model embeddings to build a deep learning framework that facilitates the annotation of CAZymes in metagenomic datasets. Our benchmarking results showed on average a higher F1 score (reflecting an average of precision and recall) on the annotated genomes of Bacteroides thetaiotaomicron, Eggerthella lenta and Ruminococcus gnavus compared to the traditional sequence homology-based method in dbCAN2. We applied our tool to a paired mother/infant longitudinal dataset and revealed unannotated CAZymes linked to microbial development during infancy. When applied to metagenomic datasets derived from patients affected by fibrosis-prone diseases such as Crohn's disease and IgG4-related disease, CAZyLingua uncovered CAZymes associated with disease and healthy states. In each of these metagenomic catalogs, CAZyLingua discovered new annotations that were previously overlooked by traditional sequence homology tools. Overall, the deep learning model CAZyLingua can be applied in combination with existing tools to unravel intricate CAZyme evolutionary profiles and patterns, contributing to a more comprehensive understanding of microbial metabolic dynamics.

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

“…If these proteins could be accurately annotated, protein engineers would have access to a wealth of diverse candidates for engineering. While enzyme engineers have long been using multiple sequence alignments (MSAs) and homology to predict the functions of unannotated protein sequences, ML classification models extend these approaches and draw from more complete features describing protein sequences and structures to predict more specific functions, such as type of reactivity and k cat . ,− Focusing on known sequences without annotations, many of these methods aim to classify enzyme sequences based on their enzyme commission (EC) numbers, which is a hierarchical classification scheme that divides enzymes into general classes and then further subclasses, based on their catalytic activities (Figure A).…”