Fusion2AI: functional re-annotation of the bacterial transparent matter proteins

Abstract

Determining the biological function of bacterial proteins remains a central challenge in microbiology. We previously developed Fusion, a reference-free scheme that clustered the tens of millions of known, unique, bacterial proteins into about four hundred thousand groups of functionally similar proteins, i.e. functions. For any newly identified proteins, Fusion function assignment could be performed using function-aware sequence alignment to each function’s representatives. However, Fusion functions lack human-readable descriptions of their functionality. Here, we leverage large language models (LLMs) to synthesize scattered, partial annotations of functions of individual proteins into coherent English-language descriptions for ∼100,000 clusters, i.e. a quarter of all Fusion functions. From these descriptions, we extract Gene Ontology (GO) terms and Enzyme Commission (EC) numbers. We validate these against annotations inferred from UniProtKB/Swiss-Prot. These annotations immediately expand functional coverage for both genome and microbiome -level analyses. For organisms in FusionDB, we annotate ≥50% more proteins than alignment to Swiss-Prot alone. For other bacteria, the gain is smaller but still reaches 30% for some organisms. For microbiomes, annotation coverage increases nearly ten-fold. Our work thus enables the analysis of microbial “transparent matter,” i.e. functions already described in earlier research that have not yet made it into usable annotations. Our annotated Fusion function database is freely available at https://services.bromberglab.org/fusion2ai and as a reference DB for metagenome annotation https://services.bromberglab.org/mifaser.