GOAnnotator: accurate protein function annotation using automatically retrieved literature.

Abstract

Automated protein function prediction/annotation (AFP) is vital for understanding biological processes and advancing biomedical research. Existing text-based AFP methods including the state-of-the-art method, GORetriever, rely on expert-curated relevant literature, which is costly and time-consuming, and cover only a small portion of the proteins in UniProt. To overcome this limitation, we propose GOAnnotator, a novel framework for automated protein function annotation. It consists of two key modules: PubRetriever, a hybrid system for retrieving and re-ranking relevant literature, and GORetriever+, an enhanced module for identifying Gene Ontology (GO) terms from the retrieved texts. Extensive experiments over three benchmark datasets demonstrate that GOAnnotator delivers high-quality functional annotations, surpassing GORetriever in realistic situations by uncovering unique literature and predicting additional functions. These results highlight its great potential to streamline and enhance annotation of protein functions without relying on manual curation. The code and data are available at https://github.com/ZhuLab-Fudan/GOAnnotator.