LLM Agent Based Protein Function Prediction

Protein function prediction remains a fundamental challenge in computational biology. Here, we present a Large Language Model (LLM) agent-based system that improves protein function prediction performance using knowledge-augmented reasoning and multi-source evidence synthesis.Our approach integrates computational predictions with structured protein metadata, scientific literature, and ontological knowledge through a multi-stage reasoning process. An LLM agent equipped with specialized tools progressively refines functional predictions by querying constraints, cross-referencing evidence, and ensuring biological plausibility. Furthermore, the system provides detailed explanations for each prediction update, documenting the reasoning process and evidence sources.We evaluate our approach against established baseline methods across three Gene Ontology sub-ontologies using four complementary metrics, achieving superior performance in threshold-dependent measures, attaining the lowest Smin scores across all ontologies and the best Fmax for Molecular Function and Cellular Component ontologies. We make our code publicly available at https://github.com/bio-ontology-research-group/go-agent.