TourSynbio-Search: A Large Language Model Driven Agent Framework for Unified Search Method for Protein Engineering

TL;DR

TourSynbio-Search introduces a large language model-powered agent framework that enables unified, natural language-based search across multiple protein and literature databases, significantly improving accessibility and efficiency in protein engineering research.

Contribution

It presents a novel multimodal LLM-driven agent framework with dual modules for literature and protein data search, enhancing information retrieval in protein engineering.

Findings

Effective retrieval of scientific literature and protein data.

Reduced technical barriers for researchers.

Streamlined access to complex biological information.

Abstract

The exponential growth in protein-related databases and scientific literature, combined with increasing demands for efficient biological information retrieval, has created an urgent need for unified and accessible search methods in protein engineering research. We present TourSynbio-Search, a novel bioinformatics search agent framework powered by the TourSynbio-7B protein multimodal large language model (LLM), designed to address the growing challenges of information retrieval across rapidly expanding protein databases and corresponding online research literature. The agent's dual-module architecture consists of PaperSearch and ProteinSearch components, enabling comprehensive exploration of both scientific literature and protein data across multiple biological databases. At its core, TourSynbio-Search employs an intelligent agent system that interprets natural language queries, optimizes search parameters, and executes search operations across major platforms including UniProt, PDB, ArXiv, and BioRxiv. The agent's ability to process intuitive natural language queries reduces technical barriers, allowing researchers to efficiently access and analyze complex biological data without requiring extensive bioinformatics expertise. Through detailed case studies in literature retrieval and protein structure visualization, we demonstrate TourSynbio-Search's effectiveness in streamlining biological information retrieval and enhancing research productivity. This framework represents an advancement in bridging the accessibility gap between complex biological databases and researchers, potentially accelerating progress in protein engineering applications. Our codes are available at: https://github.com/tsynbio/Toursynbio-Search