用大模型统一搜索蛋白数据与文献，让非专家也能高效研究蛋白工程。
TourSynbio-Search: A Large Language Model Driven Agent Framework for Unified Search Method for Protein Engineering
- 基于多模态大模型的智能代理，可理解自然语言查询并跨平台搜索
- 支持在UniProt、PDB、ArXiv等平台同步检索，提升信息获取效率
- 适合无生物信息学背景的研究人员快速获取蛋白相关知识
蛋白质数据库和科学文献的指数级增长，加之对高效生物信息检索日益增长的需求，迫切需要统一且易用的蛋白质工程检索方法。我们提出TourSynbio-Search，一个由TourSynbio-7B蛋白多模态大语言模型驱动的生物信息学搜索代理框架，旨在应对蛋白质数据库及科研文献快速增长带来的检索挑战。该代理采用双模块架构，包含PaperSearch与ProteinSearch组件，可全面探索科学文献与多个生物数据库中的蛋白质数据。其核心为智能代理系统，能解析自然语言查询，优化搜索参数，并在UniProt、PDB、ArXiv和BioRxiv等主要平台执行搜索操作。该系统通过处理直观的自然语言查询，降低了技术门槛，使研究人员无需具备深厚的生物信息学知识即可高效访问和分析复杂生物数据。通过文献检索与蛋白质结构可视化案例研究，我们验证了TourSynbio-Search在简化生物信息检索和提升研究效率方面的有效性。该框架有助于弥合复杂生物数据库与研究者之间的可及性鸿沟，有望加速蛋白质工程应用进展。代码已开源：https://github.com/tsynbio/Toursynbio-Search
原文摘要 · Abstract (English)
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
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