用AI代理系统加速酶设计，自动发现影响氧化还原性质的氨基酸位点。
Beyond Protein Language Models: An Agentic LLM Framework for Mechanistic Enzyme Design
- 构建多工具协同的AI代理框架，融合文献检索、结构解析与电势计算。
- 在铁硫簇附近定位关键残基突变，实现红移性质预测且效率远超人工。
- 适合计算生物学家和蛋白质工程研究者，推动自动化科学发现。
我们提出Genie-CAT，一种面向蛋白质设计的工具增强型大语言模型系统，旨在加速科学假说生成。以金属蛋白（如铁氧还蛋白）为例，该系统整合四项能力：基于文献的检索增强生成（RAG）、PDB文件结构解析、静电势计算及红氧化学性质的机器学习预测，形成统一的智能体工作流。通过结合自然语言推理与数据驱动、物理基础计算，系统可生成机制可解释、可验证的序列-结构-功能关联假说。在概念验证中，Genie-CAT自主识别[Fe-S]簇附近影响氧化还原性质的残基级修饰，其结果与专家假设高度一致，耗时仅为传统方法的极小部分。该框架表明，融合语言模型与领域专用工具的AI代理，可弥合符号推理与数值模拟的鸿沟，使大模型从对话助手转变为计算发现的协作伙伴。
原文摘要 · Abstract (English)
We present Genie-CAT, a tool-augmented large-language-model (LLM) system designed to accelerate scientific hypothesis generation in protein design. Using metalloproteins (e.g., ferredoxins) as a case study, Genie-CAT integrates four capabilities -- literature-grounded reasoning through retrieval-augmented generation (RAG), structural parsing of Protein Data Bank files, electrostatic potential calculations, and machine-learning prediction of redox properties -- into a unified agentic workflow. By coupling natural-language reasoning with data-driven and physics-based computation, the system generates mechanistically interpretable, testable hypotheses linking sequence, structure, and function. In proof-of-concept demonstrations, Genie-CAT autonomously identifies residue-level modifications near [Fe--S] clusters that affect redox tuning, reproducing expert-derived hypotheses in a fraction of the time. The framework highlights how AI agents combining language models with domain-specific tools can bridge symbolic reasoning and numerical simulation, transforming LLMs from conversational assistants into partners for computational discovery.
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