ProtAgents：通过结合物理学与机器学习的大语言模型多智能体协作实现蛋白质发现
ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learning
AI总结：
ProtAgents利用大语言模型驱动的多智能体协作平台，结合知识检索、结构分析和物理模拟，实现从头蛋白质设计及目标力学性能优化，展示了LLMs在多目标材料问题中的潜力。
摘要：
设计超越自然界中存在的从头蛋白质，在科学和工程应用方面具有重大前景。当前的蛋白质设计方法通常依赖于基于人工智能的模型，例如通过将蛋白质结构与材料性质或反之相联系来解决端到端问题的代理模型。然而，这些模型往往专注于特定的材料目标或结构性质，在需要将领域外知识纳入设计过程或进行综合分析时，限制了它们的灵活性。在本研究中，我们介绍了ProtAgents，一个基于大语言模型（LLMs）的从头蛋白质设计平台，其中多个具有不同能力的AI智能体在动态环境中协作解决复杂任务。智能体开发的多样性允许其在多个领域具备专业知识，包括知识检索、蛋白质结构分析、基于物理的模拟和结果分析。由LLMs赋能的智能体之间的动态协作，提供了一种解决蛋白质设计和分析问题的通用方法，本研究中的多个示例证明了这一点。所关注的问题包括设计新蛋白质、分析蛋白质结构以及通过物理模拟获取新的第一性原理数据——自然振动频率。该系统的协同努力使得能够强大地自动化和协同地设计具有目标力学性能的从头蛋白质。一方面，智能体设计的灵活性，另一方面，它们通过基于LLM的动态多智能体环境进行自主协作的能力，释放了LLMs在解决多目标材料问题方面的巨大潜力，并为自主材料发现和设计开辟了新途径。
英文摘要：
Designing de novo proteins beyond those found in nature holds significant promise for advancements in both scientific and engineering applications. Current methodologies for protein design often rely on AI-based models, such as surrogate models that address end-to-end problems by linking protein structure to material properties or vice versa. However, these models frequently focus on specific material objectives or structural properties, limiting their flexibility when incorporating out-of-domain knowledge into the design process or comprehensive data analysis is required. In this study, we introduce ProtAgents, a platform for de novo protein design based on Large Language Models (LLMs), where multiple AI agents with distinct capabilities collaboratively address complex tasks within a dynamic environment. The versatility in agent development allows for expertise in diverse domains, including knowledge retrieval, protein structure analysis, physics-based simulations, and results analysis. The dynamic collaboration between agents, empowered by LLMs, provides a versatile approach to tackling protein design and analysis problems, as demonstrated through diverse examples in this study. The problems of interest encompass designing new proteins, analyzing protein structures and obtaining new first-principles data -- natural vibrational frequencies -- via physics simulations. The concerted effort of the system allows for powerful automated and synergistic design of de novo proteins with targeted mechanical properties. The flexibility in designing the agents, on one hand, and their capacity in autonomous collaboration through the dynamic LLM-based multi-agent environment on the other hand, unleashes great potentials of LLMs in addressing multi-objective materials problems and opens up new avenues for autonomous materials discovery and design.
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