使用语言扩散模型实现面向定制动力学的智能体端到端从头蛋白质设计
Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a Language Diffusion Model
AI总结：
本文提出 VibeGen 智能体双模型框架，以简正模式振动为条件端到端生成全新蛋白质，并经全原子模拟验证其可定制动力学。
摘要：
蛋白质是动态的分子机器，其生物功能（涵盖酶催化、信号转导和结构适应）与其运动具有内在联系。然而，由于序列、结构与分子运动之间存在复杂且简并的关系，设计具有目标动态特性的蛋白质仍是一项挑战。在此，我们提出 VibeGen，这是一个生成式 AI 框架，能够以简正模式振动为条件进行端到端的从头蛋白质设计。VibeGen 采用智能体双模型架构，包括一个蛋白质设计器和一个蛋白质预测器：前者根据指定的振动模式生成候选序列，后者评估这些候选序列的动态准确性。该方法在设计过程中协同实现多样性、准确性和新颖性。通过将全原子分子模拟作为直接验证，我们证明所设计的蛋白质在形成多种稳定且与功能相关的结构的同时，能够在整个骨架上准确重现规定的简正模式振幅。值得注意的是，生成的序列是从头产生的，与天然蛋白质没有显著相似性，从而将可触及的蛋白质空间扩展到进化约束之外。我们的工作将蛋白质动力学整合到生成式蛋白质设计中，并在序列与振动行为之间建立了直接的双向联系，为工程化构建具有定制动态和功能特性的生物分子开辟了新途径。该框架对柔性酶、动态支架和生物材料的理性设计具有广泛意义，为基于动力学信息的 AI 驱动蛋白质工程铺平了道路。
英文摘要：
Proteins are dynamic molecular machines whose biological functions, spanning enzymatic catalysis, signal transduction, and structural adaptation, are intrinsically linked to their motions. Designing proteins with targeted dynamic properties, however, remains a challenge due to the complex, degenerate relationships between sequence, structure, and molecular motion. Here, we introduce VibeGen, a generative AI framework that enables end-to-end de novo protein design conditioned on normal mode vibrations. VibeGen employs an agentic dual-model architecture, comprising a protein designer that generates sequence candidates based on specified vibrational modes and a protein predictor that evaluates their dynamic accuracy. This approach synergizes diversity, accuracy, and novelty during the design process. Via full-atom molecular simulations as direct validation, we demonstrate that the designed proteins accurately reproduce the prescribed normal mode amplitudes across the backbone while adopting various stable, functionally relevant structures. Notably, generated sequences are de novo, exhibiting no significant similarity to natural proteins, thereby expanding the accessible protein space beyond evolutionary constraints. Our work integrates protein dynamics into generative protein design, and establishes a direct, bidirectional link between sequence and vibrational behavior, unlocking new pathways for engineering biomolecules with tailored dynamical and functional properties. This framework holds broad implications for the rational design of flexible enzymes, dynamic scaffolds, and biomaterials, paving the way toward dynamics-informed AI-driven protein engineering.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
