[论文解读] Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a Language Diffusion Model

Bo Ni, Markus J. Buehler|ArXiv.org|Feb 14, 2025

Evolution and Genetic Dynamics被引用 3

一句话总结

简要直述：引入 VibeGen，这是一个基于目标模态振动的端到端全新蛋白质设计框架，采用双模型架构来生成序列并预测动力学，经全原子模拟验证。

ABSTRACT

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.

研究动机与目标

• 设计具有与功能相关的定制动力学属性的蛋白质的动机。
• 开发一个以振动模态为条件的端到端生成框架。
• 通过双向评估将序列设计与动态准确性相连接。

提出的方法

• 提出 VibeGen：一个以法向模振动为条件的生成式AI框架。
• 使用一个智能的双模型系统：一个蛋白质设计器用于生成序列，一个蛋白质预测器用于评估动力学。
• 利用全原子分子模拟直接验证所给定的法向模振幅。
• 证明去 novo 序列再现目标的主链动力学并采用稳定、具功能性的结构。
• 显示生成的序列具有新颖性且与天然蛋白质没有显著相似性。

实验结果

研究问题

• RQ1 端到端的去 novo 蛋白质设计是否可以以规定的振动模为条件？
• RQ2 联合优化的设计器和预测器模型是否在生成的序列中实现了准确的动力学实现？
• RQ3 生成的、受振动条件约束的蛋白质相对于天然蛋白质是否具有新颖性？
• RQ4 全原子模拟是否能验证所设计的蛋白质确实再现了预期的法向模振幅？
• RQ5 将蛋白质动力学整合到生成式设计中对柔性酶和动态生物材料有何影响？

主要发现

• 该框架实现了以目标法向模振幅为条件的端到端设计。
• 设计的蛋白质在模拟中再现了规定的主链振动振幅。
• 生成的序列是去 novo 的，与天然蛋白质没有显著相似性。
• 设计的结构在各模态下都保持稳定且具有功能相关性。
• 该方法直接将序列与振动行为联系起来，扩展了超越进化的设计空间。