基于语言扩散模型的端到端自主蛋白质设计：定制化动态特性研究

Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a Language Diffusion Model

February 14, 2025

作者: Bo Ni, Markus J. Buehler

cs.AI

摘要

蛋白质是动态的分子机器，其生物功能——包括酶催化、信号传导和结构适应——与其运动状态密不可分。然而，由于序列、结构与分子运动之间复杂且多对一的关系，设计具有特定动态特性的蛋白质仍面临挑战。本文介绍VibeGen，一种生成式AI框架，它能够基于正常模式振动进行端到端的全新蛋白质设计。VibeGen采用了一种双模型代理架构，包含一个根据指定振动模式生成序列候选的蛋白质设计器，以及一个评估这些序列动态准确性的蛋白质预测器。这一方法在设计过程中实现了多样性、准确性和新颖性的协同。通过全原子分子模拟作为直接验证，我们展示了所设计的蛋白质在保持主链上规定的正常模式振幅的同时，能够形成多种稳定且功能相关的结构。值得注意的是，生成的序列是全新的，与天然蛋白质无显著相似性，从而将可探索的蛋白质空间扩展至进化限制之外。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，为工程化具有定制动态和功能特性的生物分子开辟了新途径。这一框架对柔性酶、动态支架和生物材料的理性设计具有广泛意义，为基于动力学的AI驱动蛋白质工程铺平了道路。

English

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.