基於語言擴散模型的端到端自主蛋白質設計：實現定制化動力學

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

蛋白質是動態的分子機器，其生物功能——包括酶催化、信號傳導和結構適應——與其運動本質相關。然而，由於序列、結構和分子運動之間存在複雜且多重的關係，設計具有特定動態特性的蛋白質仍是一大挑戰。本文介紹了VibeGen，這是一個生成式AI框架，能夠基於正態模態振動進行端到端的從頭蛋白質設計。VibeGen採用了一種雙模型架構，包含一個根據指定振動模式生成序列候選的蛋白質設計器，以及一個評估其動態準確性的蛋白質預測器。這種方法在設計過程中協同了多樣性、準確性和新穎性。通過全原子分子模擬作為直接驗證，我們展示了所設計的蛋白質在保持各種穩定且功能相關結構的同時，精確地再現了主鏈上規定的正態模態振幅。值得注意的是，生成的序列是從頭設計的，與天然蛋白質無顯著相似性，從而將可探索的蛋白質空間擴展至超越進化限制的範疇。我們的工作將蛋白質動力學整合到生成式蛋白質設計中，並建立了序列與振動行為之間直接的雙向聯繫，為工程化具有定制動態和功能特性的生物分子開闢了新途徑。這一框架對於靈活酶、動態支架和生物材料的理性設計具有廣泛意義，為基於動力學的AI驅動蛋白質工程鋪平了道路。

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.