VibeGen: Agentic end-to-end de novo protein design for tailored dynamics using a language diffusion model

Abstract

Proteins are dynamic molecular machines whose biological functions, spanning enzymatic catalysis, signal transduction, and structural adaptation, are intrinsically linked to their motions. We introduce VibeGen, a generative AI model based on 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. Via direct validation using full-atom molecular simulations, we demonstrate that the designed proteins accurately reproduce the prescribed normal mode amplitudes across the backbone while adopting various stable, functionally relevant structures. Generated sequences are de novo, exhibiting no significant similarity to natural proteins, thereby expanding the accessible protein space beyond evolutionary constraints. Our model establishes a direct, bidirectional link between sequence and vibrational behavior, unlocking efficient pathways for engineering biomolecules with tailored dynamical and functional properties. It holds broad implications for the rational design of enzymes, dynamic scaffolds, and biomaterials via dynamics-informed protein engineering.