title: Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a
    Language Diffusion Model
summary: 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.
paper_authors._id: 67b306ba817e86482ef224d5
paper_authors.name: Bo Ni
paper_authors._id: 67b306ba817e86482ef224d6
paper_authors.name: Markus J. Buehler
paper_title: Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a
    Language Diffusion Model
paper_summary: 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.
paper_ai_keywords: generative AI framework agentic dual-model architecture protein designer protein predictor normal mode vibrations full-atom molecular simulations normal mode amplitudes de novo protein design