Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a
  Language Diffusion Model
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.
67b306ba817e86482ef224d5
Bo Ni
67b306ba817e86482ef224d6
Markus J. Buehler
Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a
  Language Diffusion Model
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.
VibeGen, a generative AI framework using an agentic dual-model architecture, enables the design of de novo proteins with targeted dynamic properties by conditioning on normal mode vibrations.
generative AI agentic dual-model architecture protein designer protein predictor normal mode vibrations full-atom molecular simulations protein dynamics de novo protein design flexible enzymes dynamic scaffolds biomaterials