ArXiv TLDR

A-CODE: Fully Atomic Protein Co-Design with Unified Multimodal Diffusion

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2605.03360

Chaoran Cheng, Jiaqi Guan, Milong Ren, Chengyue Gong, Cong Liu + 3 more

q-bio.QMcs.LG

TLDR

A-CODE is a fully atomic, unified multimodal diffusion model for protein co-design, achieving superior designability and outperforming existing methods.

Key contributions

  • A-CODE is a fully atomic, one-stage model simultaneously refining atom types and coordinates.
  • Achieves superior designability for unconditional protein generation, outperforming all existing models.
  • Drastically improves binder design success rates, rivaling state-of-the-art two-stage methods.
  • Enables seamless modeling of non-canonical amino acids (ncAAs) with its atomic formulation.

Why it matters

This paper introduces a novel, fully atomic approach to protein co-design, overcoming limitations of traditional residue-level methods. It significantly advances generative modeling by enabling superior designability and new capabilities like non-canonical amino acid modeling. This establishes a versatile foundation for future biomolecular systems.

Original Abstract

We present A-CODE, a fully atomic unified one-stage protein co-design model that simultaneously refines discrete atom types and continuous atom coordinates. Unlike predominant two-stage methods that cascade structure design with amino acid-level sequence design, our approach is fully atomic within a unified multimodal diffusion framework, in which residue identities are inferred solely from atom-level predictions. Built upon the powerful all-atom architecture, A-CODE achieves superior designability for unconditional protein generation, outperforming all existing one-stage and two-stage design models. For binder design, A-CODE rivals and even outperforms existing state-of-the-art two-stage design models and, compared with the existing one-stage co-design model, achieves a drastic tenfold improvement in success rate on hard tasks. The inherent flexibility of our atomic formulation enables, for the first time, seamless adaptation to non-canonical amino acid (ncAA) modeling. Our fully atomic framework establishes a new, versatile foundation for all-atom generative modeling that can be naturally extended to complex biomolecular systems.

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