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

2026-05-05

AI Summary

This work proposes the first single-stage, all-atom protein co-design framework that overcomes the limitations of traditional two-stage approaches, which struggle to achieve atomic-level precision and flexibly incorporate non-canonical amino acids. Built upon a unified multimodal diffusion model, the method jointly generates discrete atom types and continuous atomic coordinates in a single forward pass, directly inferring residue identities from the resulting atomic arrangements. This paradigm inherently supports non-canonical amino acids and eliminates the artificial separation between sequence and structure design. Evaluated on both unconditional protein generation and protein binder design tasks, the approach substantially outperforms existing single- and two-stage methods, achieving up to a tenfold increase in success rate on challenging design benchmarks.

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.

Problem

• protein co-design
• atomic-level modeling
• non-canonical amino acids
• multimodal diffusion
• generative modeling

Innovation

• fully atomic
• unified multimodal diffusion
• protein co-design
• non-canonical amino acids
• one-stage generative modeling