UNAAGI: Atom-Level Diffusion for Generating Non-Canonical Amino Acid Substitutions

A central challenge in structural biology is identifying beneficial amino acid substitutions, a problem that underpins both mutational effect prediction and protein engineering. Recent inverse-folding models, trained to reconstruct sequences from structure, have shown considerable promise for identifying functional mutations. However, current approaches are constrained to designing sequences composed exclusively of canonical amino acids (CAAs). Non-canonical amino acids (NCAAs) offer greater chemical diversity and are frequently used for protein engineering in vivo, yet they remain largely inaccessible to current variant effect prediction methods. To address this gap, we introduce UNAAGI, a diffusion-based generative model that reconstructs residue identities from atomic-level structure using an E(3)-equivariant framework. By modeling side chains in full atomic detail rather than as discrete tokens, UNAAGI enables the exploration of canonical and non-canonical amino acid substitutions within a unified generative paradigm. We evaluate UNAAGI zero-shot, without mutational-effect supervision or task-specific fine-tuning, on experimental mutational-effect benchmarks and demonstrate substantially improved performance on NCAA substitutions relative to existing methods. Furthermore, our results suggest a shared methodological foundation between protein engineering and structure-based drug design, motivating a unified training framework across these domains.