MuseDrift: Navigating Protein Evolutionary Manifolds with Conditional Discrete Diffusion

Protein engineering often requires variants that differ from a wild-type (WT) sequence by a specified amount while remaining structurally and functionally plausible. Controlling WT similarity alone does not specify which residues should change or how their substitutions should be coordinated. Here, we introduce MuseDrift, a conditional discrete diffusion model that generates variants from a WT sequence and a target sequence identity, without task-specific property guidance. Trained on WT-homolog pairs spanning different identities, MuseDrift uses pretrained WT representations as context and identity-aware modulation to condition denoising. Compared with editing baselines at the same requested identities, its variants achieve higher mean structural prediction confidence and greater similarity to predicted WT structures. On the CAMEO dataset, it tracks requested identities spanning 30%-95%, with mean absolute errors of 0.52-2.08 percentage points across targets. Its generated variants also receive more favorable scores from external mutation-effect predictors than those from the evaluated baselines. Together, these results support MuseDrift as a framework for controlled exploration of WT-centered protein sequence space.