Towards multistate protein design with all-atom generative models

Designing proteins that adopt defined conformations in different molecular contexts remains a central challenge in protein design. Here, we ask whether all-atom generative models can learn such context dependence and use it for multistate design. We first develop AtomDiff-X, a streamlined model for end-to-end all-atom protein design, and experimentally validate its designs. We then curate a large dataset of chameleon sequences, 6–11-residue segments that adopt different secondary structures in different protein contexts. To learn from these paired structural alternatives, we introduce a dual-track attention mechanism and fine-tune AtomDiff-X to reconstruct paired protein structures. The resulting model, AtomDiff-Chameleon, generates de novo chameleon sequences up to 48 residues long together with their structural contexts and achieves the best performance in an in silico benchmark. We further apply AtomDiff-Chameleon to design pairs of proteins with identical N-termini but different C-termini, yielding distinct structures that mimic alternative splicing to switch protein function. These results indicate that sequence–structure ambiguity in natural proteins provides scalable supervision for learning context-dependent conformations. Combined with end-to-end all-atom generation, this supervision offers a promising data-driven route towards multistate protein design.