DISCO, DIffusion for Sequence-structure CO-design, is a multimodal generative model that simultaneously generates both a protein's amino acid sequence and its three-dimensional atomic structure. This might sound like an incremental advance, but it represents a fundamental departure from how the field has worked.

The wet-lab pipeline of existing generative approaches, including recent models like RFdiffusion2[5,6], RFdiffusion3[7], and BoltzGen[8], work in two separate stages: first generate a protein backbone, then use a separate inverse-folding network such as LigandMPNN[9] to predict what amino acid sequence would fold into that shape. This decoupled pipeline cannot use sequence-level signals to guide backbone design, or structural context to inform sequence choices.

DISCO eliminates this handoff entirely. It learns a joint distribution over discrete amino acid tokens and continuous 3D coordinates, denoising both simultaneously. The mathematical foundation is elegant: by independently sampling noise per modality during training, the model provably learns the joint reverse process using only unimodal losses, no special joint supervision required[10]. The coupling emerges from the architecture.