General Multimodal Protein Design Enables DNA-Encoding of Chemistry
Authors: name: Jarrid Rector-Brooks, name: Théophile Lambert, name: Marta Skreta, name: Daniel Roth, name: Yueming Long, name: Zi-Qi Li, name: Xi Zhang, name: Miruna Cretu, name: Francesca-Zhoufan Li, name: Tanvi Ganapathy, name: Emily Jin, name: Avishek Joey Bose, name: Jason Yang, name: Kirill Neklyudov, name: Yoshua Bengio, name: Alexander Tong, name: Frances H. Arnold, name: Cheng-Hao Liu
Organization: DISCO

Evolution is an extraordinary engine for enzymatic diversity, yet the chemistry it has explored remains a narrow slice of what DNA can encode. Deep generative models can design new proteins that bind ligands, but none have created enzymes without pre-specifying catalytic residues. We introduce DISCO (DIffusion for Sequence-structure CO-design), a multimodal model that co-designs protein sequence and 3D structure around arbitrary biomolecules, as well as inference-time scaling methods that optimize objectives across both modalities. Conditioned solely on reactive intermediates, DISCO designs diverse heme enzymes with novel active-site geometries. These enzymes catalyze new-to-nature carbene-transfer reactions, including alkene cyclopropanation, spirocyclopropanation, B-H, and C(sp^3)-H insertions, with high activities exceeding those of engineered enzymes. Random mutagenesis of a selected design further confirmed that enzyme activity can be improved through directed evolution. By providing a scalable route to evolvable enzymes, DISCO broadens the potential scope of genetically encodable transformations. Code is available at https://github.com/DISCO-design/DISCO.