For most of biology, proteins were things you discovered. Now they’re things you can write. De novo protein design flips the deep-learning revolution around: instead of predicting the structure of a protein that already exists, it invents proteins that never did. It started with physics — Top7, a ’novel sequence and topology’ whose crystal structure matched the design to 1.2 Å, proving we could ’explore the large regions of the protein universe not yet observed in nature.’ Then deep learning: ‘hallucination’ inverts a structure predictor to dream new folds (27 of 129 designs folded in the lab); ProteinMPNN writes the sequence for a given shape with ‘52.4% sequence recovery compared with 32.9% for Rosetta’; RFdiffusion borrows image-generation diffusion to design binders and enzymes ‘from simple molecular specifications,’ with a cryo-EM-confirmed influenza binder ’nearly identical to the design model’; and Chroma treats design ‘as Bayesian inference under external constraints’ — steered by shape, symmetry, even ’natural-language prompts.’ The predictor that made it possible, AlphaFold, is also the referee that validates it. Prediction and design: two directions of one map.
