Multi-Scenario Generative Protein Design via Preference Alignment

Generative models can now design proteins that perform one function in one context: bind a given target, scaffold a given motif, catalyze a given reaction. However, many applications need a sequence that behaves differently depending on its context, like a binder that engages two unrelated targets, or a switch that adopts one fold on its own and another once a ligand is bound. Training a model for such multi-scenario tasks directly is impractical, since structural data of the same protein across contexts is scarce. To address this, we introduce Control-Proteina (Ctrl-P), which instead coordinates separately trained models at inference time, eliminating the need for training on multi-scenario data: each model expresses its preferences as a population of sequences and conditions its own generation on the preferences of the others, so that iterative refinement converges on designs that succeed in every context. In a multi-target protein–protein interaction benchmark that spans similar and dissimilar binding interfaces, Ctrl-P solves 18 of 20 target pairs, including 8 of the 10 challenging dissimilar pairs where the strongest baseline solves 2. We further demonstrate the generality of our approach by applying Ctrl-P to design allosteric motif switches that adopt specified conformations in response to a binding partner: it solves 8 of 12 tasks, compared to the strongest baseline’s 7, while finding more than twice as many successes per task on average. A case study on pan-arenavirus binder design shows that Ctrl-P generalizes beyond two scenarios, yielding candidates predicted to engage all three targets where no evaluated baseline yields any. Together, these results extend protein design toward programmable behavior across molecular contexts.