Stable de novo protein design via joint conformational landscape and sequence optimization.

Generative protein modeling provides advanced tools for designing diverse protein sequences and structures. However, accurately modeling the conformational landscape and designing sequences remain critical challenges: ensuring that the designed sequence reliably folds into the target structure as its most stable conformation, and optimizing the sequence for a given suboptimal fixed input structure. In this study, we present a systematic analysis of jointly optimizing sequence-to-structure and structure-to-sequence mappings. This approach enables us to find optimal solutions for modeling the conformational landscape. We validate our approach with large-scale protein stability measurements, demonstrating that joint optimization is superior for designing stable proteins using a joint model (TrRosetta and TrMRF) and for achieving high accuracy in stability prediction when jointly modeling (half-masked ESMFold pLDDT + ESM2 Pseudo-likelihood). We further investigate features of sequences generated from the joint model and find that they exhibit higher frequencies of hydrophilic interactions, which may help maintain both secondary structure registry and pairing-features not captured by structure-to-sequence modeling alone.