Implicit modeling of the conformational landscape and sequence allows scoring and generation of stable proteins
Cho, Y.; Dauparas, J.; Tsuboyama, K.; Rocklin, G.; Ovchinnikov, S.
Show abstract
Generative protein modeling provides advanced tools for designing diverse protein sequences and structures. However, accurately modeling the conformational landscape and designing sequences--ensuring that the designed sequence folds into the target structure as its most stable structure--remains a critical challenge. In this study, we present a systematic analysis of jointly optimizing P(structure|sequence) and P(sequence|structure), which enables us to find optimal solutions for modeling the conformational landscape. We support this approach with experimental evidence that joint optimization is superior for (1) designing stable proteins using a joint model (TrROS (TrRosetta) and TrMRF) (2) 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.
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