POTTSENERGY: LEARNING INTERACTIONS FOR COMBINATORIAL PROTEIN STABILITY PREDICTION

Predicting the stability effects of combined mutations requires interaction models that generalize beyond individual substitutions. To meet this need, we introduce PottsEnergy, a compact framework that jointly designs structural encoding, explicit Potts parameterization, and sitewise self-supervision. Specifically, a backbone graph network predicts fields and pair couplings, while native-context pseudolikelihood and masked-context residue reconstruction train the same potentials used for mutation scoring. As a result, this design turns residue-level learning into a reusable sequence energy that combines single-site effects with explicit pair corrections. To evaluate this representation, we curate a higher-order stability benchmark from ten data sources. Even without stability-label training, PottsEnergy achieves state-of-the-art aggregate zero-shot ranking on this benchmark among the evaluated model families. Matched additive controls further show that the predictive contributions of the pair corrections are dataset-dependent. Separate diagnostics then distinguish these gains from interaction-sign accuracy. In addition, single-mutant adaptation tests the compact representation further. Together, these results connect residue reconstruction, combinatorial stability prediction, and candidate design through an explicit interaction-energy representation. More broadly, coupling learned interaction energies with quantum-assisted sampling could broaden the exploration of combinatorial sequence space for protein design.