Self-Supervised Learning of Epistatic Mutation Effects in Structure-based Models

Predicting the impact of multiple point mutations on a given protein sequence remains a central challenge in protein engineering, in large part due to the nonlinear effects of epistasis. Experimental data sets that measure multiple mutations are scarce, motivating methods that learn from structural and evolutionary data. We introduce EvoEpi, a self-supervised framework for scoring double mutations from protein sequences and backbone structures without using phenotype labels in the training objective. EvoEpi learns from joint amino-acid frequencies in multiple sequence alignments (MSAs), retaining dependencies between residue pairs that single-position supervision discards. More specifically, our structure-based model predicts changes in log-frequency ratios resulting from double mutations. Trained on the single-chain subset of the ProteinMPNN training set and their corresponding MSAs, EvoEpi achieves global/mean per-protein Spearman correlations of $0.50/0.50$, $0.42/0.55$, and $0.62/0.51$ on ThermoMutDB-D, Megascale-D, and PTMul-D, respectively, outperforming all evaluated self-supervised baselines and reaching supervised-level performance, with higher correlations than all evaluated supervised predictors on ThermoMutDB-D and PTMul-D.