Constraint-Aware Optimization for Robust Protein Stability Prediction

• URL: http://arxiv.org/abs/2606.08100v1
• Date: Sat, 06 Jun 2026 11:04:52 GMT
• Title: Constraint-Aware Optimization for Robust Protein Stability Prediction
• Abstract summary: Multimodal $G$ integrating protein language models with inverse-folding representations achieve strong in-distribution accuracy on the Megascale dataset.<n>Existing approaches address these limitations primarily through additional architectural components.<n>We introduce a constraint-aware optimization framework combining Balanced Mean Squared Error, a Siamese anti-symmetric regularizer, and a novel OOD-margin loss on the per-position predictors feature representation.
• Score: 0.3316543849024811
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal $ΔΔG$ predictors integrating protein language models with inverse-folding representations achieve strong in-distribution accuracy on the Megascale dataset but exhibit limited robustness on out-of-distribution (OOD) proteins, persistent forward-reverse bias on paired-mutation benchmarks, and under-representation of rare stabilizing mutations. Existing approaches address these limitations primarily through additional architectural components, leaving optimization-level intervention comparatively underexplored. We introduce a constraint-aware optimization framework combining Balanced Mean Squared Error, a Siamese anti-symmetric regularizer, and a novel OOD-margin consistency loss on the per-position feature representation, requiring no architectural changes to the SPURS backbone. Across eleven benchmarks and three random seeds, the framework improves Spearman correlation on S669 from 0.486 to 0.540 ($σ=0.002$ across seeds), matching the published SPURS baseline (0.50) without architectural modification, and on S461 from 0.653 to 0.711, with consistent smaller gains on five additional OOD datasets. A controlled diagnostic on Ssym reveals that anti-symmetric training does not eliminate systematic forward-reverse bias, indicating that gains arise through implicit regularization rather than exact thermodynamic constraint enforcement.

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