StabilityArc: Decoding Protein Sequence Embeddings into Generalizable Stability Landscapes

Abstract:Every protein has a unique stability landscape, but the physical consequences of mutation are governed by recurring biochemical constraints. We test whether a shared decoder, trained on measurements from diverse proteins, can interpret these constraints in an unseen target, enabling cross-protein transfer for initial experimental round prescreening. We present StabilityArc , which maps frozen ESMC-600M residue representations through a shared RoPE transformer to an Lx20 matrix of substitution effects; a symmetric, contact-aware residual aids in predicting epistasis in simultaneous substitutions. In 66 strict leave-one-protein-out evaluations covering 134,794 ProteinGym variants, StabilityArc achieves 0.7134 Spearman correlation, exceeding the strongest zero-shot baseline, ProSST-2048 (0.6526), by 0.0608. We further explore the utility of this method by providing the score as a prior for Kermut, achieving Spearman correlation of 0.8280 across three supervised split schemes, improving on Kermut's reported 0.8167.

Biomolecules (q-bio.BM); Machine Learning (cs.LG)

arXiv:2610.00742 [q-bio.BM]

(or arXiv:2610.00742v1 [q-bio.BM] for this version)

https://doi.org/10.48550/arXiv.2610.00742 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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