# Thermophilic protein classification

The structure-based protein language model ESM3 treats proteins as if in vacuum, and without solvent information its per-residue outputs mislabel hydrogen bonds as destabilizing, with error that varies with solvent-accessible surface area. HEIWA adds water–residue attention to ESM3 residue embeddings, passing solvent-mediated force-field information through learnable water key vectors and a rotational angle term. On a thermophile classification proxy task, the HEIWA model's classification metrics stay broadly comparable to the baseline, while per-residue outputs correlate better with an RMSF benchmark and SASA; temperature-ramping molecular dynamics found 23 of 38 tested proteins more stable.

The structure-based protein language model ESM3 treats proteins as if in vacuum, and without solvent information its per-residue outputs mislabel hydrogen bonds as destabilizing, with error that varies with solvent-accessible surface area. HEIWA adds water–residue attention to ESM3 residue embeddings, passing solvent-mediated force-field information through learnable water key vectors and a rotational angle term. On a thermophile classification proxy task, the HEIWA model's classification metrics stay broadly comparable to the baseline, while per-residue outputs correlate better with an RMSF benchmark and SASA; temperature-ramping molecular dynamics found 23 of 38 tested proteins more stable.