ProSAM: Modular and Energy-Guided Fine-Tuning of Protein Language Models for Structure Prediction

Fine-tuning Protein Language Models (PLMs) for structure prediction is limited by uniform feature treatment and the absence of uncertainty modeling during training. We introduce ProSAM, a modular framework that decomposes PLM representations via a Dual-path Encoder, modulates them through a Protein-Aware Structural Adapter (PASA) and Fold-Specific Prompting (FSP), and regularizes outputs with an Energy-Guided Conformational Uncertainty (ECU) module. On CASP14, ProSAM achieves TM-score 0.892±0.005 and RMSD-Cα 1.23±0.08A, improving +4.1 points over ESMFold (p<0.01, paired Wilcoxon, Cohen's =1.42). Gains persist on CASP15 (+3.4), CAMEO-hard, and PDB-2022, and are largest for low-MSA-depth targets (+4.9). ProSAM outperforms LoRA and Adapter baselines while adding only 12.3M parameters (1.9% of the frozen PLM). Code and weights will be released upon acceptance.