Leveraging Unified Sequence-Structure Representations for Enhanced Protein Stability Prediction

Abstract

Protein thermal stability, quantified by the change in Gibbs free energy ({Delta}{Delta}G) upon mutation, is critical for drug design and enzyme engineering. Current multi-modal deep learning models, despite integrating sequence, often struggle with indirect information fusion and incomplete capture of sequence-structure interactions. We introduce ProStab-Former, addressing these limitations by establishing a unified sequence-structure representation space for protein stability prediction. It leverages a frozen, multi-modal protein foundation encoder for residue-level feature extraction. Fine-tuned modules include Stability-Aware Attention Layers (SAAL) with structural prior bias and mutation-aware gating, and an Epistatic Interaction Module for multi-point mutation prediction. The model achieves superior or competitive performance, surpassing a state-of-the-art baseline. Ablation confirms SAALs critical role; strong generalization is shown across tasks including melting temperature prediction and pathogenic mutation classification. Its exceptional efficiency, predicting numerous single-point mutations in a single pass, positions it as a practical tool for high-throughput protein engineering and variant effect analysis.