Abstract
Proteins play a fundamental role in biology, and their thermostability is essential for their proper functionality. The precise measurement of thermostability is crucial, traditionally relying on resource-intensive experiments. Recent advances in deep learning, particularly in protein language models (PLMs), have significantly accelerated the progress in protein thermostability prediction. These models utilize various biological characteristics or deep representations generated by PLMs to represent the protein sequences. However, effectively incorporating structural information, based on the PLM embeddings, while not considering atomic protein structures, remains an open and formidable challenge. Here, we propose a novel Protein Contrast-enhanced Structure-Aware (ProCeSa) model that seamlessly integrates both sequence and structural information extracted from PLMs to enhance thermostability prediction. Our model employs a contrastive learning scheme guided by the categories of amino acid residues, allowing it to discern intricate patterns within protein sequences. Rigorous experiments conducted on publicly available data sets establish the superiority of our method over state-of-the-art approaches, excelling in both classification and regression tasks. Our results demonstrate that ProCeSa addresses the complex challenge of predicting protein thermostability by utilizing PLM-derived sequence embeddings, without requiring access to atomic structural data.