Predicting Protein Thermostability with Equivariant Graph Neural Networks and Contrastive Learning

However, current computational methods often struggle to jointly integrate evolutionary information, structural context, and the interactions between mutation sites and their local microenvironments, presenting obvious limitations in guiding protein modification practice.

To this end, this paper proposes the Equivariant Contrastive Learning Network (ECL-Net) for effectively predicting protein thermostability changes. Firstly, this method employs a joint sequence-structure representation strategy, deeply integrating sequence embeddings extracted by protein language models (PLMs) with spatial structural information captured by residue interaction networks, while an Equivariant Graph Neural Network (EGNN) is utilized for feature propagation and structural modeling.