Improved enzyme functional annotation prediction using contrastive learning with structural inference

Summary

The article describes a bioinformatics approach for enzyme function prediction using a contrastive learning framework, integrating protein language and computer vision models, such as ESM-2 and ResNet50. The framework enhances sequence alignment and protein structure prediction with superior precision and recall, outperforming existing models. Tags like 'Transformer', 'Protein structure prediction', and 'Convolutional neural network' highlight the method's combination of advanced AI models to improve bioinformatics analyses.