TALE-cmap: Protein function prediction based on a TALE-based architecture and the structure information from contact map

Abstract

Protein function prediction is one of the most critical tasks in bioinformatics. The computational predictors that can accurately predict the protein functions from their sequences are highly desired. With the development of the protein structure prediction methods, it is interesting to explore a new approach to use the predicted protein structures to improve the predictive performance of protein function prediction. TALE is a successful sequence-based method for protein function prediction. Therefore, in this study, we employed the TALE-based architecture to integrate sequence embeddings, contact map embeddings, and GO label embeddings to predict protein functions. These embeddings represent the proteins at the sequence, structure, and function levels. The TALE-cmap predictor outperforms the other state-of-the-art methods, indicating that structural information is essential for protein function prediction.