ResCNNT-fold: Combining residual convolutional neural network and Transformer for protein fold recognition from language model embeddings

Xinyi Qin; Guangzhong Liu; Min Liu

doi:10.1016/j.compbiomed.2023.107571

ResCNNT-fold: Combining residual convolutional neural network and Transformer for protein fold recognition from language model embeddings

Comput Biol Med. 2023 Oct 17:166:107571. doi: 10.1016/j.compbiomed.2023.107571. Online ahead of print.

Authors

Xinyi Qin¹, Guangzhong Liu², Min Liu³

Affiliations

¹ College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China. Electronic address: qinxinyi@stu.shmtu.edu.cn.
² College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China. Electronic address: gzhliu@shmtu.edu.cn.
³ College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China. Electronic address: liumin@shmtu.edu.cn.

PMID: 37864911
DOI: 10.1016/j.compbiomed.2023.107571

Abstract

A comprehensive understanding of protein functions holds significant promise for disease research and drug development, and proteins with analogous tertiary structures tend to exhibit similar functions. Protein fold recognition stands as a classical approach in the realm of protein structure investigation. Despite significant advancements made by researchers in this field, the continuous updating of protein databases presents an ongoing challenge in accurately identifying protein fold types. In this study, we introduce a predictor, ResCNNT-fold, for protein fold recognition and employ the LE dataset for testing purpose. ResCNNT-fold leverages a pre-trained language model to obtain embedding representations for protein sequences, which are then processed by the ResCNNT feature extractor, a combination of residual convolutional neural network and Transformer, to derive fold-specific features. Subsequently, the query protein is paired with each protein whose structure is known in the template dataset. For each pair, the similarity score of their fold-specific features is calculated. Ultimately, the query protein is identified as the fold type of the template protein in the pair with the highest similarity score. To further validate the utility and efficacy of the proposed ResCNNT-fold predictor, we conduct a 2-fold cross-validation experiment on the fold level of the LE dataset. Remarkably, this rigorous evaluation yields an exceptional accuracy of 91.57%, which surpasses the best result among other state-of-the-art protein fold recognition methods by an approximate margin of 10%. The excellent performance unequivocally underscores the compelling advantages inherent to our proposed ResCNNT-fold predictor in the realm of protein fold recognition. The source code and data of ResCNNT-fold can be downloaded from https://github.com/Bioinformatics-Laboratory/ResCNNT-fold.

Keywords: Fold-specific features; Language model; Protein fold recognition; Residual convolutional neural network; Transformer.