UniBioPAN: A Novel Universal Classification Architecture for Bioactive Peptides Inspired by Video Action Recognition

The classification of bioactive peptides is of great importance in protein biology, but there is still a lack of a universal and effective classifier. Inspired by video action recognition, we developed the UniBioPAN architecture to create a universal peptide classifier to solve this problem. The architecture treats the peptide sequence as a video sequence and the molecular image of each amino acid in the peptide sequence as a video frame, enabling feature extraction and classification using convolutional neural networks, bidirectional long short-term memory networks, and fully connected networks. As a novel peptide classification architecture, UniBioPAN significantly outperforms other universal architecture in ACC, AUC and MCC across 11 data sets, and F1 score in 9 data sets. UniBioPAN is available in three ways: python script, jupyter notebook script and web server (https://gzliang.cqu.edu.cn/software/UniBioPAN.html). In summary, UniBioPAN is a universal, convenient, and high-performance peptide classification architecture. UniBioPAN holds significant importance in the discovery of bioactive peptides and the advancement of peptide classifiers. All the codes and data sets are publicly available at https://github.com/sanwrh/UniBioPAN.