LPBERT: A Protein–Protein Interaction Prediction Method Based on a Pre-Trained Language Model

The prediction of protein–protein interactions is a key task in proteomics. Since protein sequences are easily available and understandable, they have become the primary data source for predicting protein–protein interactions. With the development of natural language processing technology, language models have become a research hotspot in recent years, and protein language models have also been developed accordingly. Compared with single-encoding methods, such as Word2Vec and one-hot, language models specifically designed for proteins are expected to extract more comprehensive information from sequences, thereby enhancing the performance of protein–protein interaction prediction methods. Inspired by the protein language model ProteinBERT, this study designed the LPBERT deep learning framework, which is a novel end-to-end deep learning architecture. LPBERT, which is based on ProteinBERT, combines Convolutional Neural Networks, Transformer encoders, and Bidirectional Long Short-Term Memory networks to achieve efficient prediction. Upon evaluation using the BioGRID H. sapiens and S. cerevisiae datasets, LPBERT outperformed other comparison methods, where it achieved accuracies of 98.93% and 97.94%, respectively. Moreover, it also demonstrated good performances on multiple other datasets. These experimental results indicate that LPBERT performed excellently in protein–protein interaction prediction tasks, thereby substantiating the effectiveness of introducing protein language models in this field.