DNA binding protein identification method based on Transformer-BiLSTM feature fusion

Protein is closely related to life activities. As a special protein,DeoxyriboNucleic Acid(DNA) binding protein plays an irreplaceable role in life activities. Therefore, the study of DNA binding protein has very important practical significance, and the research prospect of this subject is very broad. Although the traditional biotechnology has high precision, its cost is very expensive, relatively low efficiency and high equipment requirements, so it is not suitable for the modern society to study a large number of proteins. To some extent, machine learning makes up for the shortcomings of biological experiment technology, but it is far less efficient and convenient than deep learning technology in data processing. In this study, a deep learning framework based on Bidirectional parallel Long Term and Short Term Memory neural network (BiLSTM) and Transformer is proposed to identify DNA binding proteins. The model can not only further extract the information and characteristics of protein sequences, but also further extract the characteristics of evolutionary information. Finally, the two features are integrated for training and testing. This model expands the research ideas of researchers in protein feature extraction, and provides a reference for extracting global protein features with Transformer encoder blocks. On the PDB2272 dataset, the accuracy(ACC) and Matthew Correlation Coefficient(MCC) improved by 2.64% and 5.51%, respectively, compared to the PDBP_ Fusion model. The experimental results of this model have certain advantages.