A hybrid deep learning model for classification of plant transcription factor proteins

Abstract

Studies on the amino acid sequences, protein structure, and the relationships of amino acids are still a large and challenging problem in biology. Although bioinformatics studies have progressed in solving these problems, the relationship between amino acids and determining the type of protein formed by amino acids are still a problem that has not been fully solved. This problem is why the use of some of the available protein sequences is also limited. This study proposes a hybrid deep learning model to classify amino acid sequences of unknown species using the amino acid sequences in the plant transcription factor database. The model achieved 98.23% success rate in the tests performed. With the hybrid model created, transcription factor proteins in the plant kingdom can be easily classified. The fact that the model is hybrid has made its layers lighter. The training period has decreased, and the success has increased. When tested with a bidirectional LSTM produced with a similar dataset to our dataset and a ResNet-based ProtCNN model, a CNN model, the proposed model was more successful. In addition, we found that the hybrid model we designed by creating vectors with Word2Vec is more successful than other LSTM or CNN-based models. With the model we have prepared, other proteins, especially transcription factor proteins, will be classified, thus enabling species identification to be carried out efficiently and successfully. The use of such a triplet hybrid structure in classifying plant transcription factors stands out as an innovation brought to the literature.