DCSE:Double-Channel-Siamese-Ensemble model for protein protein interaction prediction

Abstract

Background Protein-protein interaction (PPI) is very important for many biochemical processes. Therefore, accurate prediction of PPI can help us better understand the role of proteins in biochemical processes. Although there are many methods to predict PPI in biology, they are time-consuming and lack accuracy, so it is necessary to build an efficiently and accurately computational model in the field of PPI prediction. Results We present a novel sequence-based computational approach called DCSE (Double-Channel-Siamese-Ensemble) to predict potential PPI. In the encoding layer, we treat each amino acid as a word, and map it into an N-dimensional vector. In the feature extraction layer, we extract features from local and global perspectives by Multilayer Convolutional Neural Network (MCN) and Multilayer Bidirectional Gated Recurrent Unit with Convolutional Neural Networks (MBC). Finally, the output of the feature extraction layer is then fed into the prediction layer to output whether the input protein pair will interact each other. The MCN and MBC are siamese and ensemble based network, which can effectively improve the performance of the model. In order to demonstrate our model’s performance, we compare it with four machine learning based and three deep learning based models. The results show that our method outperforms other models in all evaluation criteria. The Accuracy, Precision, $$F_{1}$$ F 1 , Recall and MCC of our model are 0.9303, 0.9091, 0.9268, 0.9452, 0.8609. For the other seven models, the highest Accuracy, Precision, $$F_{1}$$ F 1 , Recall and MCC are 0.9288, 0.9243, 0.9246, 0.9250, 0.8572. We also test our model in the imbalanced dataset and transfer our model to another species. The results show our model is excellent. Conclusion Our model achieves the best performance by comparing it with seven other models. NLP-based coding method has a good effect on PPI prediction task. MCN and MBC extract protein sequence features from local and global perspectives and these two feature extraction layers are based on siamese and ensemble network structures. Siamese-based network structure can keep the features consistent and ensemble based network structure can effectively improve the accuracy of the model.

Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review

“…Chen et al [ 53 ] designed the Double-Channel-Siamese-Ensemble (DCSE) model, a sequence-based computational approach, for pairwise PPI prediction, with superior performance. Additionally, Gao et al [ 54 ] developed EResCNN, a predictor for PPIs based on an ensemble residual convolutional neural network, outperforming existing models in PPI prediction on various datasets.…”

“…The trade-off between model complexity and interpretability is another substantial challenge. As seen in studies like Soleymani et al [ 77 ] and Chen et al [ 53 ], deep learning models can be highly complex with numerous layers and nodes, leading to improved predictive performance. However, this complexity can often compromise interpretability, making it challenging to extract biological insights from the models.…”

GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs

“…For instance, LSTM-PHV method [ 8 ] and other related approaches [ 25 ] leveraged the LSTM (Long Short-Term Memory) model to capture long-range dependency features in protein sequences. Methods like ADH-PPI [ 26 ] and DCSE [ 27 ] integrated CNN and RNN to extract both local and long-range features from protein sequences, which were then combined to predict PPI. Attention mechanisms have also been widely utilized to identify key sequence features in protein sequences [ 23 , 26 , 28 ].…”

Prediction of Protein–Protein Interactions Based on Integrating Deep Learning and Feature Fusion

“…In this study, we focused our attention on sequence-based approaches because of the advantages of rich and easy-to-search protein sequence data sources compared to protein structure data sources [ 2 ]. In addition, numerous tests have shown that amino acid sequence information alone is capable of identifying new protein–protein interactions [ 3 , 4 , 5 ].…”