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- Title
SeBPPI: A Sequence-Based Protein–Protein Binding Predictor.
- Authors
Wang, Bo; Mao, Jun; Wei, Min; Qi, Yifei; Zhang, John Z. H.
- Abstract
Protein–protein interaction (PPI) plays an important role in biological processes such as signal transduction, immune response and membrane transport, etc. In this work, a protein sequence-based machine learning model, SeBPPI, to predict protein–protein binding was proposed. In this model, the descriptors were generated from three pre-trained models: Unirep, ESM and TAPE. The performance of SeBPPI with these pre-trained models was evaluated on several different test datasets. The accuracy of our binary prediction model shows improvement over the existing methods. We also compared the performance of two classification heads: The Recurrent convolution neural network (RCNN) and the fully connected neural network (FNN) and found that the use of RCNN is beneficial for the overall improvement in the accuracy of the model. This study helps to improve the accuracy in sequence-based protein–protein binding predictions. The model used in this work is integrated in the web server http://www.icdrug.com/ICDrug/SeBPPI. In this paper, a sequence-based protein-protein binding predictor was proposed. This machine learning model combined the RCNN model and the pre-trained models. Comparison between other machine learning models illustrated the SeBPPI model was a powerful tool.
- Subjects
CONVOLUTIONAL neural networks; RECURRENT neural networks; INTERNET servers; SIGNAL processing; MACHINE learning; PROTEIN-protein interactions
- Publication
Journal of Computational Biophysics & Chemistry, 2022, Vol 21, Issue 6, p729
- ISSN
2737-4165
- Publication type
Article
- DOI
10.1142/S2737416522500314