SeBPPI: A Sequence-Based Protein-Protein Binding Predictor

Bo Wang, Jun Mao, Min Wei, Yifei Qi, John Z.H. Zhang

Research output: Contribution to journalArticlepeer-review


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

Original languageEnglish (US)
Pages (from-to)729-737
Number of pages9
JournalJournal of Computational Biophysics and Chemistry
Issue number6
StatePublished - Sep 1 2022


  • Protein-protein interaction
  • neural network
  • pre-trained model

ASJC Scopus subject areas

  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Computational Theory and Mathematics


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