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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Protein-protein interaction and site prediction using transfer learning.

Tuoyu Liu1, Han Gao2, Xiaopu Ren1

  • 1Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.

Briefings in Bioinformatics
|October 23, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces MP-BERT, a new model for identifying protein-protein interactions (PPIs) and interaction sites. The model shows superior performance and generalization across organisms, aiding in protein research.

Keywords:
BERTPPI siteprotein–protein interactiontransfer learningtransformer

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Machine learning in proteomics

Background:

  • Advanced language models are increasingly used for biological sequence analysis.
  • Protein-protein interactions (PPIs) are crucial for cellular functions.
  • Identifying PPIs and their interaction sites is essential for understanding biological processes.

Purpose of the Study:

  • To develop and evaluate a novel deep learning model for predicting protein-protein interactions (PPIs) and interaction sites.
  • To assess the model's performance and generalization capabilities across different organisms.
  • To demonstrate the effectiveness of transfer learning for protein pair tasks.

Main Methods:

  • Training a Bidirectional Encoder Representation from Transformers (BERT) model, named MindSpore ProteinBERT (MP-BERT), using protein pairs.
  • Fine-tuning MP-BERT for PPI prediction (MPB-PPI) and interaction site prediction (MPB-PPISP).
  • Evaluating model performance on diverse benchmark datasets and multiple organisms.

Main Results:

  • The fine-tuned MPB-PPI model outperformed state-of-the-art methods in PPI prediction.
  • An amalgamated organism model achieved 92.65% accuracy, showing high generalization.
  • The MPB-PPISP model demonstrated capability in predicting interaction site propensity.

Conclusions:

  • The MP-BERT framework effectively predicts both PPIs and their interaction sites.
  • Transfer learning significantly enhances performance on protein pair tasks.
  • The developed models offer a powerful tool for advancing protein interaction research.