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CKG-TPI: integrating collaborative knowledge graph with sequence interactions for TCR-peptide binding specificity.

Yue Liu1, Haoyan Wang1, Guohua Wang1

  • 1Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.

Briefings in Bioinformatics
|September 22, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces CKG-TPI, a new computational model for predicting T-cell receptor (TCR) and peptide interactions. The novel framework significantly improves prediction accuracy, aiding vaccine design and immunotherapy research.

Keywords:
T-cell receptor (TCR)TCR–peptide bindingcollaborative knowledge graphgraph neural networkpeptide

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Area of Science:

  • Immunology
  • Computational Biology
  • Bioinformatics

Background:

  • Accurate prediction of T-cell receptor (TCR)-peptide interactions is crucial for immunology, impacting vaccine development and immunotherapy.
  • Current computational methods for TCR-peptide binding prediction face challenges in robustness and accuracy.
  • Existing methods often lack the integration of higher-order biological context.

Purpose of the Study:

  • To develop a novel computational framework for accurate TCR-peptide binding prediction.
  • To integrate interaction patterns and biological context using a collaborative knowledge graph.
  • To enhance the efficiency and reliability of identifying TCR-peptide interactions for immunological applications.

Main Methods:

  • Development of a collaborative knowledge graph (CKG-TPI) integrating TCR and peptide sequence interactions.
  • Application of graph neural networks (GNNs) within the CKG-TPI framework.
  • Validation using multiple independent, publicly available datasets.

Main Results:

  • CKG-TPI consistently outperformed state-of-the-art models on independent datasets.
  • Achieved a 9.89% improvement in Area Under the ROC Curve (AUC) compared to the UnifyImmun baseline.
  • Demonstrated a 23.93% increase in Area Under the Precision-Recall Curve (AUPRC) over leading baseline methods.
  • Attention weight visualization confirmed model effectiveness.

Conclusions:

  • CKG-TPI represents a significant advancement in computational TCR-peptide interaction prediction.
  • The model's ability to integrate biological context enhances prediction accuracy.
  • CKG-TPI shows potential as a powerful tool for immunological research and the discovery of novel therapeutics.