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Multimodal learning on heterogeneous subgraphs and LLMs representation for MHC-peptide binding affinity prediction.

Ruimeng Li1, Ying Wang1, Haozhou Li1

  • 1Faculty of Electronic and Information Engineering, The Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, 710049, China.

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|March 1, 2026
PubMed
Summary

Predicting MHC-peptide binding affinity is crucial for immunotherapy. Our novel Contrast learning-based Multi-feature Heterogeneous Subgraph model (CMHS) improves prediction accuracy by integrating sequence and structural data.

Keywords:
Contrastive learningCross-attentionEdge-induced subgraph extractGCNHeterogeneous graph

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Area of Science:

  • Computational biology
  • Immunoinformatics
  • Machine learning

Background:

  • Accurate prediction of Major Histocompatibility Complex (MHC)-peptide binding affinity is critical for developing effective immunotherapies.
  • Current computational methods face challenges in simultaneously modeling functional semantics, evolutionary constraints, and structural dynamics of polymorphic residues.
  • There is a need for advanced models that can capture complex interactions between MHC molecules and peptides.

Purpose of the Study:

  • To develop a novel computational model, the Contrast learning-based Multi-feature Heterogeneous Subgraph model (CMHS), for enhanced MHC-peptide binding affinity prediction.
  • To integrate diverse data representations, including sequence and structural information, for a more comprehensive understanding of MHC-peptide interactions.
  • To establish a new benchmark in predicting hypervariable immune interactions.

Main Methods:

  • Utilized LoRA fine-tuning with ESM2 and BLOSUM50 for MHC-exclusive sequence representation, capturing functional dependencies and conserved residues.
  • Employed a biophysics-guided heterogeneous graph network for structural representation, incorporating a novel trainable Gaussian noise layer guided by crystallographic B-factors.
  • Implemented a three-stage message-passing framework with subgraph aggregation and extraction, followed by contrastive learning to align sequence and graph representation spaces.

Main Results:

  • The CMHS model demonstrated significant improvements in prediction accuracy across 16 HLA allele benchmarks.
  • Achieved an average Spearman rank correlation coefficient (SRCC) improvement of 8.7% compared to existing methods.
  • Showcased an average Area Under the Curve (AUC) improvement of 7.6%, indicating enhanced predictive performance.

Conclusions:

  • The CMHS model represents a new paradigm for predicting hypervariable immune interactions, particularly MHC-peptide binding affinity.
  • The integration of sequence and structural data through contrastive learning significantly enhances prediction capabilities.
  • This approach offers a promising direction for advancing immunotherapeutic development through improved computational modeling.