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Related Concept Videos

Antigens Involved in Adaptive Immunity01:26

Antigens Involved in Adaptive Immunity

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An antigen is any substance the immune system identifies as foreign and potentially harmful to the body, prompting an immune response. Antigens have two functional properties: immunogenicity and reactivity. Immunogenicity is the ability of an antigen to stimulate a specific immune response. At the same time, reactivity describes the antigen's ability to react with the cells and antibodies produced in response to it.
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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
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Related Experiment Video

Updated: Sep 16, 2025

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis
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pMHChat, characterizing the interactions between major histocompatibility complex class II molecules and peptides

Jiani Ma1, Zhikang Wang2, Cen Tong3

  • 1School of Information and Control Engineering, China University of Mining and Technology, No. 1 Daxue Road, Tongshan District, Xuzhou, Jiangsu 221116, China.

Briefings in Bioinformatics
|July 7, 2025
PubMed
Summary

We developed pMHChat, a novel model using large language models (LLMs) and deep hypergraph learning to predict major histocompatibility complex (MHC) class II-peptide binding. This tool enhances understanding of immune responses for applications in vaccine development and immunotherapy.

Keywords:
MHC class II-peptide bindinghypergraph convolutional networklarge language modelresidue contact profiling

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Area of Science:

  • Immunology
  • Computational Biology
  • Bioinformatics

Background:

  • Understanding major histocompatibility complex (MHC) class II-peptide interactions is vital for immune system research.
  • Applications include neoantigen design, vaccine development, and personalized immunotherapy.

Purpose of the Study:

  • To develop a predictive model for MHC class II-peptide binding reactivity, affinity, and residue contact profiling.
  • To enhance the accuracy and provide detailed insights into peptide-MHC (pMHC) complex interactions.

Main Methods:

  • Integration of large language models (LLMs) and deep hypergraph learning.
  • A four-stage process: LLM fine-tuning, feature encoding/map fusion, task-specific prediction, and downstream analysis.
  • Utilized pMHChat model with MHC pseudo-sequences and peptide sequences as input.

Main Results:

  • Achieved superior performance in binding reactivity prediction (AUC: 0.8744, AUPRC: 0.8390) via five-fold cross-validation.
  • Demonstrated strong binding affinity prediction (Pearson correlation: 0.7311).
  • Exhibited top performance in leave-one-molecule-out and independent evaluations, with residue contact profiling capabilities.

Conclusions:

  • pMHChat significantly advances predictive accuracy for MHC-peptide binding.
  • The model offers valuable residue contact profiling for understanding critical binding patterns.
  • pMHChat is a powerful tool for immunological research and therapeutic development.