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Related Concept Videos

Antigens Involved in Adaptive Immunity01:26

Antigens Involved in Adaptive Immunity

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.
Complete Antigens
Complete antigens possess both immunogenicity and reactivity.
Antigen Processing Pathways01:31

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MHC molecules are key players in the immune response, enabling T cells to recognize and respond to specific antigens. They are present on the surface of all nucleated cells in the body and are instrumental in presenting antigens to T cells and activating them. T cells recognize the MHC-antigen complex and initiate an immune response. MHC class I and MHC class II are two main types of MHC molecules, each associated with a distinct antigen processing pathway.
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  3. Epimii: Structure-aware Graph Neural Networks For Mhc-ii Epitope Generation.
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  3. Epimii: Structure-aware Graph Neural Networks For Mhc-ii Epitope Generation.

Related Experiment Video

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
07:59

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

EpiMII: Structure-Aware Graph Neural Networks for MHC-II Epitope Generation.

Jiayi Yuan1, Xiaowei Xu2, Ze-Yu Sun1

  • 1Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy; National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA.

Research (Washington, D.C.)
|June 17, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

EpiMII, a new AI model, designs effective Major histocompatibility complex class II (MHC-II) neoantigens for cancer immunotherapy. It enhances T cell activation and reduces tumor growth, offering a promising approach for personalized cancer vaccines.

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Published on: October 15, 2021

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07:59

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Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis
09:32

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Published on: October 15, 2021

Area of Science:

  • Immunology
  • Computational Biology
  • Bioinformatics

Background:

  • Major histocompatibility complex class II (MHC-II) neoantigens are crucial for cancer immunotherapy, influencing T cell responses.
  • Current methods for predicting and designing functional neoantigens are limited by accuracy and the scarcity of experimental data.
  • Developing novel, highly immunogenic neoantigens is essential for advancing cancer vaccines and personalized treatments.

Purpose of the Study:

  • To develop a structure-aware graph neural network model, EpiMII, for the de novo design of MHC-II epitopes.
  • To generate mimotopes that maintain T cell specificity while improving MHC-II binding affinity and immunogenicity.
  • To overcome limitations in existing neoantigen prediction tools through a structure-guided approach.

Main Methods:

  • EpiMII utilizes an inverse folding strategy integrating 3D structural information to design MHC-II epitopes.
  • The model was trained on a large dataset of 142,934 homology-modeled MHC-II epitope structures.
  • Performance was benchmarked against existing tools using sequence recovery rates on held-out and crystallized epitope datasets.

Main Results:

  • EpiMII achieved a 66.7% sequence recovery rate on a held-out test set and 79.0% on crystallized epitopes, outperforming ProteinMPNN.
  • In a hepatocellular carcinoma model, EpiMII-designed epitopes activated CD4+ T cells and induced cytokine secretion (IFN-γ, TNF-α) in vitro.
  • One designed epitope (P4) significantly reduced tumor volume in mice, demonstrating in vivo efficacy.

Conclusions:

  • EpiMII is a powerful tool for structure-guided neoantigen discovery, enabling the de novo design of immunogenic MHC-II epitopes.
  • The model's ability to enhance T cell activation and reduce tumor growth has significant implications for cancer vaccine development.
  • EpiMII advances personalized immunotherapy by facilitating the design of targeted and effective neoantigen-based treatments.