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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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Related Experiment Video

Updated: May 2, 2026

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

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

Published on: October 15, 2021

MHC binding prediction with KernelRLSpan and its variations.

Wen-Jun Shen¹, Yu Ting Wei², Xin Guo³

¹Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.

Journal of Immunological Methods

|March 8, 2014

Summary

This summary is machine-generated.

This study enhances Major Histocompatibility Complex (MHC) binding prediction for T cell activation. A modified KernelRLSpanI algorithm and a novel consensus method show improved prediction of naturally processed peptides for vaccine development.

Keywords:

Diffusion map Eluted peptide prediction MHC Major histocompatibility complex class I Major histocompatibility complex class II Peptide binding prediction String kernel

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

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Published on: October 15, 2021

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Published on: February 28, 2019

Area of Science:

Immunology
Bioinformatics
Computational Biology

Background:

Antigenic peptides presented by MHC molecules are crucial for T and B cell proliferation and differentiation.
Accurate MHC binding prediction is vital for identifying potential vaccine candidates.
The KernelRLSpan algorithm has shown promise for MHC II binding prediction.

Purpose of the Study:

To modify and apply KernelRLSpan for MHC I binding prediction (KernelRLSpanI).
To develop a novel consensus method integrating KernelRLSpanI with NetMHCpan and NetMHC for predicting naturally processed peptides.
To improve MHC II binding prediction using diffusion maps.

Main Methods:

Modification of the KernelRLSpan algorithm to create KernelRLSpanI for MHC I binding prediction.
Development of a consensus method by combining KernelRLSpanI, NetMHCpan, and NetMHC.
Application of diffusion maps to enhance MHC II binding prediction.

Main Results:

KernelRLSpanI demonstrated effectiveness in MHC I binding prediction.
The consensus method achieved top performance in the MLI 2012 Competition (group 2) for predicting naturally processed peptides.
Progress was made in improving MHC II binding prediction using diffusion maps.

Conclusions:

The developed KernelRLSpanI and consensus method represent advancements in MHC binding prediction.
These improved predictors can aid in the identification of effective vaccine candidates.
Further enhancements to MHC II prediction methods are ongoing.