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Antigens Involved in Adaptive Immunity

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 Pathways

Antigen Processing Pathways

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.
MHC Class I: Presenting Endogenous...

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Updated: Jul 5, 2026

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

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

Published on: March 25, 2014

Building a meta-predictor for MHC class II-binding peptides.

Lei Huang¹, Oleksiy Karpenko, Naveen Murugan

¹Bioengineering Bioinformatics, University of Illinois at Chicago, USA.

Methods in Molecular Biology (Clifton, N.J.)

|May 3, 2008

Summary

Predicting major histocompatibility complex (MHC)-peptide binding is complex. A new meta-predictor integrates multiple computational methods for more reliable MHC-peptide binding predictions.

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Area of Science:

Immunoinformatics
Computational Biology
Bioinformatics

Background:

Predicting major histocompatibility complex (MHC) class II-peptide binding is crucial for understanding immune responses.
Existing computational methods for MHC-peptide binding prediction have limitations due to variable peptide lengths.

Purpose of the Study:

To develop a robust meta-predictor for MHC class II-peptide binding.
To integrate predictions from diverse computational tools for enhanced accuracy.

Main Methods:

A Naïve Bayesian approach was employed to build the meta-predictor.
The system architecture allows seamless incorporation of results from any number of individual prediction tools.

Main Results:

The developed meta-predictor is designed for flexibility, accommodating various prediction algorithms.
Integration of multiple predictors is expected to improve the reliability of MHC-peptide binding predictions.

Conclusions:

The meta-predictor offers a promising solution for more confident MHC class II-peptide binding predictions.
This approach addresses the challenge of variable peptide lengths in MHC binding prediction.