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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.

Conserved Binding Sites

Conserved Binding Sites

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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...

Ligand Binding Sites

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...

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Updated: Jun 16, 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

SVR-PAIRWISE method to predict MHC-II binding peptides.

Juan Liu¹, Lian Wang, Shanfeng Zhu

¹School of Computer, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. wl791014@whu.edu.cn

International Journal of Bioinformatics Research and Applications

|January 30, 2010

Summary

This summary is machine-generated.

Predicting peptides that bind to MHC class II molecules is challenging. A new SVR-PAIRWISE method combining Support Vector Regression and pairwise alignment offers improved quantitative prediction of these crucial immune response peptides.

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

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

Immunoinformatics
Computational Biology
Peptide-MHC Binding Prediction

Background:

Peptide binding to Major Histocompatibility Complex (MHC) molecules is critical for initiating immune responses.
MHC molecules exist in class I and class II forms, with MHC-II binding prediction being notably difficult.
Variable peptide lengths associated with MHC-II pose challenges for existing prediction models.

Purpose of the Study:

To develop a novel method for quantitative prediction of MHC-II binding peptides.
To address the limitations of existing methods in predicting MHC-II binding peptides due to variable lengths.

Main Methods:

Introduction of the SVR-PAIRWISE method, integrating Support Vector Regression (SVR) with pairwise alignment.
Application of the SVR-PAIRWISE method for quantitative prediction of MHC-II binding peptides.

Main Results:

The SVR-PAIRWISE method demonstrates satisfying performance in predicting MHC-II binding peptides.
Comparative analysis shows competitive results against popular existing methods.

Conclusions:

The SVR-PAIRWISE method provides an effective approach for quantitative MHC-II binding peptide prediction.
This method overcomes some limitations of previous techniques, offering a valuable tool in immunoinformatics.