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Related Concept Videos

Conserved Binding Sites01:49

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...
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
Ligand Binding Sites02:40

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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Related Experiment Video

Updated: May 28, 2026

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

Predicting peptide binding affinities to MHC molecules using a modified semi-empirical scoring function.

Webber W P Liao1, Jonathan W Arthur

  • 1Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia.

Plos One
|October 4, 2011
PubMed
Summary
This summary is machine-generated.

Predicting peptide-Major Histocompatibility Complex (MHC) binding is crucial for understanding autoimmune diseases. This study enhances prediction accuracy by integrating large datasets and multiple crystal structures, improving upon existing methods.

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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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Last Updated: May 28, 2026

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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
  • Structural Biology
  • Computational Biology

Background:

  • The Major Histocompatibility Complex (MHC) is vital for immune response, presenting antigens to T cells.
  • MHC molecules binding self-peptides can trigger autoimmune diseases like multiple sclerosis.
  • Understanding peptide-MHC binding mechanisms is key for disease etiology and drug design.

Purpose of the Study:

  • To improve the prediction of peptide-MHC binding using a modified Fresno semi-empirical scoring function.
  • To assess the impact of data size and structural diversity on prediction accuracy.
  • To identify and propose solutions for obstacles in structure-based prediction methods.

Main Methods:

  • Utilized open-source software and public domain tools.
  • Applied the Fresno semi-empirical scoring function to predict binding for HLA class I and II alleles (HLA A2, DR15, DR1, DR4).
  • Analyzed the influence of binding data volume and crystal structure similarity on predictive performance.

Main Results:

  • Increased binding data and diverse crystal structures significantly enhanced prediction accuracy.
  • Method performance correlated positively with the structural similarity of employed crystal structures.
  • Identified key challenges hindering structure-based prediction methods.

Conclusions:

  • Structure-based peptide-MHC binding prediction can be improved with comprehensive data and structural information.
  • Addressing identified obstacles is necessary for structure-based methods to match sequence-based method performance.
  • This work provides a foundation for more accurate computational prediction in immunology and drug discovery.