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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...
Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
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: Jun 12, 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

EpiTOP--a proteochemometric tool for MHC class II binding prediction.

Ivan Dimitrov1, Panayot Garnev, Darren R Flower

  • 1Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria.

Bioinformatics (Oxford, England)
|June 26, 2010
PubMed
Summary

EpiTOP is a new tool that predicts T-cell epitopes by identifying peptide binding to MHC class II proteins. This immunoinformatic approach significantly reduces the time and resources needed for vaccine design.

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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

Related Experiment Videos

Last Updated: Jun 12, 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

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:

  • Immunoinformatics
  • Vaccine Design
  • Computational Biology

Background:

  • T-cell epitope identification is crucial for effective vaccine development.
  • Peptide binding to Major Histocompatibility Complex (MHC) proteins is a prerequisite for T-cell epitope recognition.
  • Accurate prediction of MHC-peptide interactions is a significant challenge in immunoinformatics.

Purpose of the Study:

  • To introduce EpiTOP, a novel computational server for predicting T-cell epitopes.
  • To leverage proteochemometrics, a quantitative structure-activity relationship (QSAR) approach, for MHC class II binding prediction.
  • To provide a user-friendly tool for accelerating epitope identification in vaccine design.

Main Methods:

  • EpiTOP employs proteochemometrics, a QSAR method, to model ligand binding across related proteins.
  • The server utilizes a quantitative matrix for predicting binding affinity to 12 distinct HLA-DRB1 alleles.
  • The prediction model is trained and validated using known epitope data.

Main Results:

  • EpiTOP successfully identifies 89% of known epitopes within the top 20% of predicted binders.
  • The tool demonstrates an 80% reduction in laboratory labor, materials, and time compared to traditional methods.
  • EpiTOP provides comprehensive and quantitative binding predictions.

Conclusions:

  • EpiTOP represents a significant advancement in T-cell epitope prediction for vaccine design.
  • The server offers a highly efficient and accurate method for identifying potential epitopes.
  • EpiTOP is freely accessible and will be continuously updated with new predictive models.