Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

T Cell Activation and Clonal Selection01:22

T Cell Activation and Clonal Selection

T cells are integral to our adaptive immune system, recognizing and effectively responding to foreign antigens. T cell activation and clonal selection are pivotal in orchestrating this immune response. This article elucidates these mechanisms, detailing the roles of cluster of differentiation (CD) markers, major histocompatibility complex (MHC) molecules, costimulatory signals, and the process of clonal selection.
Naive T cells that have not yet encountered an antigen express two primary CD...
Cross-reactivity00:42

Cross-reactivity

Overview

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Review: application and opportunities for machine learning and artificial intelligence in preclinical immunogenicity risk assessment.

Frontiers in immunology·2026
Same author

Cancer epitope prediction tools and analysis pipelines in CEDAR.

Nucleic acids research·2026
Same author

NetMHCIIphosPan: A Machine Learning Tool for Predicting HLA Class II Antigen Presentation of Phosphorylated Peptides.

Journal of proteome research·2026
Same author

Standardising image registration and dose mapping for thoracic reirradiation: A national multi-centre benchmarking study.

Physics and imaging in radiation oncology·2026
Same author

NetMHCIIphosPan: a machine learning tool for predicting HLA class II antigen presentation of phosphorylated peptides.

bioRxiv : the preprint server for biology·2026
Same author

Integrated computational and experimental workflow identifies a novel T-cell epitope from Babesia bovis RON5 recognized by infected cattle.

Vaccine·2025

Related Experiment Video

Updated: Jul 19, 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

The validity of predicted T-cell epitopes.

Claus Lundegaard1, Morten Nielsen, Ole Lund

  • 1Center for Biological Sequence Analysis, BioCentrum, Building 208, Technical University of Denmark, 2800 Lyngby, Denmark. lunde@cbs.dtu.dk

Trends in Biotechnology
|October 19, 2006
PubMed
Summary

Bioinformatics tools for predicting MHC class I binding are valuable for epitope discovery. A large-scale study estimated their real-world utility and provided data for benchmarking new prediction methods.

More Related Videos

Peptide:MHC Tetramer-based Enrichment of Epitope-specific T cells
13:58

Peptide:MHC Tetramer-based Enrichment of Epitope-specific T cells

Published on: October 22, 2012

Related Experiment Videos

Last Updated: Jul 19, 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

Peptide:MHC Tetramer-based Enrichment of Epitope-specific T cells
13:58

Peptide:MHC Tetramer-based Enrichment of Epitope-specific T cells

Published on: October 22, 2012

Area of Science:

  • Immunology
  • Bioinformatics
  • Computational Biology

Background:

  • Predictive models for Major Histocompatibility Complex (MHC) class I binding peptides have existed for over ten years.
  • The practical utility of these predictions in identifying actual T-cell epitopes has remained largely unquantified in large-scale studies.

Purpose of the Study:

  • To estimate the real-world value of high-performing MHC class I binding predictions in epitope discovery.
  • To highlight the role of bioinformatics as a cost- and time-saving approach in immunological research.
  • To provide a benchmark dataset for evaluating the performance of existing and novel CTL epitope prediction tools.

Main Methods:

  • Large-scale investigation of MHC class I binding prediction performance.
  • Analysis of epitope identification success rates using predictive algorithms.
  • Data compilation for benchmarking purposes.

Main Results:

  • The study provides the first large-scale estimation of the utility of MHC class I binding predictions for epitope discovery.
  • Demonstrates the significant resource-saving potential of bioinformatics in this field.
  • Offers a valuable dataset for comparative analysis of prediction tool performance.

Conclusions:

  • High-performing MHC class I binding prediction tools are essential for efficient epitope discovery.
  • Bioinformatics significantly reduces the resources required for identifying T-cell epitopes.
  • The generated data serves as a crucial benchmark for the validation of computational epitope prediction methods.