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Related Concept Videos

Antigens Involved in Adaptive Immunity01:26

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 Pathways01:31

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.
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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.
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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.
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Related Experiment Video

Updated: May 14, 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

Prediction of MHC class I binding peptides by a query learning algorithm based on hidden markov models.

Keiko Udaka1, Hiroshi Mamitsuka, Yukinobu Nakaseko

  • 1Department of Biophysics, Kyoto University, Japan.

Journal of Biological Physics
|January 25, 2013
PubMed
Summary

This study introduces a query learning algorithm using hidden Markov models (HMMs) to improve Major Histocompatibility Complex (MHC) class I peptide binding prediction. The method effectively reduces data needs and enhances prediction accuracy for identifying potential drug targets.

Keywords:
MHC class I moleculesalgorithmbindingexperimental designpeptidespredictionquery learningspecificitystring analysis

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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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06:50

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:

  • Computational biology
  • Immunoinformatics
  • Machine learning

Background:

  • Predicting peptide binding to Major Histocompatibility Complex (MHC) class I molecules is crucial for understanding immune responses and developing vaccines or immunotherapies.
  • Traditional methods often require extensive experimental peptide binding data for training predictive models, which can be costly and time-consuming.

Purpose of the Study:

  • To develop a novel query learning algorithm to enhance the efficiency of predicting MHC class I binding peptides.
  • To reduce the amount of experimental peptide binding data required for training predictive models, specifically Hidden Markov Models (HMMs).

Main Methods:

  • A query learning algorithm was developed, utilizing a committee of multiple Hidden Markov Models (HMMs) trained on existing peptide binding data.
  • The algorithm iteratively samples peptides, predicts their binding affinity using the HMM committee, and prioritizes experimental testing for peptides with the least consistent predictions.
  • This active learning cycle integrates computational analysis with experimental feedback to refine the predictive model.

Main Results:

  • After seven rounds of active learning and testing 181 peptides, the algorithm's predictive performance surpassed existing matrix-based prediction methods.
  • The combined approach, integrating HMMs and matrix-based methods, achieved 84% accuracy in predicting binder peptides (log Kd < -6).
  • Visual inspection of trained HMM parameter distributions provided insights into the dynamic specificity of MHC molecules.

Conclusions:

  • The developed query learning algorithm significantly improves the efficiency and accuracy of MHC class I peptide binding prediction.
  • This approach offers a powerful tool for accelerating the identification of relevant peptides for immunological applications.
  • The method not only enhances predictive performance but also provides a deeper understanding of MHC-peptide interactions.