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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.

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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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Prediction of human major histocompatibility complex class II binding peptides by continuous kernel discrimination

Ju He1, Guobing Yang, Hanbing Rao

  • 1College of Chemistry, Sichuan University, Chengdu 610064, People's Republic of China.

Artificial Intelligence in Medicine
|December 3, 2011
PubMed
Summary

This study developed a machine learning model using continuous kernel discrimination (CKD) for predicting major histocompatibility complex (MHC) class II binding peptides. The CKD model significantly improved prediction accuracy and reduced feature complexity, outperforming previous methods.

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Area of Science:

  • Immunoinformatics
  • Computational Biology
  • Machine Learning in Immunology

Background:

  • Accurate prediction of major histocompatibility complex (MHC) class II binding peptides is crucial for identifying helper T cell epitopes.
  • The variable length of binding peptides presents a significant challenge in developing accurate predictive models.
  • Reducing experimental costs in epitope identification necessitates improved computational prediction methods.

Purpose of the Study:

  • To develop an accurate machine learning model for predicting MHC class II binding peptides.
  • To address the challenge of variable peptide lengths in MHC binding prediction.
  • To enhance the efficiency of identifying helper T cell epitopes.

Main Methods:

  • Employed the continuous kernel discrimination (CKD) machine learning method for predicting MHC class II binders.
  • Utilized composition transition and distribution features for peptide sequence encoding.
  • Applied Metropolis Monte Carlo simulated annealing for effective feature selection.

Main Results:

  • Feature selection significantly improved model performance, reducing features from 147 to 24 (Dataset-1) and 44 (Dataset-2).
  • Area under the receiver operating characteristic curve (AUC) improved from 0.8088 to 0.9034 (Dataset-1) and 0.7349 to 0.8499 (Dataset-2) post-feature selection.
  • The optimized CKD model demonstrated superior performance compared to earlier models, with AUC values ranging from 0.831 to 0.980 on benchmark datasets.

Conclusions:

  • The CKD method significantly outperforms previously proposed machine learning methods for MHC class II binding peptide prediction.
  • Feature selection is a critical step for enhancing the accuracy and efficiency of MHC binding prediction models.
  • The selection of an appropriate cut-off value for the CKD classifier is essential for optimal performance.