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Related Experiment Video

Updated: Sep 23, 2025

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Published on: October 15, 2021

HLAncPred: a method for predicting promiscuous non-classical HLA binding sites.

Anjali Dhall¹, Sumeet Patiyal¹, Gajendra P S Raghava¹

¹Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi-110020, India.

Briefings in Bioinformatics

|May 17, 2022

Summary

Researchers developed an AI tool, HLAncPred, to predict non-classical Human Leukocyte Antigen (HLA) binders. This advances immunotherapy development for diseases like cancer and COVID-19 by identifying potential peptide targets.

Keywords:

COVID-19 binders non-classical HLA prediction standalone web server

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Last Updated: Sep 23, 2025

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Published on: October 15, 2021

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

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Published on: March 25, 2014

Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

Published on: September 6, 2017

Area of Science:

Immunology
Computational Biology
Bioinformatics

Background:

Human Leukocyte Antigens (HLA) are critical for immune responses.
Non-classical HLA-E and HLA-G offer advantages in immunotherapy for cancer and COVID-19.
Predicting non-classical HLA binders is challenging due to limited data.

Purpose of the Study:

To develop an AI-based method for predicting non-classical HLA binding peptides.
To create a user-friendly tool for the scientific community.
To identify potential non-classical HLA binders in SARS-CoV-2 spike proteins.

Main Methods:

Trained and tested machine learning models on experimentally validated data from IEDB.
Developed an artificial intelligence-based prediction method for class-Ib HLA alleles.
Created a web server (HLAncPred) and standalone package for binder prediction.

Main Results:

Achieved high AUC values: >0.98 for HLA-G, 0.96 for HLA-E*01:01, and 0.94 for HLA-E*01:03.
Validated performance against existing non-classical HLA prediction models.
Identified potential non-classical HLA binding peptides in COVID-19 variants, including Omicron.

Conclusions:

The developed AI models accurately predict non-classical HLA binders.
HLAncPred facilitates immunotherapy research by identifying promiscuous binders.
This tool aids in understanding immune responses and developing targeted therapies.