RBM-MHC: A Semi-Supervised Machine-Learning Method for Sample-Specific Prediction of Antigen Presentation by HLA-I Alleles

The recent increase of immunopeptidomics data, obtained by mass spectrometry or binding assays, opens up possibilities for investigating endogenous antigen presentation by the highly polymorphic human leukocyte antigen class I (HLA-I) protein. State-of-the-art methods predict with high accuracy pres...

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Bibliographic Details
Published in:Cell systems Vol. 12; no. 2; p. 195
Main Authors: Bravi, Barbara, Tubiana, Jérôme, Cocco, Simona, Monasson, Rémi, Mora, Thierry, Walczak, Aleksandra M
Format: Journal Article
Language:English
Published: United States 17.02.2021
Subjects:
Algorithms
Alleles
Antigen Presentation - genetics
Antigen Presentation - immunology
Computational Biology - methods
Databases, Protein
Epitopes
Histocompatibility Antigens Class I - genetics
Histocompatibility Antigens Class I - immunology
HLA Antigens - genetics
HLA Antigens - immunology
Humans
Machine Learning
Major Histocompatibility Complex - immunology
Mass Spectrometry - methods
Models, Theoretical
Peptides - chemistry
Protein Binding
SARS-CoV-2 epitopes
antigen presentation
Restricted Boltzmann Machines
machine learning
HLA prediction
immunopeptidomics
neoantigen discovery
ISSN:2405-4720, 2405-4720
Online Access:Get more information
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Summary:The recent increase of immunopeptidomics data, obtained by mass spectrometry or binding assays, opens up possibilities for investigating endogenous antigen presentation by the highly polymorphic human leukocyte antigen class I (HLA-I) protein. State-of-the-art methods predict with high accuracy presentation by HLA alleles that are well represented in databases at the time of release but have a poorer performance for rarer and less characterized alleles. Here, we introduce a method based on Restricted Boltzmann Machines (RBMs) for prediction of antigens presented on the Major Histocompatibility Complex (MHC) encoded by HLA genes-RBM-MHC. RBM-MHC can be trained on custom and newly available samples with no or a small amount of HLA annotations. RBM-MHC ensures improved predictions for rare alleles and matches state-of-the-art performance for well-characterized alleles while being less data demanding. RBM-MHC is shown to be a flexible and easily interpretable method that can be used as a predictor of cancer neoantigens and viral epitopes, as a tool for feature discovery, and to reconstruct peptide motifs presented on specific HLA molecules.
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ISSN:2405-4720
2405-4720
DOI:10.1016/j.cels.2020.11.005