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Habermann, D.* ; Kharimzadeh, H.* ; Walker, A.* ; Li, Y.* ; Yang, R.* ; Kaiser, R.* ; Brumme, Z.L.* ; Timm, J.* ; Roggendorf, M. ; Hoffmann, D.*

HAMdetector: A Bayesian regression model that integrates information to detect HLA-associated mutations.

Bioinformatics 38, 2428-2436 (2022)
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MOTIVATION: A key process in anti-viral adaptive immunity is that the Human Leukocyte Antigen system (HLA) presents epitopes as Major Histocompatibility Complex I (MHC I) protein-peptide complexes on cell surfaces and in this way alerts CD8+ cytotoxic T-Lymphocytes (CTLs). This pathway exerts strong selection pressure on viruses, favoring viral mutants that escape recognition by the HLA/CTL system. Naturally, such immune escape mutations often emerge in highly variable viruses, e.g. HIV or HBV, as HLA-associated mutations (HAMs), specific to the hosts MHC I proteins. The reliable identification of HAMs is not only important for understanding viral genomes and their evolution, but it also impacts the development of broadly effective anti-viral treatments and vaccines against variable viruses. By their very nature, HAMs are amenable to detection by statistical methods in paired sequence/HLA data. However, HLA alleles are very polymorphic in the human host population which makes the available data relatively sparse and noisy. Under these circumstances, one way to optimize HAM detection is to integrate all relevant information in a coherent model. Bayesian inference offers a principled approach to achieve this. RESULTS: We present a new Bayesian regression model for the detection of HAMs that integrates a sparsity-inducing prior, epitope predictions, and phylogenetic bias assessment, and that yields easily interpretable quantitative information on HAM candidates. The model predicts experimentally confirmed HAMs as having high posterior probabilities, and it performs well in comparison to state-of-the-art models for several data sets from individuals infected with HBV, HDV, and HIV. AVAILABILITY: The source code of this software is available at https://github.com/HAMdetector/Escape.jl under a permissive MIT license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Hiv-1 Gag; Escape; Adaptation; Horseshoe; Evolution; Selection; Pressure; Epitopes; Polymorphisms; Mechanisms
ISSN (print) / ISBN 1367-4803
Journal Bioinformatics
Quellenangaben Volume: 38, Issue: 9, Pages: 2428-2436 Article Number: , Supplement: ,
Publisher Oxford University Press
Publishing Place Oxford
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Virology (VIRO)
Grants Michael Smith Foundation for Health Research
Canadian Institutes for Health Research
Deutsche Forschungsgemeinschaft