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

Autor:	Habermann D; Bioinformatics and Computational Biophysics, Faculty of Biology, University of Duisburg-Essen, Essen 45117, Germany., Kharimzadeh H; Division of Clinical Pharmacology, University Hospital, LMU Munich, Munich, Germany., Walker A; Institute of Virology, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine-Universität, Düsseldorf 40225, Germany., Li Y; AIDS and HIV Research Group, State Key Laboratory of Virology, Wuhan Institute of Virology, Chinese Academy of Science, Wuhan, China., Yang R; AIDS and HIV Research Group, State Key Laboratory of Virology, Wuhan Institute of Virology, Chinese Academy of Science, Wuhan, China., Kaiser R; Institute of Virology, University of Cologne, Faculty of Medicine and University Hospital of Cologne, Cologne 50935, Germany., Brumme ZL; Faculty of Health Sciences, Simon Fraser University, Burnaby, Canada.; British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada., Timm J; Institute of Virology, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine-Universität, Düsseldorf 40225, Germany., Roggendorf M; Institute of Virology, School of Medicine, Technical University of Munich/Helmholtz Zentrum München, Munich, Germany., Hoffmann D; Bioinformatics and Computational Biophysics, Faculty of Biology, University of Duisburg-Essen, Essen 45117, Germany.; Center of Medical Biotechnology, University of Duisburg-Essen, Essen, Germany.; Center for Computational Sciences and Simulation, University of Duisburg-Essen, Essen, Germany.
Jazyk:	angličtina
Zdroj:	Bioinformatics (Oxford, England) [Bioinformatics] 2022 Apr 28; Vol. 38 (9), pp. 2428-2436.
DOI:	10.1093/bioinformatics/btac134
Abstrakt:	Motivation: A key process in anti-viral adaptive immunity is that the human leukocyte antigen (HLA) system 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 datasets from individuals infected with HBV, HDV and HIV. Availability and Implementation: The source code of this software is available at https://github.com/HAMdetector/Escape.jl under a permissive MIT license. The data underlying this article were provided by permission. Data will be shared on request to the corresponding author with permission of the respective co-authors. Supplementary Information: Supplementary data are available at Bioinformatics online. (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
Databáze:	MEDLINE
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