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Methods Mol Biol. 2019;2024:301-307. doi: 10.1007/978-1-4939-9597-4_19.

Enhancing Mass Spectrometry-Based MHC-I Peptide Identification Through a Targeted Database Search Approach.

Author information

1
Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada.
2
Department of Pathology, Dalhousie University, Halifax, NS, Canada.
3
Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark.
4
Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina.
5
Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada. shashi.gujar@dal.ca.
6
Department of Pathology, Dalhousie University, Halifax, NS, Canada. shashi.gujar@dal.ca.
7
Department of Biology, Dalhousie University, Halifax, NS, Canada. shashi.gujar@dal.ca.
8
Centre for Innovative and Collaborative Health Services Research, IWK Health Centre, Halifax, NS, Canada. shashi.gujar@dal.ca.

Abstract

MHC-bound peptide ligands dictate the activation and specificity of CD8+ T- cells-based and thus are important for devising T-cell immunotherapies. In recent times, advances in mass spectrometry (MS) have enabled the precise identification of these peptides, wherein MS/MS spectra are compared against a reference proteome. Unfortunately, matching immunopeptide MS/MS to reference proteome databases is hindered by inflated search spaces attributed to the number of matches that need to be considered due to a lack of enzyme restriction. These large search spaces limit the efficiency with which MHC-I peptides are identified. Here we offer a solution to this problem whereby we describe a targeted database search approach and accompanying tool SpectMHC that is based on a priori predicted MHC-I peptides (Murphy et al., J Proteome Res 16:1806-1816, 2017).

KEYWORDS:

Bioinformatics; Immuno-informatics; MHC ligandome; MHC peptides; Mass spectrometry; NetMHC; SpectMHC

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