Enhancing Mass Spectrometry-Based MHC-I Peptide Identification Through a Targeted Database Search Approach.
Konda, P., Murphy, J. P., Nielsen, M. and Gujar, S.
Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada.
Department of Pathology, Dalhousie University, Halifax, NS, Canada.
Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark.
Instituto de Investigaciones Biotecnologicas, Universidad Nacional de San Martin, San Martin, Buenos Aires, Argentina.
Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada. shashi.gujar@dal.ca.
Department of Pathology, Dalhousie University, Halifax, NS, Canada. shashi.gujar@dal.ca.
Department of Biology, Dalhousie University, Halifax, NS, Canada. shashi.gujar@dal.ca.
Centre for Innovative and Collaborative Health Services Research, IWK Health Centre, Halifax, NS, Canada. shashi.gujar@dal.ca.
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).
Methods in Molecular Biology 2024: 301-307 (2019)