MHC-I Ligand Discovery Using Targeted Database Searches of Mass Spectrometry Data: Implications for T-Cell Immunotherapies
Author(s) -
J. Patrick Murphy,
Prathyusha Konda,
Daniel J. Kowalewski,
Heiko Schuster,
Derek R. Clements,
Youra Kim,
Alejandro Cohen,
Tanveer Sharif,
Morten Nielsen,
Stefan Stevanović,
Patrick W. Lee,
Shashi Gujar
Publication year - 2017
Publication title -
journal of proteome research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.644
H-Index - 161
eISSN - 1535-3907
pISSN - 1535-3893
DOI - 10.1021/acs.jproteome.6b00971
Subject(s) - allotype , major histocompatibility complex , proteome , computational biology , epitope , human leukocyte antigen , biology , mhc class i , t cell , antigen , immunology , bioinformatics , immune system
Class I major histocompatibility complex (MHC-I)-bound peptide ligands dictate the activation and specificity of CD8 + T cells and thus are important for devising T-cell immunotherapies. In recent times, advances in mass spectrometry (MS) have enabled the precise identification of these MHC-I peptides, wherein MS spectra are compared against a reference proteome. Unfortunately, matching these spectra to reference proteome databases is hindered by inflated search spaces attributed to a lack of enzyme restriction in the searches, limiting the efficiency with which MHC ligands are discovered. Here we offer a solution to this problem whereby we developed a targeted database search approach and accompanying tool SpectMHC, that is based on a priori-predicted MHC-I peptides. We first validated the approach using MS data from two different allotype-specific immunoprecipitates for the C57BL/6 mouse background. We then developed allotype-specific HLA databases to search previously published MS data sets of human peripheral blood mononuclear cells (PBMCs). This targeted search strategy improved peptide identifications for both mouse and human ligandomes by greater than 2-fold and is superior to traditional "no enzyme" searches of reference proteomes. Our targeted database search promises to uncover otherwise missed novel T-cell epitopes of therapeutic potential.
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