MHC-I Ligand Discovery Using Targeted Database Searches of Mass Spectrometry Data: Implications for T-Cell Immunotherapies.

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.

[1]  Hans-Georg Rammensee,et al.  HLA ligandome analysis identifies the underlying specificities of spontaneous antileukemia immune responses in chronic lymphocytic leukemia (CLL) , 2014, Proceedings of the National Academy of Sciences.

[2]  Ruedi Aebersold,et al.  A first dataset toward a standardized community-driven global mapping of the human immunopeptidome , 2016, Data in brief.

[3]  William Stafford Noble,et al.  Semi-supervised learning for peptide identification from shotgun proteomics datasets , 2007, Nature Methods.

[4]  P. Coulie,et al.  Tumour antigens recognized by T lymphocytes: at the core of cancer immunotherapy , 2014, Nature Reviews Cancer.

[5]  Deborah Hix,et al.  The immune epitope database (IEDB) 3.0 , 2014, Nucleic Acids Res..

[6]  Alessandro Sette,et al.  An open-source computational and data resource to analyze digital maps of immunopeptidomes , 2015, eLife.

[7]  Oliver Kohlbacher,et al.  The antigenic landscape of multiple myeloma: mass spectrometry (re)defines targets for T-cell-based immunotherapy. , 2015, Blood.

[8]  J. Yates,et al.  An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database , 1994, Journal of the American Society for Mass Spectrometry.

[9]  G. Hämmerling,et al.  Isolation of twelve monoclonal antibodies against Ia and H-2 antigens. Serological characterization and reactivity with B and T lymphocytes , 1979, Immunogenetics.

[10]  William Stafford Noble,et al.  Analysis of peptide MS/MS spectra from large-scale proteomics experiments using spectrum libraries. , 2006, Analytical chemistry.

[11]  Benjamin Schubert,et al.  EpiToolKit—a web-based workbench for vaccine design , 2015, Bioinform..

[12]  Bing Zhang,et al.  Protein identification using customized protein sequence databases derived from RNA-Seq data. , 2012, Journal of proteome research.

[13]  William Stafford Noble,et al.  Tandem Mass Spectrum Identification via Cascaded Search , 2015, Journal of proteome research.

[14]  S. Stevanović,et al.  Biochemical large-scale identification of MHC class I ligands. , 2013, Methods in molecular biology.

[15]  G. Hämmerling,et al.  Localization of allodeterminants on H-2Kb antigens determined with monoclonal antibodies and H-2 mutant mice. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Etienne Caron,et al.  Analysis of Major Histocompatibility Complex (MHC) Immunopeptidomes Using Mass Spectrometry* , 2015, Molecular & Cellular Proteomics.

[17]  Morten Nielsen,et al.  Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers , 2008, Bioinform..

[18]  M. Nielsen,et al.  NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets , 2016, Genome Medicine.

[19]  S. Lemieux,et al.  Global proteogenomic analysis of human MHC class I-associated peptides derived from non-canonical reading frames , 2016, Nature Communications.

[20]  M. Mann,et al.  Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. , 2003, Analytical chemistry.

[21]  Z. Modrušan,et al.  Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing , 2014, Nature.

[22]  C. Barnstable,et al.  Production of monoclonal antibodies to group A erythrocytes, HLA and other human cell surface antigens-new tools for genetic analysis , 1978, Cell.

[23]  H. Rammensee,et al.  SYFPEITHI: database for MHC ligands and peptide motifs , 1999, Immunogenetics.

[24]  N. Nagarajan,et al.  ERAAP Shapes the Peptidome Associated with Classical and Nonclassical MHC Class I Molecules , 2016, The Journal of Immunology.

[25]  P. Kloetzel,et al.  A large fraction of HLA class I ligands are proteasome-generated spliced peptides , 2016, Science.

[26]  V. Brusic,et al.  Evaluation of MHC class I peptide binding prediction servers: Applications for vaccine research , 2008, BMC Immunology.

[27]  Alexey I Nesvizhskii,et al.  Effective Leveraging of Targeted Search Spaces for Improving Peptide Identification in Tandem Mass Spectrometry Based Proteomics. , 2015, Journal of proteome research.

[28]  William Stafford Noble Mass spectrometrists should search only for peptides they care about , 2015, Nature Methods.

[29]  Morten Nielsen,et al.  Gapped sequence alignment using artificial neural networks: application to the MHC class I system , 2016, Bioinform..

[30]  Morten Nielsen,et al.  NetMHCcons: a consensus method for the major histocompatibility complex class I predictions , 2011, Immunogenetics.

[31]  Steven P Gygi,et al.  Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry , 2007, Nature Methods.

[32]  I. Mellman,et al.  Oncology meets immunology: the cancer-immunity cycle. , 2013, Immunity.

[33]  Brendan K Faherty,et al.  Optimization and Use of Peptide Mass Measurement Accuracy in Shotgun Proteomics*S , 2006, Molecular & Cellular Proteomics.