PMID- 31848261
OWN - NLM
STAT- MEDLINE
DCOM- 20201109
LR  - 20240229
IS  - 1535-9484 (Electronic)
IS  - 1535-9476 (Print)
IS  - 1535-9476 (Linking)
VI  - 19
IP  - 2
DP  - 2020 Feb
TI  - Mass Spectrometry Based Immunopeptidomics Leads to Robust Predictions of 
      Phosphorylated HLA Class I Ligands.
PG  - 390-404
LID - S1535-9476(20)35086-6 [pii]
LID - 10.1074/mcp.TIR119.001641 [doi]
AB  - The presentation of peptides on class I human leukocyte antigen (HLA-I) molecules 
      plays a central role in immune recognition of infected or malignant cells. In 
      cancer, non-self HLA-I ligands can arise from many different alterations, 
      including non-synonymous mutations, gene fusion, cancer-specific alternative mRNA 
      splicing or aberrant post-translational modifications. Identifying HLA-I ligands 
      remains a challenging task that requires either heavy experimental work for in 
      vivo identification or optimized bioinformatics tools for accurate predictions. 
      To date, no HLA-I ligand predictor includes post-translational modifications. To 
      fill this gap, we curated phosphorylated HLA-I ligands from several 
      immunopeptidomics studies (including six newly measured samples) covering 72 
      HLA-I alleles and retrieved a total of 2,066 unique phosphorylated peptides. We 
      then expanded our motif deconvolution tool to identify precise binding motifs of 
      phosphorylated HLA-I ligands. Our results reveal a clear enrichment of 
      phosphorylated peptides among HLA-C ligands and demonstrate a prevalent role of 
      both HLA-I motifs and kinase motifs on the presentation of phosphorylated 
      peptides. These data further enabled us to develop and validate the first 
      predictor of interactions between HLA-I molecules and phosphorylated peptides.
CI  - (c) 2020 Solleder et al.
FAU - Solleder, Marthe
AU  - Solleder M
AD  - Department of Oncology, Ludwig Institute for Cancer Research, University of 
      Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, 
      Switzerland.
FAU - Guillaume, Philippe
AU  - Guillaume P
AD  - Department of Oncology, Ludwig Institute for Cancer Research, University of 
      Lausanne, Lausanne, Switzerland.
FAU - Racle, Julien
AU  - Racle J
AD  - Department of Oncology, Ludwig Institute for Cancer Research, University of 
      Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, 
      Switzerland.
FAU - Michaux, Justine
AU  - Michaux J
AD  - Department of Oncology, Ludwig Institute for Cancer Research, University of 
      Lausanne, Lausanne, Switzerland; Department of Oncology, Ludwig Institute for 
      Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland.
FAU - Pak, Hui-Song
AU  - Pak HS
AD  - Department of Oncology, Ludwig Institute for Cancer Research, University of 
      Lausanne, Lausanne, Switzerland; Department of Oncology, Ludwig Institute for 
      Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland.
FAU - Muller, Markus
AU  - Muller M
AD  - Swiss Institute of Bioinformatics, Lausanne, Switzerland.
FAU - Coukos, George
AU  - Coukos G
AD  - Department of Oncology, Ludwig Institute for Cancer Research, University of 
      Lausanne, Lausanne, Switzerland; Department of Oncology, Ludwig Institute for 
      Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland.
FAU - Bassani-Sternberg, Michal
AU  - Bassani-Sternberg M
AD  - Department of Oncology, Ludwig Institute for Cancer Research, University of 
      Lausanne, Lausanne, Switzerland; Department of Oncology, Ludwig Institute for 
      Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland. 
      Electronic address: Michal.Bassani@chuv.ch.
FAU - Gfeller, David
AU  - Gfeller D
AD  - Department of Oncology, Ludwig Institute for Cancer Research, University of 
      Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, 
      Switzerland. Electronic address: David.Gfeller@unil.ch.
LA  - eng
PT  - Journal Article
DEP - 20191217
PL  - United States
TA  - Mol Cell Proteomics
JT  - Molecular & cellular proteomics : MCP
JID - 101125647
RN  - 0 (Histocompatibility Antigens Class I)
RN  - 0 (Ligands)
RN  - 0 (Peptides)
SB  - IM
MH  - Histocompatibility Antigens Class I/genetics/*metabolism
MH  - Humans
MH  - Ligands
MH  - Mass Spectrometry
MH  - Peptides/*metabolism
MH  - Phosphorylation
MH  - Proteomics
PMC - PMC7000122
OTO - NOTNLM
OT  - HLA peptidomics
OT  - HLA-I ligand predictions
OT  - Mass spectrometry
OT  - computational biology
OT  - computational immunology
OT  - immunology
OT  - peptidomics
OT  - phosphorylated HLA-I binding motifs
OT  - phosphorylated HLA-I ligands
OT  - phosphorylation
COIS- * The authors declare that they have no conflicts of interest with the contents 
      of this article.
EDAT- 2019/12/19 06:00
MHDA- 2020/11/11 06:00
PMCR- 2021/02/01
CRDT- 2019/12/19 06:00
PHST- 2019/06/25 00:00 [received]
PHST- 2019/12/06 00:00 [revised]
PHST- 2019/12/19 06:00 [pubmed]
PHST- 2020/11/11 06:00 [medline]
PHST- 2019/12/19 06:00 [entrez]
PHST- 2021/02/01 00:00 [pmc-release]
AID - S1535-9476(20)35086-6 [pii]
AID - TIR119.001641 [pii]
AID - 10.1074/mcp.TIR119.001641 [doi]
PST - ppublish
SO  - Mol Cell Proteomics. 2020 Feb;19(2):390-404. doi: 10.1074/mcp.TIR119.001641. Epub 
      2019 Dec 17.