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dc.contributor.authorSarkizova, Siranushen_US
dc.contributor.authorKlaeger, Susanen_US
dc.contributor.authorLe, Phuong M.en_US
dc.contributor.authorLi, Letitia W.en_US
dc.contributor.authorOliveira, Giacomoen_US
dc.contributor.authorKeshishian, Hasmiken_US
dc.contributor.authorHartigan, Christina R.en_US
dc.contributor.authorZhang, Wandien_US
dc.contributor.authorBraun, David A.en_US
dc.contributor.authorLigon, Keith L.en_US
dc.contributor.authorBachireddy, Pavanen_US
dc.contributor.authorZervantonakis, Ioannis K.en_US
dc.contributor.authorRosenbluth, Jennifer M.en_US
dc.contributor.authorOuspenskaia, Tamaraen_US
dc.contributor.authorLaw, Travisen_US
dc.contributor.authorJustesen, Suneen_US
dc.contributor.authorStevens, Jonathanen_US
dc.contributor.authorLane, William J.en_US
dc.contributor.authorEisenhaure, Thomasen_US
dc.contributor.authorLan Zhang, Guangen_US
dc.contributor.authorClauser, Karl R.en_US
dc.contributor.authorHacohen, Niren_US
dc.contributor.authorCarr, Steven A.en_US
dc.contributor.authorWu, Catherine J.en_US
dc.contributor.authorKeskin, Derin B.en_US
dc.coverage.spatialUnited Statesen_US
dc.date2019-10-24
dc.date.accessioned2020-08-10T17:01:33Z
dc.date.available2020-08-10T17:01:33Z
dc.date.issued2020-02
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/31844290
dc.identifier.citationSiranush Sarkizova, Susan Klaeger, Phuong M Le, Letitia W Li, Giacomo Oliveira, Hasmik Keshishian, Christina R Hartigan, Wandi Zhang, David A Braun, Keith L Ligon, Pavan Bachireddy, Ioannis K Zervantonakis, Jennifer M Rosenbluth, Tamara Ouspenskaia, Travis Law, Sune Justesen, Jonathan Stevens, William J Lane, Thomas Eisenhaure, Guang Lan Zhang, Karl R Clauser, Nir Hacohen, Steven A Carr, Catherine J Wu, Derin B Keskin. 2020. "A large peptidome dataset improves HLA class I epitope prediction across most of the human population.." Nat Biotechnol, Volume 38, Issue 2, pp. 199 - 209. https://doi.org/10.1038/s41587-019-0322-9
dc.identifier.issn1546-1696
dc.identifier.urihttps://hdl.handle.net/2144/41361
dc.descriptionPublished in final edited form as: Nat Biotechnol. 2020 February ; 38(2): 199–209. doi:10.1038/s41587-019-0322-9.en_US
dc.description.abstractPrediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I-associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, -B, -C and -G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena, providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I-associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines.en_US
dc.description.sponsorshipP01 CA229092 - NCI NIH HHS; P50 CA101942 - NCI NIH HHS; T32 HG002295 - NHGRI NIH HHS; T32 CA009172 - NCI NIH HHS; U24 CA224331 - NCI NIH HHS; R21 CA216772 - NCI NIH HHS; R01 CA155010 - NCI NIH HHS; U01 CA214125 - NCI NIH HHS; T32 CA207021 - NCI NIH HHS; R01 HL103532 - NHLBI NIH HHS; U24 CA210986 - NCI NIH HHSen_US
dc.format.extentp. 199 - 209en_US
dc.languageeng
dc.language.isoen_US
dc.relation.ispartofNat Biotechnol
dc.subjectAlgorithmsen_US
dc.subjectAllelesen_US
dc.subjectAmino acid motifsen_US
dc.subjectCell lineen_US
dc.subjectDatabases, proteinen_US
dc.subjectEpitopesen_US
dc.subjectGenetic locien_US
dc.subjectHistocompatibility antigens class Ien_US
dc.subjectHumansen_US
dc.subjectLigandsen_US
dc.subjectPeptide hydrolasesen_US
dc.subjectPeptidesen_US
dc.subjectProteasome endopeptidase complexen_US
dc.subjectProteomeen_US
dc.titleA large peptidome dataset improves HLA class I epitope prediction across most of the human populationen_US
dc.typeArticleen_US
dc.description.versionAccepted manuscripten_US
dc.identifier.doi10.1038/s41587-019-0322-9
pubs.elements-sourcepubmeden_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, Metropolitan Collegeen_US
pubs.publication-statusPublisheden_US
dc.identifier.mycv495456


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