bioRxivBioinformaticsPreprint
HLAProphet: Personalized allele-level quantification of the HLA proteins
Michael B. MumphreyiD, Ginny Xiaohe LiiD, Noshad HosseiniiD, Alexey I. NesvizhskiiiD, Marcin P. CieslikiD
Abstract
Loss of HLA expression in tumor cells is a commonly observed phenotype that is known to be associated with T-cell evasion. Proteogenomic characterizations of the molecular mechanisms underpinning this loss of HLA expression are hindered by the polymorphic nature of the HLA proteins, with most individuals having germline HLA sequences that are highly divergent from the sequences found in standard reference databases. To address this issue, we have developed HLAProphet, an algorithm that utilizes HLA types from paired DNA sequencing data to provide personalized allele-level quantification of the HLA proteins from TMT mass spectrometry data. We show that HLAProphet triples the number of tryptic peptide identifications made by standard reference based approaches, and produces protein expression values that have high concordance with RNA expression and known loss of heterozygosity events.
- organism: human
- assay: proteomics
- research focus: protein structure prediction
- research focus: variant calling
- research focus: differential expression
- approach: statistical model
- Citations
- 0
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- scHLAcount: Allele-specific HLA expression from single-cell gene expression data
Charlotte A. Darby, Michael J. T. Stubbington, … Ian T. Fiddes·BioinformaticsPeer-reviewed·Apr 17, 2020
- Gene and protein sequence features augment HLA class I ligand predictions
K. Bresser, B. Nicolet, … T. Schumacher·bioRxiv·Sep 3, 2023
- EPIP: MHC-I epitope prediction integrating mass spectrometry derived motifs and tissue-specific expression profiles
Weipeng Hu, Si Qiu, … Leo J. Lee·bioRxiv·Aug 24, 2020
- HLApollo: A superior transformer model for pan-allelic peptide-MHC-I presentation prediction, with diverse negative coverage, deconvolution and protein language features.
W. J. Thrift, N. W. Lounsbury, … S. Jhunjhunwala·bioRxiv·Dec 8, 2022
- Measuring peptide-MHC generalization to unseen alleles across both HLA classes
Venkatesh P. Mysore·bioRxiv·Jun 23, 2026
- Validated machine learning algorithm with sub-clonal sensitivity reveals widespread pan-cancer human leukocyte antigen loss of heterozygosity
Rachel Marty Pyke, Dattatreya Mellacheruvu, … Sean Michael Boyle·bioRxiv·May 20, 2021