Detection of human unannotated microproteins by mass spectrometry-based proteomics: a community assessment
Aaron C. WacholderiD, Eric W. DeutschiD, Leron W. KokiD, Jip T. van DinteriD, Jiwon LeeiD, James C. WrightiD, Sébastien LeblanciD, Ayodya H JayatissaiD, Kevin JiangiD, Ihor ArefieviD, Kevin CaoiD, Francis BourassaiD, Félix-Antoine TrifiroiD, Michal Bassani‐SternbergiD, Pavel V. BaranoviD, Annelies BogaertiD, Sonia Pankaj ChothaniiD, Ivo Fierro-MontiiD, Daria FijałkowskaiD, Kris GevaertiD, Norbert HübneriD, Jonathan M. MudgeiD, Jorge Ruiz‐OreraiD, Jana Felicitas SchulziD, Juan Antonio VizcaínoiD, , Anne‐Ruxandra CarvunisiD
Abstract
Thousands of short open reading frames (sORFs) are translated outside of annotated coding sequences. Recent studies have pioneered searching for sORF-encoded microproteins in mass spectrometry (MS)- based proteomics and peptidomics datasets. Here, we assessed literature-reported MS-based identifications of unannotated human proteins. We find that studies vary by three orders of magnitude in the number of unannotated proteins they report. Of nearly 10,000 reported sORF-encoded peptides, 96% were unique to a single study, and 12% mapped to annotated proteins or proteoforms. Manual curation of a benchmark dataset of 406 manually evaluated spectra from 204 sORF-encoded proteins revealed large variation in peptide-spectrum match (PSM) quality between studies, with immunopeptidomics studies generally reporting higher quality PSMs than conventional enzymatic digests of whole cell lysates. We estimate that 65% of predicted sORF-encoded protein detections in immunopeptidomics studies were supported by high-quality PSMs versus 7.8% in non-immunopeptidomics datasets. Our work stresses the need for standardized protocols and analysis workflows to guide future advancements in microprotein detection by MS towards uncovering how many human microproteins exist.
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