High-quality peptide evidence for annotating non-canonical open reading frames as human proteins

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Сен. 9, 2024

A major scientific drive is to characterize the protein-coding genome as it provides primary basis for study of human health. But fundamental question remains: what has been missed in prior genomic analyses? Over past decade, translation non-canonical open reading frames (ncORFs) observed across cell types and disease states, with implications proteomics, genomics, clinical science. However, impact ncORFs limited by absence a large-scale understanding their contribution proteome. Here, we report collaborative efforts stakeholders immunopeptidomics, Ribo-seq ORF discovery, gene annotation, produce consensus landscape protein-level evidence ncORFs. We show that at least 25% set 7,264 give rise translated products, yielding over 3,000 peptides pan-proteome analysis encompassing 3.8 billion mass spectra from 95,520 experiments. With these data, developed an annotation framework created public tools researchers through GENCODE PeptideAtlas. This work will provide platform advance ncORF-derived proteins biomedical discovery and, beyond humans, diverse animals plants where are similarly observed.

Язык: Английский

Maximizing immunopeptidomics-based bacterial epitope discovery by multiple search engines and rescoring

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 23, 2024

Mass spectrometry-based discovery of bacterial immunopeptides presented by infected cells allows untargeted antigens that can serve as vaccine candidates. Reliable identification epitopes such immunopeptidomics approaches is however challenged their extreme low abundance. Here, we describe an optimized bioinformatical framework to enhance the confident immunopeptides. Immunopeptidomics data cell cultures with foodborne model pathogen Listeria monocytogenes were searched four different search engines, PEAKS, Comet, Sage and MSFragger, followed data-driven rescoring MS2Rescore. Compared standard single search-engine results, this integrated workflow boosted number identified on average 27% led high detection 18 additional peptides (+27%) matching 15 proteins (+36%). Despite overall large agreement between a small conflicts (< 1%) in spectra-to-peptide assignments revealed ambiguous identifications served quality filter. Finally, show compatibility our sensitive timsTOF acquisition find rescoring, now inclusion ion mobility features, identifies 76% more compared orbitrap-based acquisition. Together, results demonstrate how integration multiple engine along maximizes immunopeptides, boosting for development.

Язык: Английский

MHCquant2 refines immunopeptidomics tumor antigen discovery

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Дек. 4, 2024

The identification of human leukocyte antigen (HLA)-presented peptides as targets anti-cancer T cell response is pivotal for the development novel immunotherapies. Mass spectrometry (MS)-based immunopeptidomics enables detection these peptides, yet confident identifications and thus implementation in immunotherapy design are hampered by high diversity low abundance naturally presented HLA peptides. Here, we introduce MHCquant2, a Nextflow-based open-source pipeline that leverages OpenMS tools peptide property predictors (DeepLC, MS²PIP) highly sensitive scalable quantification across various MS platforms. MHCquant2 increased up to 27% with significant expansion low-abundant outperforming state-of-the-art pipelines. Using build comprehensive benign tissue repository comprising re-analyzed data from available immunopeptidomes benign_MHCquant2 dataset, adding more than 160,000 First applications this enabled (i) refinement tumor-associated antigens, (ii) novel, high-frequent tumor-exclusive antigens multiple tumor entities, (iii) mutation-derived neoepitopes. refines discovery immunopeptidomics, paving way off-the-shelf personalized design.

Язык: Английский