Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Март 13, 2024

Abstract Human leukocyte antigen (HLA) class I peptide ligands (HLAIps) are key targets for developing vaccines and immunotherapies against infectious pathogens or cancer cells. Identifying HLAIps is challenging due to their high diversity, low abundance, patient individuality. Here, we develop a highly sensitive method identifying using liquid chromatography-ion mobility-tandem mass spectrometry (LC-IMS-MS/MS). In addition, train timsTOF-specific peak intensity MS 2 PIP model tryptic non-tryptic peptides implement it in Rescore (v3) together with the CCS predictor from ionmob. The optimized method, Thunder-DDA-PASEF, semi-selectively fragments singly multiply charged based on IMS m/z. Moreover, employs sensitivity mode extended resolution fewer MS/MS frames (300 ms TIMS ramp, 3 frames), doubling coverage of immunopeptidomics analyses, compared proteomics-tailored DDA-PASEF (100 10 frames). Additionally, rescoring boosts identification by 41.7% 33%, resulting 5738 as little one million JY cell equivalents, 14,516 20 million. This enables in-depth profiling diverse human lines plasma. Finally, Raji cells transfected express SARS-CoV-2 spike protein results 16 HLAIps, thirteen which have been reported elicit immune responses patients.

Язык: Английский

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Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors into Peptide Identification

Molecular & Cellular Proteomics, Год журнала: 2024, Номер 23(7), С. 100798 - 100798

Опубликована: Июнь 12, 2024

Язык: Английский

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TIMS²Rescore: A Data Dependent Acquisition-Parallel Accumulation and Serial Fragmentation-Optimized Data-Driven Rescoring Pipeline Based on MS²Rescore

Journal of Proteome Research, Год журнала: 2025, Номер unknown

Опубликована: Фев. 6, 2025

The high throughput analysis of proteins with mass spectrometry (MS) is highly valuable for understanding human biology, discovering disease biomarkers, identifying therapeutic targets, and exploring pathogen interactions. To achieve these goals, specialized proteomics subfields, including plasma proteomics, immunopeptidomics, metaproteomics, must tackle specific analytical challenges, such as an increased identification ambiguity compared to routine experiments. Technical advancements in MS instrumentation can mitigate issues by acquiring more discerning information at higher sensitivity levels. This exemplified the incorporation ion mobility parallel accumulation serial fragmentation (PASEF) technologies timsTOF instruments. In addition, AI-based bioinformatics solutions help overcome integrating data into workflow. Here, we introduce TIMS2Rescore, a data-driven rescoring workflow optimized DDA-PASEF from platform includes new MS2PIP spectrum prediction models IM2Deep, deep learning-based peptide predictor. Furthermore, fully streamline throughput, TIMS2Rescore directly accepts Bruker raw search results ProteoScape many other engines, Sage PEAKS. We showcase performance on immunopeptidomics (HLA class I II), metaproteomics sets. open-source freely available https://github.com/compomics/tims2rescore.

Язык: Английский

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Maximizing Immunopeptidomics-Based Bacterial Epitope Discovery by Multiple Search Engines and Rescoring

Journal of Proteome Research, Год журнала: 2025, Номер unknown

Опубликована: Март 13, 2025

Mass spectrometry-based discovery of bacterial immunopeptides presented by infected cells allows untargeted antigens that can serve as vaccine candidates. However, reliable identification epitopes is challenged their extremely low abundance. Here, we describe an optimized bioinformatic framework to enhance the confident immunopeptides. Immunopeptidomics data cell cultures with Listeria monocytogenes were searched four different search engines, PEAKS, Comet, Sage and MSFragger, followed data-driven rescoring MS2Rescore. Compared individual engine results, this integrated workflow boosted immunopeptide average 27% led high-confidence detection 18 additional peptides (+27%) matching 15 proteins (+36%). Despite strong agreement between a small number spectra (<1%) had ambiguous matches multiple excluded ensure identifications. Finally, demonstrate our sensitive timsTOF SCP acquisition find rescoring, now inclusion ion mobility features, identifies 76% more compared Q Exactive HF acquisition. Together, results how integration along maximizes identification, boosting for development.

Язык: Английский

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Trends in Mass Spectrometry-Based Single-Cell Proteomics

Analytical Chemistry, Год журнала: 2025, Номер unknown

Опубликована: Март 16, 2025

InfoMetrics Analytical ChemistryASAPArticle CiteCitationCitation and abstractCitation referencesMore citation options ShareShare onFacebookXWeChatLinkedInRedditEmailBlueskyJump toExpandCollapse ReviewMarch 16, 2025Trends in Mass Spectrometry-Based Single-Cell ProteomicsClick to copy article linkArticle link copied!Ximena Sanchez-AvilaXimena Sanchez-AvilaDepartment of Chemistry Biochemistry, Brigham Young University, Provo, Utah 84602, United StatesMore by Ximena Sanchez-AvilaView BiographyRaphaela M. de OliveiraRaphaela OliveiraDepartment Raphaela OliveiraView BiographySiqi HuangSiqi HuangDepartment Siqi HuangView BiographyChao WangChao WangDepartment Chao WangView Biographyhttps://orcid.org/0009-0008-6197-2985Ryan T. Kelly*Ryan KellyDepartment States*Email: [email protected]More Ryan KellyView Biographyhttps://orcid.org/0000-0002-3339-4443Other Access OptionsAnalytical ChemistryCite this: Anal. Chem. 2025, XXXX, XXX, XXX-XXXClick citationCitation copied!https://pubs.acs.org/doi/10.1021/acs.analchem.5c00661https://doi.org/10.1021/acs.analchem.5c00661Published March 2025 Publication History Received 28 January 2025Accepted February 2025Revised 23 2025Published online 16 2025review-article© American Chemical SocietyRequest reuse permissionsACS Publications© SocietySubjectswhat are subjects Article automatically applied from the ACS Subject Taxonomy describe scientific concepts themes article. Cells Isolation spectrometry Peptides proteins Sample preparation Note: In lieu an abstract, this is article's first page. Read To access article, please review available below. Get instant Purchase for 48 hours. Check out below using your ID or as a guest. Restore my guest Recommended through Your Institution You may have institution. institution does not content. Add change let them know you'd like include access. Through Recommend Name Loading Institutional Login Options... Change Explore subscriptions institutions Log with if you previously purchased it member benefits. hours $48.00 cart Checkout Cited By Click section linkSection copied!This has yet been cited other publications.Download PDF e-AlertsGet e-AlertsAnalytical copied!https://doi.org/10.1021/acs.analchem.5c00661Published 2025© permissionsArticle Views6Altmetric-Citations-Learn about these metrics closeArticle Views COUNTER-compliant sum full text downloads since November 2008 (both HTML) across all individuals. These regularly updated reflect usage leading up last few days.Citations number articles citing calculated Crossref daily. Find more information counts.The Altmetric Attention Score quantitative measure attention that research received online. Clicking on donut icon will load page at altmetric.com additional details score social media presence given how calculated.Recommended Articles

Язык: Английский

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TIMS²Rescore: A DDA-PASEF optimized data-driven rescoring pipeline based on MS²Rescore

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

Опубликована: Июнь 2, 2024

Abstract The high throughput analysis of proteins with mass spectrometry (MS) is highly valuable for understanding human biology, discovering disease biomarkers, identifying therapeutic targets, and exploring pathogen interactions. To achieve these goals, specialized proteomics subfields – such as plasma proteomics, immunopeptidomics, metaproteomics must tackle specific analytical challenges, an increased identification ambiguity compared to routine experiments. Technical advancements in MS instrumentation can counter issues by acquiring more discerning information at higher sensitivity levels, exemplified the incorporation ion mobility parallel accumulation - serial fragmentation (PASEF) technologies timsTOF instruments. In addition, AI-based bioinformatics solutions help overcome integrating data into workflow. Here, we introduce TIMS 2 Rescore, a data-driven rescoring workflow optimized DDA-PASEF from This platform includes new PIP spectrum prediction models IM2Deep, deep learning-based peptide predictor. Furthermore, fully streamline throughput, Rescore directly accepts Bruker raw data, search results ProteoScape many other engines, including Amanda PEAKS. We showcase performance on immunopeptidomics (HLA class I II), sets. open-source freely available https://github.com/compomics/tims2rescore .

Язык: Английский

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High-coverage immunopeptidomics using timsTOF mass spectrometers with Thunder-DDA-PASEF boosted by MS2Rescore

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

Опубликована: Авг. 6, 2024

Major histocompatibility complex (MHC, or Human leukocyte antigen, HLA) peptide ligands can be exploited to develop immunotherapies targeting immunogenic disease-specific immunopeptides, such as virus- cancer mutation-derived peptides. Liquid chromatography-coupled with mass spectrometry (LC-MS)-based immunopeptidomics is the gold standard for identifying MHC ligands. We previously optimized a workflow enabling identification of more than 10,000 class I per cell line. This process comprises three major steps: (I) high-recovery immunopeptidome enrichment, (II) an MS acquisition in timsTOF Pro called Thunder-Data-Dependent Acquisition Parallel Accumulation-SErial Fragmentation (Thunder-DDA-PASEF), (III) and using PEAKS XPro boosted by MS²Rescore data-driven rescoring. Here, we describe our deep-coverage step-by-step, from sample preparation data analysis validation.

Язык: Английский

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UniScore, a unified and universal measure for peptide identification by multiple search engines

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

Опубликована: Окт. 13, 2024

Abstract We propose UniScore as a metric for integrating and standardizing the outputs of multiple search engines in analysis data-dependent acquisition (DDA) data from LC/MS/MS-based bottom-up proteomics. is calculated annotation information attached to product ions alone by matching amino acid sequences candidate peptides suggested engine with ion spectrum. The acceptance criteria are controlled independently score values using false discovery rate based on target-decoy approach. Compared other rescoring methods that use deep learning-based spectral prediction, larger amounts can be processed minimal computing resources. When applied large-scale global proteome phosphoproteome data, approach outperformed each conventional single examined (Comet, X! Tandem, Mascot MaxQuant). Furthermore, could also directly peptide chimeric spectra without any additional filters.

Язык: Английский

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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

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MultiStageSearch: a multi-step proteogenomic workflow for taxonomic identification of viral proteome samples adressing database bias

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

Опубликована: Май 18, 2024

Abstract The recent years, with the global SARS-Cov-2 pandemic, have shown importance of strain level identification viral pathogens. While gold-standard approach for unkown sample remains genomics, studies necessity and advantages orthogonal experimental approaches such as proteomics, based on proteomic database search methods. databases required references both proteins genome sequences are known to be biased towards certain taxa, pathogenic strains or species, common model organisms. Aditionally, not comprehensive genomic databases. We present MultiStageSearch, an iterative taxonomic samples combining potentially species inferred using a generalist reference database. MultiStageSearch then automatically creates proteogenomic This is further pre-processed byfiltering duplicates well clustering identical ORFs address potential bias in Furthermore, workflow independent NCBI taxonomy, enabling inference that taxonomy. performed benchmark several demonstrate performance inference. shows superior compared state art methods untargeted data while being taxonomy at level.

Язык: Английский

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