Immunopeptidomics Collection

Curated collection of representative recent manuscripts (last 12 months; compiled in January 2026) that used the MSFragger/FragPipe computational platform for immunopeptidomics.

HLA export by melanoma cells decoys cytotoxic T cells to promote immune evasion
Chemla, Y.; Itzhaki, O.; Melamed, S.; Weller, C.; Sade, Y.; Manich, P.; Reshef, K.; Xenidis, N.; Levy, G.; Parikh, R.; Bartok, O.; Tal, I.; et al.; Cell. 2026.
https://doi.org/10.1016/j.cell.2025.11.020

Immunopeptidomics analysis comparing MHC class I–bound peptides isolated from melanoma cells and their secreted melanosomes. Eluted peptides were analyzed by LC–MS/MS using MSFragger within FragPipe with non-enzymatic search parameters and peptide-level FDR control. FragPipe-based processing enabled side-by-side characterization of cellular and melanosomal immunopeptidomes, showing extensive overlap in peptide repertoires, detection of tumor-associated antigens, and identification of a small number of mutation-derived neoantigens on melanosomes. These immunopeptidomics results were combined with proteomic profiling and TCR sequencing to assess how melanosome-associated HLA–peptide complexes interact with CD8⁺ T cells.

Adoptive T cell therapy targeting an inducible and broadly shared product of aberrant mRNA translation
Champagne, J.; Nielsen, M.M.; Feng, X.; Montenegro Navarro, J.; Pataskar, A.; Voogd, R.; Giebel, L.; Nagel, R.; Berenst, N.; Fumagalli, A.; Kochavi, A.; Lovecchio, D.; Valcanover, L.; Malka, Y.; Yang, W.; Laos, M.; Li, Y.; Proost, N.; van de Ven, M.; van Tellingen, O.; Bleijerveld, O.B.; Haanen, J.B.A.G.; Olweus, J.; Agami, R.; Immunity. 2025.
https://doi.org/10.1016/j.immuni.2024.12.004

Immunopeptidomics study examining interferon-γ– and tryptophan-depletion–induced changes in the MHC class I peptide repertoire of cancer cells. HLA class I–bound peptides were isolated from multiple cancer cell lines and analyzed by LC–MS/MS, with data processed using MSFragger within FragPipe against a combined wild-type and W→F-substitutant peptide database. FragPipe-based non-enzymatic searches with peptide-level FDR control enabled systematic identification of hundreds of W→F neoepitopes, primarily restricted to HLA-A*24:02. The resulting immunopeptidomics data were used to define shared, inducible neoepitopes and to support downstream functional evaluation of T cell recognition and adoptive T cell therapy models.

Translation dysregulation in cancer as a source for targetable antigens
Weller, C.; Bartok, O.; McGinnis, C.S.; Palashati, H.; Chang, T.-G.; Malko, D.; et al.; Cancer Cell. 2025.
https://doi.org/10.1016/j.ccell.2025.03.003

Immunopeptidomics analysis of melanoma cells with loss of the tRNA-modifying enzyme TYW2. HLA class I–bound peptides from wild-type and TYW2 knockout cells were analyzed by LC–MS/MS and processed using MSFragger within FragPipe with non-enzymatic search settings and peptide-level FDR control. FragPipe-enabled analysis identified canonical and out-of-frame peptides associated with impaired translation fidelity, supporting comparisons of how translational dysregulation alters the MHC class I peptide repertoire.

Peptidomic and proteomic analysis of precision-cut lung slice supernatants
Hansen, A.H.; Lorentzen, L.G.; Leeming, D.J.; Sand, J.M.B.; Hägglund, P.; Davies, M.J.; Analytical Biochemistry. 2025.
https://doi.org/10.1016/j.ab.2025.115837

Method-focused study establishing a combined peptidomics and proteomics workflow for secretome analysis of precision-cut lung slices. LC–MS/MS DDA-PASEF data were processed using MSFragger within FragPipe to support unspecific (peptidomic) and semi-specific (proteomic) database searches against the rat UniProt reference proteome. FragPipe was configured with broad peptide length and mass ranges to enable identification of endogenous peptides lacking defined enzymatic termini, alongside conventional proteolytic peptides, with 1% PSM-level FDR control. IonQuant was used for MS1-based intensity extraction with match-between-runs enabled, allowing quantitative comparison across biological and technical replicates. The study demonstrates FragPipe’s suitability for large-search-space, non-tryptic peptide identification workflows relevant to immunopeptidomics-style analyses of naturally presented or secreted peptides.

Increased EThcD efficiency on the hybrid Orbitrap Excedion Pro mass analyzer extends the depth in identification and sequence coverage of HLA class I immunopeptidomes
Kessler, A.L.; Fort, K.L.; Resemann, H.C.; Krüger, P.; Wang, C.; Koch, H.; Hauschild, J.-P.; Marino, F.; Heck, A.J.R.; Molecular & Cellular Proteomics. 2025.
https://doi.org/10.1016/j.mcpro.2025.101049

Methodology-focused immunopeptidomics study evaluating EThcD fragmentation on the Orbitrap Excedion Pro across multiple HLA class I ligandomes. HLA-bound peptides from several cell lines were analyzed by LC–MS/MS using HCD and EThcD, with data processed using MSFragger within FragPipe under non-enzymatic digestion settings and peptide-level FDR control. FragPipe-based analysis was used to compare identification rates, sequence coverage, and allele-specific biases between fragmentation modes, showing that EThcD increases sequence coverage and recovers subsets of immunopeptides that are less efficiently identified by HCD alone, including peptides with internal arginine residues and arginine methylation.

Benchmarking software for DDA-PASEF immunopeptidomics
Chen, Y.; Preikschat, A.; Arnold, A.; Pecori, R.; Gomez-Zepeda, D.; Tenzer, S.; Molecular & Cellular Proteomics. 2026.
https://doi.org/10.1016/j.mcpro.2025.101492

Systematic benchmarking study comparing commonly used software platforms for DDA-PASEF–based immunopeptidomics, including FragPipe (with MSFragger), PEAKS, MaxQuant, and MHCquant. HLA class I and II peptides enriched from the JY cell line were analyzed under non-enzymatic search settings with 1% PSM-level FDR control. FragPipe was evaluated using MSFragger with MSBooster rescoring and MS1-based quantification and was among the top-performing tools in terms of peptide coverage. In particular, FragPipe showed strong performance when using expanded and personalized databases, highlighting its suitability for immunopeptidomics applications that require large or customized search spaces, such as neoantigen discovery.

Immunogenic cryptic peptides dominate the antigenic landscape of ovarian cancer
Raja, R.; Mangalaparthi, K.K.; Madugundu, A.K.; Jessen, E.; Pathangey, L.; Magtibay, P.; Butler, K.; Christie, E.; Pandey, A.; Curtis, M.; Science Advances. 2025.
https://doi.org/10.1126/sciadv.ads7405

Proteogenomic immunopeptidomics study of metastatic ovarian cancer combining RNA-seq–derived personalized databases with LC–MS/MS analysis of HLA class I peptides. Data were processed using MSFragger within FragPipe with non-enzymatic search settings and PSM-level FDR control, enabling identification of canonical and cryptic peptides, including those derived from noncoding transcripts. The FragPipe-supported analysis was used to assess the contribution of cryptic peptides to the ovarian cancer antigen repertoire and to guide downstream prioritization for functional testing.

Peptidomics mapping of proteolysis highlights triple activation of sprouted seeds by germination, homogenisation and species mixture
Bera, I.; Fernández-Díaz, R.; O’Sullivan, M.; Jacquier, J.-C.; Scaife, C.; Litovskich, G.; Wynne, K.; Shields, D.C.; bioRxiv. 2026.
https://doi.org/10.64898/2026.01.08.698447

Large-scale plant peptidomics study examining how germination, homogenisation, and inter-species mixing alter proteolytic patterns in chickpea, lentil, mung, and broccoli sprouts. Endogenous peptides were analyzed by LC–MS/MS on a timsTOF Pro, with data processed using MSFragger within FragPipe under non-specific digestion settings and broad peptide length constraints. FragPipe outputs were used for peptide identification, label-free quantification with IonQuant, and downstream extraction of N- and C-terminal cleavage motifs to compare species-specific and cooperative proteolysis across experimental conditions.

Deep learning to decode sites of RNA translation in normal and cancerous tissues
Clauwaert, J.; McVey, Z.; Gupta, R.; Yannuzzi, I.; Basrur, V.; Nesvizhskii, A.I.; Menschaert, G.; Prensner, J.R.; Nature Communications. 2025.
https://doi.org/10.1038/s41467-025-56543-0

Study introducing RiboTIE, a transformer-based method for identifying translated open reading frames from ribosome profiling data, with orthogonal mass spectrometry validation. Proteomics data from medulloblastoma cell lines were analyzed using FragPipe with MSFragger under standard tryptic settings, with protein databases augmented by RiboTIE- and ORFquant-predicted non-canonical ORFs. FragPipe-based searching enabled detection of peptides supporting selected non-canonical ORFs and N-terminal extensions, providing independent MS evidence for a subset of RiboTIE-predicted translation events and supporting integration of ribosome profiling with proteomics for non-canonical proteome characterization.

Adoptive T cell therapy targeting an inducible and broadly shared product of aberrant mRNA translation
Champagne, J.; Nielsen, M.M.; Feng, X.; Montenegro Navarro, J.; Pataskar, A.; Voogd, R.; Giebel, L.; Nagel, R.; Berenst, N.; Fumagalli, A.; Kochavi, A.; Lovecchio, D.; Valcanover, L.; Malka, Y.; Yang, W.; Laos, M.; Li, Y.; Proost, N.; van de Ven, M.; Haanen, J.B.A.G.; Olweus, J.; Agami, R.; Immunity. 2025.
https://doi.org/10.1016/j.immuni.2024.12.004

Immunopeptidomics study investigating tryptophan-depletion–induced W→F substitutant neoepitopes as targets for adoptive T cell therapy. HLA class I peptides were isolated from multiple cancer cell lines treated with IFNγ and analyzed by LC–MS/MS, with data searched using MSFragger within FragPipe against concatenated wild-type and W→F–substitutant databases under non-enzymatic settings. FragPipe-based analysis enabled identification of hundreds of W→F neoepitopes, including a set of shared HLA-A*24:02–restricted peptides, which were subsequently used to guide TCR discovery, validation, and functional testing in vitro and in vivo.

Deep exploration of the immunopeptidome of a pancreatic cancer cell line: implications for clinical immunopeptidomics and immunotherapy
Dorvash, M.; Illing, P.T.; Croft, N.P.; Ramarathinam, S.H.; Purcell, A.W.; Molecular & Cellular Proteomics. 2025.
https://doi.org/10.1016/j.mcpro.2025.101030

Immunopeptidomics study of the Panc1 pancreatic cancer cell line using DDA and DIA LC–MS/MS. HLA class I peptides were identified with MSFragger in FragPipe under non-enzymatic search settings and peptide-level FDR control. FragPipe supported single-shot and high-pH RP–fractionated analyses, spectral library generation, and low-input experiments, enabling identification of thousands of HLA ligands, including cancer-testis antigen–derived peptides.

Detection and quantitation of small proteins using mass spectrometry
Franco, P.H.C.; Zeinert, R.; Meier-Credo, J.; Storz, G.; Langer, J.D.; Molecular & Cellular Proteomics. 2025.
https://doi.org/10.1016/j.mcpro.2025.101052

Systematic benchmarking study of mass spectrometry strategies for identifying and quantifying small proteins (<50 aa) in Escherichia coli. LC–MS/MS data from top–down and bottom–up workflows (DDA, DIA, PRM) were processed using MSFragger within FragPipe under non-specific and semi-specific digestion settings, with peptide- and protein-level FDR control. FragPipe-based analysis was used to compare data acquisition modes, sample preparation strategies, and database search configurations, showing strong performance for small-protein detection in DDA and DIA datasets and supporting open and proteogenomic searches. While not an immunopeptidomics study per se, the work is relevant to immunopeptidomics-style analyses due to its focus on short peptides, non-tryptic search spaces, and large-database searching.

PCI-DB: a novel primary tissue immunopeptidome database to guide next-generation peptide-based immunotherapy development**
Lemke, S.; Dubbelaar, M.L.; Zimmermann, P.; Bauer, J.; Nelde, A.; Hoenisch Gravel, N.; Scheid, J.; Wacker, M.; Jung, S.; Dengler, A.; Maringer, Y.; Rammensee, H.-G.; Gouttefangeas, C.; Nahnsen, S.; Walz, J.S.; *Journal for ImmunoTherapy of Cancer. 2025.
https://doi.org/10.1136/jitc-2024-011366

Large-scale immunopeptidomics resource aggregating HLA class I and II peptides from >3,000 malignant and benign primary tissue samples. Raw LC–MS/MS data were reprocessed in a standardized manner using open-source immunopeptidomics pipelines that incorporate MSFragger-based peptide identification with global peptide-level FDR control. The resulting PCI-DB comprises millions of HLA peptides across >40 tissue and cancer entities and supports comparative analysis of tumor-exclusive peptides, cancer-testis antigens, and naturally presented neoepitopes, enabling data-driven selection of peptide targets for immunotherapy development.

PEPSeek-mediated identification of novel epitopes from viral and bacterial pathogens and the impact on host cell immunopeptidomes
Cormican, J.A.; Medfai, L.; Wawrzyniuk, M.; Pasen, M.; Afrache, H.; Fourny, C.; Khan, S.; Gneiße, P.; Soh, W.T.; Timelli, A.; Nolfi, E.; Pannekoek, Y.; Cope, A.; Urlaub, H.; Sijts, A.J.A.M.; Mishto, M.; Liepe, J.; Molecular & Cellular Proteomics. 2025.
https://doi.org/10.1016/j.mcpro.2025.100937

Immunopeptidomics methods paper introducing PEPSeek, a workflow for sensitive identification of pathogen-derived MHC class I peptides from infected cells. Raw LC–MS/MS data were searched using MSFragger (integrated via inSPIRE/PEPSeek) under non-enzymatic settings with posterior error probability–based filtering. MSFragger-based identification was used to detect viral and bacterial epitopes from SARS-CoV-2, Listeria monocytogenes, and Chlamydia trachomatis, and to support quantitative comparisons of pathogen- and host-derived immunopeptidomes across infection conditions.

Method-focused immunopeptidomics study assessing EThcD fragmentation performance on the Orbitrap Excedion Pro. HLA class I peptides from multiple cell lines were analyzed by LC–MS/MS using HCD and EThcD, with data processed using MSFragger within FragPipe under non-enzymatic search settings and peptide-level FDR control. FragPipe-based analysis was used to compare identification counts and sequence coverage across fragmentation modes, showing that EThcD increases sequence coverage and recovers subsets of HLA peptides that are less efficiently identified by HCD alone.

Evaluation of mass spectrometry strategies for detecting and quantifying small proteins (<50 amino acids) using Escherichia coli as a model system, with data processed using MSFragger within FragPipe. Top–down and bottom–up LC–MS/MS workflows (DDA, DIA, PRM) were analyzed under non-specific and semi-specific digestion settings, enabling identification of short and non-tryptic peptides with FDR control. FragPipe-based analysis supported comparison of acquisition and quantification strategies and demonstrated suitability of MSFragger for small-protein and large-search-space workflows.

Proteogenomic immunopeptidomics study of metastatic ovarian cancer identifying HLA class I cryptic peptides derived from noncoding transcripts. Personalized, RNA-seq–derived three-frame databases were searched using MSFragger within FragPipe under non-enzymatic settings with stringent FDR control, enabling sensitive detection of noncanonical peptides absent from reference proteomes. FragPipe-based analysis showed that cryptic peptides comprise a small fraction of the ligandome but represent a dominant class of tumor antigens and informed downstream immunogenicity testing.