ArticleNucleic acids research2023
Updated MS²PIP web server supports cutting-edge proteomics applications.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed.
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Label-Free Targeted Proteomics Data Analysis Workflow Selection - Benchmarking AI-based and Data-Driven Approaches.Molecular & cellular proteomics : MCP · 2026Article
- Carafe2 enables high qualitybioRxiv : the preprint server for biology · 2026Article
- Unlocking the Next Decade of Proteomics with Standardized, Structured Metadata.Journal of proteome research · 2026Review
- The ProteomeXchange consortium in 2026: making proteomics data FAIR.Nucleic acids research · 2026Article
- The Charcot-Marie-Tooth Neuropathy (CMTX3) Complex Structural Variation Causes Differential SOX3 Spatiotemporal Expression.Human mutation · 2026Article
- Koina: Democratizing machine learning for proteomics research.Nature communications · 2025Article
- Enhancing peptide identification in metaproteomics through curriculum learning in deep learning.Nature communications · 2025Article
- MHCquant2 refines immunopeptidomics tumor antigen discovery.Genome biology · 2025Article
- The microbiologist's guide to metaproteomics.iMeta · 2025Review
- Open-Source and FAIR Research Software for Proteomics.Journal of proteome research · 2025Review
- Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models.Proteomics · 2025Review
- ProHap enables human proteomic database generation accounting for population diversity.Nature methods · 2025Article
- The Proteomics Standards Initiative Standardized Formats for Spectral Libraries and Fragment Ion Peak Annotations: mzSpecLib and mzPAF.Analytical chemistry · 2024Article
- Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification.Molecular & cellular proteomics : MCP · 2024Review
- ADAM19 cleaves the PTH receptor and associates with brachydactyly type E.Life science alliance · 2024Article
- Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MSNature communications · 2024Article
- OpenProt 2.0 builds a path to the functional characterization of alternative proteins.Nucleic acids research · 2024Article
- Bioinformatics Pipeline for Processing Single-Cell Data.Methods in molecular biology (Clifton, N.J.) · 2024Article
- What Can Ribo-Seq, Immunopeptidomics, and Proteomics Tell Us About the Noncanonical Proteome?Molecular & cellular proteomics : MCP · 2023Article
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9 authors.
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Abstract
Interest in the use of machine learning for peptide fragmentation spectrum prediction has been strongly on the rise over the past years, especially for applications in challenging proteomics identification workflows such as immunopeptidomics and the full-proteome identification of data independent acquisition spectra. Since its inception, the MS²PIP peptide spectrum predictor has been widely used for various downstream applications, mostly thanks to its accuracy, ease-of-use, and broad applicability. We here present a thoroughly updated version of the MS²PIP web server, which includes new and more performant prediction models for both tryptic- and non-tryptic peptides, for immunopeptides, and for CID-fragmented TMT-labeled peptides. Additionally, we have also added new functionality to greatly facilitate the generation of proteome-wide predicted spectral libraries, requiring only a FASTA protein file as input. These libraries also include retention time predictions from DeepLC. Moreover, we now provide pre-built and ready-to-download spectral libraries for various model organisms in multiple DIA-compatible spectral library formats. Besides upgrading the back-end models, the user experience on the MS²PIP web server is thus also greatly enhanced, extending its applicability to new domains, including immunopeptidomics and MS3-based TMT quantification experiments. MS²PIP is freely available at https://iomics.ugent.be/ms2pip/.
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