Updated MS²PIP web server delivers fast and accurate MS² peak intensity prediction for multiple fragmentation methods, instruments and labeling techniques

peer-reviewed · Nucleic Acids Research · 2019

peer-reviewed · Nucleic Acids Research · 2019. Ralf Gabriels et al. MS²PIP is a data-driven tool that accurately predicts peak intensities for a given peptide’s fragmentation…
Date 2019-07-02
Type peer-reviewed
Venue Nucleic Acids Research
Publisher Oxford University Press (OUP)
Contribution adjacent
DOI 10.1093/nar/gkz299
Citations (OpenAlex) 91
Venue 2-year citedness 16.42

Abstract

MS²PIP is a data-driven tool that accurately predicts peak intensities for a given peptide’s fragmentation mass spectrum. Since the release of the MS²PIP web server in 2015, we have brought significant updates to both the tool and the web server. In addition to the original models for CID and HCD fragmentation, we have added specialized models for the TripleTOF 5600+ mass spectrometer, for TMT-labeled peptides, for iTRAQ-labeled peptides, and for iTRAQ-labeled phosphopeptides. Because the fragmentation pattern is heavily altered in each of these cases, these additional models greatly improve the prediction accuracy for their corresponding data types. We have also substantially reduced the computational resources required to run MS²PIP, and have completely rebuilt the web server, which now allows predictions of up to 100 000 peptide sequences in a single request. The MS²PIP web server is freely available at https://iomics.ugent.be/ms2pip/.

Authors

  1. Ralf Gabriels · Ghent University, VIB
  2. Lennart Martens · Ghent University, Infrastructure Nationale de Protéomique (ProFI-FR2048), University of Strasbourg, VIB
  3. Sven Degroeve · VIB

Methods and tools

  • MS2PIP: Predictor of MS/MS fragment-ion peak intensities, used to rescore search-engine and de novo sequencing results. Introduced in 2013 as a random-forest model; the 2019 web-server update added models for further fragmentation methods, instruments and labelling techniques, and the 2023 update added tryptic, non-tryptic, immunopeptide and TMT models and proteome-wide predicted spectral libraries with DeepLC retention times.

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