ms2pip

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MS²PIP: MS2 Peak Intensity Prediction - Fast and accurate peptide fragmentation spectrum prediction for multiple fragmentation methods, instruments and labeling techniques.

About

MS²PIP is a tool to predict MS2 peak intensities from peptide sequences. The result is a predicted peptide fragmentation spectrum that accurately resembles its observed equivalent. These predictions can be used to validate peptide identifications, generate proteome-wide spectral libraries, or to select discriminative transitions for targeted proteomics. MS²PIP employs the XGBoost <https://xgboost.readthedocs.io/en/stable/>_ machine learning algorithm and is written in Python and C.

.. figure:: https://raw.githubusercontent.com/compomics/ms2pip/v4.0.0/img/mirror-DVAQIFNNILR-2.png

Mirror plot of an observed (top) and MS²PIP-predicted (bottom) spectrum for the peptide DVAQIFNNILR/2.

You can install MS²PIP on your machine by following the installation instructions <https://ms2pip.readthedocs.io/en/latest/installation/>. For a more user-friendly experience, go to the MS²PIP web server <https://iomics.ugent.be/ms2pip>. There, you can easily upload a list of peptide sequences, after which the corresponding predicted MS2 spectra can be downloaded in multiple file formats. The web server can also be contacted through the RESTful API <https://iomics.ugent.be/ms2pip/api/>_.

The MS³PIP Python application can perform the following tasks:

• predict-single: Predict fragmentation spectrum for a single peptide and optionally visualize the spectrum.
• predict-batch: Predict fragmentation spectra for a batch of peptides.
• predict-library: Predict a spectral library from protein FASTA file.
• correlate: Compare predicted and observed intensities and optionally compute correlations.
• correlate-single: Compare predicted and observed intensities for a single peptide spectrum.
• get-training-data: Extract feature vectors and target intensities from observed spectra for training.
• annotate-spectra: Annotate peaks in observed spectra.

MS²PIP supports a wide range of PSM input formats and spectrum output formats, and includes pre-trained models for multiple fragmentation methods, instruments and labeling techniques. See Usage <https://ms2pip.readthedocs.io/en/latest/usage>_ for more information.

Related projects

• MS²Rescore <https://github.com/compomics/ms2rescore/>_: Use MS²PIP and other peptide prediction tools to boost peptide identification results.
• DeepLC <https://github.com/compomics/deeplc/>_: Retention time prediction for (modified) peptides using deep learning.
• IM2Deep <https://github.com/compomics/im2deep>_: Ion mobility prediction for (modified) peptides using deep learning.
• psm_utils <https://github.com/compomics/psm_utils/>_: Common utilities for parsing and handling peptide-spectrum matches and search engine results in Python

Citations

If you use MS²PIP for your research, please cite the following publication:

• Declercq, A., Bouwmeester, R., Chiva, C., Sabidó, E., Hirschler, A., Carapito, C., Martens, L., Degroeve, S., Gabriels, R. (2023). Updated MS²PIP web server supports cutting-edge proteomics applications. Nucleic Acids Research doi:10.1093/nar/gkad335 <https://doi.org/10.1093/nar/gkad335>_

Prior MS²PIP publications:

• Gabriels, R., Martens, L., & Degroeve, S. (2019). Updated MS²PIP web server delivers fast and accurate MS2 peak intensity prediction for multiple fragmentation methods, instruments and labeling techniques. Nucleic Acids Research doi:10.1093/nar/gkz299 <https://doi.org/10.1093/nar/gkz299>_
• Degroeve, S., Maddelein, D., & Martens, L. (2015). MS²PIP prediction server: compute and visualize MS2 peak intensity predictions for CID and HCD fragmentation. _Nucleic Acids Research, 43(W1), W326–W330. doi:10.1093/nar/gkv542 <https://doi.org/10.1093/nar/gkv542>_
• Degroeve, S., & Martens, L. (2013). MS²PIP: a tool for MS/MS peak intensity prediction. Bioinformatics (Oxford, England), 29(24), 3199–203. doi:10.1093/bioinformatics/btt544 <https://doi.org/10.1093/bioinformatics/btt544>_

Please also take note of, and mention, the MS²PIP version you used.

Full documentation

The full documentation, including installation instructions, usage examples, and the command-line and Python API reference, can be found at ms2pip.readthedocs.io <https://ms2pip.readthedocs.io>_.

Contributing

Bugs, questions or suggestions? Feel free to post an issue in the issue tracker <https://github.com/compomics/ms2pip/issues/>_ or to make a pull request. Any contribution, small or large, is welcome!