MS²PIP Server

Input file and settings

From peptide list

PEPREC file

MS²PIP takes a PEPREC (Peptide Record) file as an input. This is a space, tab, colon, or semicolon-separated file that lists all peptides. To run our server smoothly, we limit the number of peptides to 500000. If you need to predict more peptide spectra, we recommend you to split-up your dataset into multiple batches, or to download MS²PIP from GitHub and run it locally.

A PEPREC file contains the following columns:

• spec_id: A unique ID for the peptide.
• peptide: Peptide sequence.
• modifications: PTMs for the given peptide. Every modification is listed as location|name, separated by a pipe (|) between the location, the name, and other PTMs. The location is an integer counted starting at 1 for the first AA. 0 is reserved for N-terminal modifications. Name must be encoded as a ProForma 2 modification tag that can be resolved to a mass, for example a PSI-MOD or Unimod modification name or accession (e.g. oxidation or U:35), an elemental formula (e.g. Formula:C12H20O2), or an arbitrary mass shift (e.g. +15.9949). Unmodified peptides are marked with a hyphen (-).
• charge: Precursor charge of the peptide.

Optionally, a protein_list column can be provided with proteins formatted as "['example_protein_1', 'example_protein_2']". The provided proteins will then be written in the Comment field of the MSP file.

Example of a PEPREC file:

spec_id modifications peptide charge
peptide1 - ACDE 2
peptide2 2|Carbamidomethyl ACDEFGHI 3
peptide3 0|iTRAQ|10|Oxidation ACDEFGHIKMNPQ 2

Allowed filename extensions for the PEPREC file are: .peprec, .csv, .tsv and .txt.

PEPREC files can be created from, for example, Excel, by exporting the table to a .CSV file. We recommend to use the psm_utils Python package to convert various search engine output files to a PEPREC file.

From a protein FASTA file

MS²PIP an DeepLC can also generate a spectral library from a protein FASTA file. Protein entries will be first in silico digested to peptides and for each combination of precursor charge state and modifications, the fragmentation spectra will be predicted. As for a normal peptide list input, the modifications are only considered for the m/z values, not for the predicted peak intensities.

While some peptide 'search space' parameters can be configured on this web server, we recommend using a local MS²PIP installation for more flexibility. These restrictions are mainly put in place to avoid an overload due to accidental setting of parameters that lead to a combinatorial explosion of the peptide 'search space'. In the local version, more options are available for cleavage agents, peptide lengths, precursor charge states, custom modifications, output file formats, etc.

Prediction models

MS²PIP contains prediction models for various fragmentation modes, instruments, and peptide modifications. In the following table, we list all MS2 acquisition information and peptide properties for the different models. For optimal results, your experimental data should match the properties of the MS²PIP model. For more specific information on the experimental settings, please refer to the train dataset publications. These are listed on the MS²PIP GitHub README page .

Always take note of the MS²PIP version and model you use and mention these in your publications. The current online MS²PIP version is v4.1.1.

Results

MS²PIP predictions can be downloaded in CSV, MGF, MSP and BibloSpec / Skyline (SSL and MS2) file formats. Predicted intensities are normalized to the total ion current (sum of all intensities) and add up to 1 in the CSV file and to 10.000 in the MGF, MSP and MS2 files. On the download page we also provide an interactive visualization of the predicted spectra.