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 100.000. 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
1for the first AA.
0is reserved for N-terminal modifications.
Namehas to correspond to a preset or custom PTM (see below) . Unmodified peptides are marked with a hyphen (
charge: Precursor charge of the peptide.
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 files can be created from, for example, Excel, by exporting the table to a
MS²PIP GitHub repository, we also provide a host of Python scripts to convert common search engine output files to a PEPREC file.
A list of all modifications and the corresponding mass shifts is required for MS²PIP to properly calculate the fragmentation peak m/z values. Even though we provide specialized models for certain modififications, the specific modification info you provide here and in the PEPREC file does not influence the predicted peak intensites. It is only used to calculate m/z values.
You can select some preset modifications below or provide your own list. For the preset modifications, we use the PSI-MS names and monoisotopic mass shifts from Unimod. This means that, if you use these preset modifications, the modification names in your PEPREC file need to match the Unimod PSI-MS names. If MS²PIP encounters a modification in the PEPREC file that is not provided in the modifications list, it will skip that peptide.
If you provide your own list of modifications, each line can only contain one modification, with the following comma-separated properties:
If a certain modification occurs on different amino acids, every modification-amino acid combination
should have it's own entry and have a unique name
N- and C-terminal modifications can be added in the same way, but require
C-term instead of an amino acid code.
Example of a custom modification list:
MS²PIP currently supports the models listed in the table below. Always take note of the MS²PIP version and model versions you use and mention these in your publications. The current MS²PIP version is v20190312.
|Model||Current model version||Train-test dataset (unique peptides)||Evaluation dataset (unique peptides)||Median Pearson correlation on evaluation dataset|
(1 623 712)
|HCD2021||v20210416||[Combined dataset] (520 579)||PXD008034
|CID||v20190107||NIST CID Human
|NIST CID Yeast
|iTRAQphospho||v20190107||NIST iTRAQ phospho
|TMT||v20190107||Peng Lab TMT Spectral Library
(1 185 547)
|HCDch2 (including b++ and y++ ions)||v20190107||MassIVE-KB
(1 623 712)
|0.903786 (+) and 0.644162 (++)|
|CIDch2 (including b++ and y++ ions)||v20190107||NIST CID Human
|NIST CID Yeast
|0.904947 (+) and 0.813342 (++)|
|Immuno-HCD||v20210316||[Combined dataset] (460 191)||
|CID-TMT||v20220104||[in-house dataset] (72 138)||PXD005890
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 instance, all MS²PIP models were trained on tryptic peptides. As the C-terminal lysine and arginine heavily influence MS² fragmentation, these models are not intended to make predictions for non-tryptic peptides.
For more specific information on the experimental settings, please refer to the train datasets' publications (links are provided in the table above).
|Model||Fragmentation method||MS² mass analyzer||Peptide properties|
|CID||CID||Linear ion trap||Tryptic digest|
|iTRAQ||HCD||Orbitrap||Tryptic digest, iTRAQ-labeled|
|iTRAQphospho||HCD||Orbitrap||Tryptic digest, iTRAQ-labeled, enriched for phosphorylation|
|TMT||HCD||Orbitrap||Tryptic digest, TMT-labeled|
|TTOF5600||CID||Quadrupole Time-of-Flight||Tryptic digest|
|HCDch2 (including b++ and y++ ions)||HCD||Orbitrap||Tryptic digest|
|CIDch2 (including b++ and y++ ions)||CID||Linear ion trap||Tryptic digest|
|CID-TMT||CID||Linear ion trap||Tryptic digest, TMT-labeled|
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.