MS²PIP is a tool to predict MS2 signal peak intensities from peptide sequences. It employs the XGBoost machine learning algorithm and is written in Python.
Below, you can easily upload a list of peptide sequences or a protein FASTA file, after which the corresponding predicted MS2 spectra can be downloaded in multiple file formats. We also provide ready-to-download proteome-wide spectral libraries for various model organisms, ideal for DIA or DDA spectral library searching.
More advanced users can also access MS²PIP Server through our RESTful API. We provide Swagger-generated API documentation and an example Python script to contact the API.
For more customizability, MS²PIP can be installed locally. Check out the MS²PIP GitHub repository for more information.
If you use MS²PIP for your research, please cite the following publication:
Prior MS²PIP publications:
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
has to correspond
to a preset or custom PTM (see below) . 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.
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:
N-term
or C-term
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 (eg PhosphoS
,
PhosphoT
and PhosphoY
or TMT6plex
and
TMT6plexN
). N- and C-terminal modifications can be added in the same way, but
require N-term
or C-term
instead of an amino acid code.
Example of a custom modification list:
Oxidation,15.994915,M
Carbamidomethyl,57.021464,C
PhosphoS,79.966331,S
PhosphoT,79.966331,T
PhosphoY,79.966331,Y
iTRAQ,144.102063,N-term
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.
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 v3.11.0.
Model | Fragmentation method | MS2 mass analyzer | Peptide properties |
---|---|---|---|
HCD (2021) New | HCD | Orbitrap | Tryptic/Chymotrypsin digest |
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 |
Immuno-HCD New | HCD | Orbitrap | Immunopeptides (HLA class I and class II) |
CID-TMT New | 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.
Download pregenerated spectral libraries for common model organisms here. These libraries are available in multiple download formats and are compatible with most DIA or DDA spectral library search engines. The libraries are updated whenever new prediction models are available and at least yearly with new UniProt Proteome versions. Older version remain available for download here.
Protein sequences downloaded from UniProt Proteomes on 13/02/2023. Contains 20594 protein entries.
MSP (DIA-NN compatible) BiblioSpec / Skyline (SSL, MS2)Protein sequences downloaded from UniProt Proteomes on 13/02/2023. Contains 27498 protein entries.
MSP (DIA-NN compatible) BiblioSpec / Skyline (SSL, MS2)Protein sequences downloaded from UniProt Proteomes on 12/12/2022. Contains 23844 protein entries.
MSP (DIA-NN compatible) BiblioSpec / Skyline (SSL, MS2)Protein sequences downloaded from UniProt Proteomes on 12/12/2022. Contains 19838 protein entries.
MSP (DIA-NN compatible) BiblioSpec / Skyline (SSL, MS2)Protein sequences downloaded from UniProt Proteomes on 12/12/2022. Contains 23844 protein entries.
MSP (DIA-NN compatible) BiblioSpec / Skyline (SSL, MS2)Protein sequences downloaded from UniProt Proteomes on 12/12/2022. Contains 20358 protein entries.
MSP (DIA-NN compatible) BiblioSpec / Skyline (SSL, MS2)Protein sequences downloaded from UniProt Proteomes on 12/12/2022. Contains 13821 protein entries.
MSP (DIA-NN compatible) BiblioSpec / Skyline (SSL, MS2)Protein sequences downloaded from UniProt Proteomes on 13/02/2023. Contains 4402 protein entries.
MSP (DIA-NN compatible) BiblioSpec / Skyline (SSL, MS2)Protein sequences downloaded from UniProt Proteomes on 13/02/2023. Contains 21968 protein entries.
MSP (DIA-NN compatible) BiblioSpec / Skyline (SSL, MS2)Protein sequences downloaded from UniProt Proteomes on 12/12/2022. Contains 22860 protein entries.
MSP (DIA-NN compatible) BiblioSpec / Skyline (SSL, MS2)[2, 3]
8
30
None
None
trypsin
2
[{'name': 'Acetyl', 'unimod_accession': 1, 'mass_shift': 42.01057, 'protein_n_term': True}, {'name': 'Oxidation', 'unimod_accession': 35, 'mass_shift': 15.9994, 'amino_acid': 'M'}, {'name': 'Carbamidomethyl', 'unimod_accession': 4, 'mass_shift': 57.0513, 'amino_acid': 'C'}]
HCD2021
True
A prebuilt in silico predicted spectral library was downloaded from the MS²PIP web server (https://iomics.ugent.be/ms2pip/). The library was generated using the following software packages: MS²PIP v3.11.0 for peptide spectrum prediction, DeepLC v1.2.1 for peptide retention time prediction, and Pyteomics v4.5.6 for parsing the FASTA file and applying in silico digestion. The following parameters were used: charges: [2, 3], min_peplen: 8, max_peplen: 30, min_precursor_mz: None, max_precursor_mz: None, cleavage_rule: trypsin, missed_cleavages: 2, modifications: [{'name': 'Acetyl', 'unimod_accession': 1, 'mass_shift': 42.01057, 'protein_n_term': True}, {'name': 'Oxidation', 'unimod_accession': 35, 'mass_shift': 15.9994, 'amino_acid': 'M'}, {'name': 'Carbamidomethyl', 'unimod_accession': 4, 'mass_shift': 57.0513, 'amino_acid': 'C'}], ms2pip_model: HCD2021, add_retention_time: True.
If you have any questions, feedback or suggestions, please contact one of the following people: