Oktoberfest

Getting started

What you can do with oktoberfest:

• CE Calibration (CollisionEnergyCalibration)

This task estimates the optimal collision energy (CE) based on a given search result. You need to upload a RAW file as well as the MaxQuant's msms.txt for calibration. Prosit will:

1. Select a random subset of high-scoring PSMs
2. Predict those in for each CE from 18 to 39.
3. Calculate which CE achieves highest correlations with the experimental spectra Please note: Sequences with amino acid U or O are not supported. Modifications except "M(ox)" are not supported. Each C is treated as Cysteine with carbamidomethylation (fixed modification in MaxQuant).

• Spectral Library (SpectralLibraryGeneration)

This task generates a spectral library either by digesting a given FASTA file, or by predicting a list of peptides given in a CSV file. You need to provide a collision energy (CE) for prediction. To estimate an optimal CE for prediction, please use "CE Calibration". When a FASTA file is provided, Prosit will:

1. Digest the FASTA, for the given parameters (i.e. protease).
2. Predict all spectra at the given collision energy. When a CSV with peptides is provided, Prosit will directly predict all spectra.

• Rescoring (Rescoring)

This task rescores an existing MaxQuant search (FDR 100%) using features generated from fragmentation prediction. You need to upload a RAW file as well as the MaxQuant's msms.txt file from a search. Prosit will:

1. Calibrate itself against the RAW.
2. Predict all sequences in the msms.txt.
3. Use the predicted spectra to generate features for percolator.
4. Run percolator to rescore the search. Please note: You need a MaxQuant search at 100% FDR, otherwise targets may be filtered by MaxQuant's FDR calculation before rescoring. Sequences with amino acid U or O are not supported. Modifications except "M(ox)" are not supported. Each C is treated as Cysteine with carbamidomethylation (fixed modification in MaxQuant).

Installation

After cloning the repository of oktoberfest, create a new conda environment:

conda create -n oktoberfest

After activating the newly-created conda environment, go to the main folder with the repository of oktoberfest and run:

pip install .

Create a config.json file which should contain the following flags:

• jobType = CollisionEnergyAlignment, SpectralLibraryGeneration or Rescoring

• tag = tmt, tmtpro, itraq4 or itraq8; default = tmt

• peptide_identification_method = peptide identification method: percolator or mokapot; default = percolator

• allFeatures = true if all features should be used by the percolator; default = false

• regressionMethod = regression method for curve fitting (mapping from predicted iRT values to experimental retention times): lowess, spline or logistic; default = lowess

• fileUploads

  • search_type = Maxquant, Msfragger, Mascot or Internal; default = Maxquant

  • raw_type = thermo or mzml; default = thermo

  • fasta = path to the FASTA file, if FASTA file is provided

  • peptides.csv = true if you like to provide the list of peptides

• models

  • intensity = intensity model

  • irt = irt model

  • proteotypicity = proteotypicity model

• prosit_server = server for the Prosit prediction

• numThreads = number of threads from the config file; default = 1

• jobId = job ID for the Prosit prediction

• searchPath = path to the search file (if the search type is msfragger, then the path to the xlsx file should be provided); default = ""

The following flags are relevant only if a FASTA file is provided:

• fastaDigestOptions

  • fragmentation = fragmentation method: HCD or CID

  • digestion = digestion mode: full, semi or none; default = full

  • cleavages = number of allowed missed cleavages used in the search engine; default = 2

  • minLength = minimum peptide length allowed used in the search engine; default = 7

  • maxLength = maximum peptide length allowed used in the search engine; default = 60

  • enzyme = type of enzyme used in the search engine; default = trypsin

  • specialAas = special amino acids used by MaxQuant for decoy generation; default = KR

  • db = Target, decoy or concat; default = concat

An example of the config file can be found in /oktoberfest/example_config.json.

For prosit_server and jobId: ask Wassim Gabriel ([email protected]) or Ludwig Lautenbacher ([email protected]).

Finally, run

python oktoberfest/run_oktoberfest.py —-search_dir path_to_search_dir —-config_path path_to_config_file

Note: The search_dir should contain both the raw files and the MaxQuant's msms.txt from a search.

Models

• intensity models

  • Prosit_2019_intensity
  • Prosit_2020_intensity_hcd
  • Prosit_2020_intensity_cid
  • Prosit_2020_intensityTMT

• irt models

  • Prosit_2019_irt
  • Prosit_2020_irt_TMT

License

The project is licensed under the MIT license.

References

[1] Gessulat S, Schmidt T, Zolg DP, Samaras P, Schnatbaum K, Zerweck J, Knaute T, Rechenberger J, Delanghe B, Huhmer A, Reimer U, Ehrlich HC, Aiche S, Kuster B, Wilhelm M: “PROSIT: Proteome-wide prediction of peptide tandem mass spectra by deep learning”. Nature Methods. 2019; 16(6):509-518. doi: 10.1038/s41592-019-0426-7.

[2] Gabriel, Wassim & The, Matthew & Zolg, Daniel & Bayer, Florian & Shouman, Omar & Lautenbacher, Ludwig & Schnatbaum, Karsten & Zerweck, Johannes & Knaute, Tobias & Delanghe, Bernard & Huhmer, Andreas & Wenschuh, Holger & Reimer, Ulf & Médard, Guillaume & Kuster, Bernhard & Wilhelm, Mathias. (2022). Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides. Analytical Chemistry. 94. 10.1021/acs.analchem.1c05435.