invitroSPI

Spliced peptide identification from in vitro digestions of polypeptides with purified proteasomes

Please cite the following publication if you are using invitroSPI for your research:

Roetschke, H.P., Rodriguez-Hernandez, G., Cormican, J.A. et al. InvitroSPI and a large database of proteasome-generated spliced and non-spliced peptides. Sci Data 10, 18 (2023). https://doi.org/10.1038/s41597-022-01890-6

overview

The invitroSPI pipeline consists of six main steps that are implemented in a Snakemake workflow:

1. parsing of search result files and creation of a preliminary MS database (MSDB) containing all peptide-spectrum matches (PSMs), mapping of peptides to their substrate origins and potential product type re-assignment
2. scoring and filtering of PSMs using Mascot's ion score and q-value as well as the delta score described in the manuscript
3. identification and, optionally, removal of synthesis errors using the control runs
4. mapping of peptides to the substrate sequence accounting for potential multi-mappers
5. database statistics and diagnostic information
6. MS2 spectra plotting with annotation of peptide fragment ions.

Additionally, code for the computation of all possible spliced and non-spliced peptides which is used for the Mascot search is provided in SOURCE/_generateSearchDB_polypeptides.R. We also include a script containing useful functions for downstream analyses (invitroSPI_utils.R).

execution

invitroSPI relies on Conda and Snakemake. In order to install Conda, click on this link and follow the installation guidelines for your respective operating system.
After installing Conda, you need to install Snakemake. The Snakemake installation procedure is described here.

Briefly, open the terminal on your computer and paste the following lines sequentially:
conda install -n base -c conda-forge mamba
conda activate base
mamba create -c conda-forge -c bioconda -n snakemake snakemake
Additionally, you might need to run conda update conda.

Download this repository as a .zip file (click on Code at the upper right corner of this repository --> Download ZIP), move the .zip file in the desired directory on your computer and unpack it. Make sure to edit INPUT/sample_list.csv and INPUT/config.yaml before starting the data processing (see below for a more detailed description) Open the terminal in this directory and enter: conda activate snakemake.

The pipeline can be executed by pasting snakemake --use-conda --cores all -R createMSDB into the terminal. The progress of the pipeline execution should appear in your terminal window. In case you have installed an older version of Conda/Snakemake and encounter an error when executing the pipeline, try executing snakemake --use-conda --cores all -R createMSDB --conda-frontend conda.

After your jobs finished, enter conda deactivate in order to terminate your Conda environment.

input

invitroSPI identifies spliced and non-spliced peptides from Mascot search result files. Therefore, the user must provide a table sample_list.csv in the INPUT/ folder containing information about:

• project name
• substrate ID
• substrate sequence
• time point
• search result file name
• replicate
• MSfile (optional)
• metainformation (optional)
• folder with .mgf files

!!! In case you are creating/editing the sample_list.csv file in Microsoft Excel, make sure to save it as actual comma-separated file. I.e., Save as... --> Comma-separated Values (.csv) To check that the sample list is in the correct format, you can open it using a text editor and verify that the columns are separated by commas (and NOT semicolons). !!!

Additionally, the user must provide search result files deposited in the folder INPUT/search_results/ and list their names in the sample_list.csv table.

An example of the sample_list.csv table is given below and can be modified accordingly by the user:

project_name	substrateID	substrateSeq	digestTime	filename	replicate	MSfile	metainformation
test_data	TSN2	TSN2.fasta	4	F029125.csv	1		INPUT/metainformation_testData.csv
test_data	TSN89	RTKAWNRQLYPEW	4	F029129.csv	1		INPUT/metainformation_testData.csv
test_data	TSN2	VSRQLRTKAWNRQLYPEWTEAQR	CTRL	F029123.csv	1		INPUT/metainformation_testData.csv
test_data	TSN89	RTKAWNRQLYPEW	CTRL	F029127.csv	1		INPUT/metainformation_testData.csv

You can either paste the substrate sequence in the sample_list directly or put the name of a single-entry .fasta file containing the substrate sequence. This file should be located in INPUT/sequences.

Note that also the filenames of the control files must be provided. For the control files, put CTRL in the digestTime column. Please provide all other time points in hours (e.g., 0.5 for 30 min).
MSfile is an optional column that might be helpful to keep track of the .raw/.mgf files that were used to generate the respective search result files. It will not be used to create the database.
In case you would like to include additional information in the final database (for instance species, location, instrument, substrate origin, proteasome isotype, ...), you could do so by creating a .csv table containing all this information. The .csv table must also contain a column filename corresponding to the filename in the sample list (see INPUT/metainformation_SciData2022 for an example). Provide the name of the metainformation table in the metainformation column of the sample list.

The invitroSPI workflow is constructed in such a way that the user can keep appending projects and search result files to the sample_list. However, only the samples of the current project (which is specified in INPUT/config.yaml) are processed and stored separately in OUTPUT/project_name subdirectories.

output

The pipeline provides a final database of identified spliced and non-spliced peptides in .csv format (OUTPUT/project_name/ProteasomeDB.csv), as well as all intermediate files in binary format(OUTPUT/project_name/tmp/).

Additionally, some diagnostic plots, and database statistics are being produced which can also be found in the OUTPUT/ folder. They comprise:

• number of unique peptides
• number and frequencies of peptides and PSMs at each time point
• length distribution of peptides, splice reactants, and intervening sequences at each time point
• MS2 spectra plots in .pdf format

parameters that can be modified by the user

We are using the following default parameters that are specified in the INPUT/config.yaml file and that can be changed by the user:

• project_name: indicates which files listed in the sample_list.csv should be processed (enter valid directory names only! - no spaces or German umlauts)
• delta_score: 0.3
• ion_score: 20
• q_value: 0.05
• include_scanNum: "yes"
• keep_synErrors: "no"
• mgf_folder : "absolute_path_to_your_mgf_folder"

Processing of scan numbers is only possible if .mgf files were created with msconvert or Mascot Distiller. In case you provide search results in another format (not recommended), please set include_scanNum to "no". In case you would like to include synthesis errors (labelled as such) in the final ProteasomeDB, change the keep_synErrors flag accordingly.