An app by B. Ferrero-Bordera

pip install alpaca-proteomics

from alpaca_proteomics import alpaca

Stay tuned, the paper is submitted.

A data analysis using Jupyter Notebooks is described here using the dataset published in Ferrero-Bordera et al. 2024. Microbiology Spectrum

Example datasets are available in the following folder.

• Enriched_example.txt: Exoproteome dataset from Ferrero-Bordera et al. 2024. Microbiology Spectrum
• Membrane_example.txt: Membrane proteome dataset from Antelo-Varela et al. 2019. Anal. Chem.

Required packages and last tested working versions:

• pandas 2.1.4
• numpy 1.24.3
• matplotlib 3.8.1
• scipy 1.11.3
• scikit-learn 1.4.1.post1
• sklearn 1.0.1
• seaborn 0.11.2
• thefuzz 0.20.0

• Protein Groups file containing the protein Groups and the quantified intensities as .csv, .txt or .xlsx.
• Quantification Standards file as .csv, .txt or .xlsx. It requires 3 columns for a proper execution (Accession, MW (kDa) & StdConcentration (µg/µl). See Quantification for more details.
• Enrichment Standards (Optional) file as .csv, .txt or .xlsx. It requires 3 columns for a proper execution (Accession, MW (kDa) & Mix concentration (µg/µl). See Enrichment for more details.

Experimental details (in our example params.txt) can be added as txt, csv or xlsx formats. This file can include the columns described in the following table:

Table 1. Experimental parameters table. This example covers all possible columns. Nonetheless, not all columns are necessary. For example, Enrichment columns (EnrichmentDirection, StdDilution, StdVolume) are only used if any enrichment step was performed. More information about this is described in the Enrichment section.

• Condition: Condition in which
• SampleVolume: Protein extract volume (µL) used for protein digestion.
• ProteinConcentration: Determined protein concentration (µg/µl) in the sample.
• AmountMS: Protein amount (µg) injected in the MS.
• CellsPerML: Measured cells per mL of culture.
• TotalCultureVolume: Total cultivation volume (µL).
• ProteinSRM (Optional): If the enrichment of a subcellular fraction has been calculated using targeted proteomics (SRM). This corresponds to the accession of measured protein in SRM to calculate the enrichment.
• fmolSRM (Optional): If the enrichment of a subcellular fraction has been calculated using targeted proteomics (SRM). Fmol of the proteins measured in the targeted proteomics measurements.
• Enrichment Optional: Boolean (True or False). Samples that have been enriched should be specified as True
• EnrichmentDirection Optional: Amplification or Sampling (see Supplementary Material).
• StdDilution (Optional): This parameter specifies how many times the stock solution of enrichment standards has been diluted before adding it to the sample. If the standards were not diluted before addition, specify 1. Only used when the enrichment is calculated through the function alpaca.gathers() details of the preparation of the used proteins should be added.
• StdVolume (Optional): Volume of enrichment standards (µL) added to the sample. Only used in case the enrichment is calculated through the function alpaca.gathers() details of the preparation of the used proteins should be added.

Functions for data import, cleaning and pre-processing.

alpaca.eats(File): this function is meant to offer flexibility on the ProteinGroup file importation as some scientists could have the data in a .txt, .csv or .xlsx.

alpaca.spits(DataFrame): this function aims to give coherence to the imported data, as it could be that MaxQuant output organisation is changed by the user or another software like Perseus. It returns our formatted DataFrame, and 2 lists: columns which contain all df.columns after formatting, and default which is a list with all suggested columns for dataframe slicing.

Absolute quantification using Alpaca is optimised for label-free methods, relying on the addition of a set of anchor proteins at a known amount.

Table 2. Format for the file describing the stock solution of anchor proteins.

alpaca.census(): calculates the abundances in moles based on added anchor proteins (Table 2). Anchor proteins should be specified in a dataframe using the format described in Table 2.

In case the study focuses on a fraction of the proteome (e.g., membrane proteome or exoproteome), it is likely that during the sample preparation and enrichment step was performed. This module allows to connect the enrichment step to the data based on how the samples were prepared.

Enrichment factors are calculated based on the fmol quantified in the enriched sample to the raw or non-enriched sample:

$$ ER = \frac{fmol_{enriched}}{fmol_{non-enriched}} $$

For that purpose, 2 strategies are currently covered under our pipeline:

1. The quantification of specific proteins of the analysed fraction on both before and after the enrichment step using Targeted MS (SRM).

This strategy was described by Antelo-Varela et al. 2019 and relies on using external protocols (e.g., Skyline) to quantify the enrichment step. Enrichment factors can be added to the parameters table under the column Enrichment_Factor. Additionally, the SRM quantified amount for a given protein can be added on the columns ProteinSRM (Accession of the quantified protein) and fmolSRM (Quantified fmol in the analysed proteome fraction).

2. The addition of whole proteins at a known concentration before performing the enrichment step.

This approach was described by Ferrero-Bordera et al. 2024 and requires a protein mixture at a known concentration added before the enrichment step. Used standards have to be formatted as specified in the table below:

Table 3. Enrichment standards

alpaca.gathers(): calculates the enrichment factors for each specified fraction based on the sample preparation (Table 1) and the added standards to the sample. Standards should be specified in a dataframe using the format described in Table 3.

This module connects the protein amounts quantified in the sample and the sample preparation. Thus, allowing the calculation of protein amounts to the original state (e.g. bacterial culture, raw culture supernatant). This step brings deeper insights to the user based on the known experimental parameters, yielding highly valuable data (e.g., molecules per cell, fmol / µmol of protein extract)

alpaca.wool(): this function integrates the sample preparation details with the quantified proteins injected in the MS.