DOSCHEDA: a web application for interactive chemoproteomics data analysis

Data input

The application is designed to take three different types of data:

1. Peptide Intensities. These are obtained from Proteome Discoverer, MaxQuant or similar software. The same procedure applied in Proteome Discoverer 2.1 has been implemented in DOSCHEDA for summing the reporter ions to protein relative quantification. The protein fold changes [Ctrl]/[treated] are then converted to log2 scale and then passed into the pipeline.

2. Fold Changes. These are the protein fold changes, [Ctrl]/[treated].

3. Log2 Fold Changes. These are the log2 protein fold changes.

DOSCHEDA has been optimized for Proteome Discoverer 2.1 (PD 2.1), but it can also use data from other software given that the input file contains specific columns as described in Table 1.

Statistical analyses

The experimental design as shown in Table 2 will determine the type of analysis that can be performed in DOSCHEDA. The standard pipeline utilizes a linear model with a quadratic form ${\displaystyle f\left(x\right)=a_0+a_{1}x+a_2{x}^2}$ where a₀ is the intercept, a₁ the slope and a₂ the quadratic coefficient. This pipeline is suitable for experimental designs with less than 5 channels (4-plex) and more than 1 replicate (Table 2). Increasing free drug concentrations and generation of a dose-response relationship with the protein target(s) provides information about specific drug-target interactions. The linear model analysis implemented in DOSCHEDA by using the limma R package (Smyth, 2004) will identify proteins with a significant p value (slope, intercept, quadratic) as the protein drug-target(s).

Alternatively, in cases where biological input material is not a limiting factor, chemoproteomics experiments consist of a full-scale dose-response, in which 5 or more concentrations are tested (e.g., 6-plex or 10-plex). In this case, the user can run the sigmoidal pipeline where the four-parameter log- logistic (Ritz et al., 2015) model is applied (Table 2). Here, the p values of the model parameters (min, max, slope, RB₅₀) will be computed to rank proteins based on their selectivity profile (Smyth & Collins, 2009). The half maximal residual binding (RB₅₀) is a measure of the effectiveness of a drug in binding to a protein. Thus, this quantitative measure indicates how much of a drug or small molecule is needed to saturate binding to a protein by half, and can be used to compare drug-protein profiles. The RB₅₀ values are typically expressed as molar concentration and are computed in the sigmoidal pipeline for each protein within DOSCHEDA. Furthermore, the corrected RB₅₀, according to Daub (2015) corresponds to the ratio (r) of the proteins enriched in the second incubation (supernatant) versus those retained in the first incubation (DMSO or blank) with the affinity matrix. This pulldown of pulldown or depletion factor (r) enables the calculation of a K_d for each protein and it will be part of DOSCHEDA’s outputs.

Peptide removal process using Pearson correlation

When peptide intensities and two replicates are available, DOSCHEDA implements a prior step to the peptide aggregation process to consider of potential technical experimental errors.

The peptide removal function is tailored having in mind the typical chemoproteomics experimental design and aims to leverage the empirical peptides information of experimental replicates avoiding data imputation or using spectra features of peptides (Fischer & Renard, 2016) as they might not be necessarily available information to the final user.

The peptide removal algorithm is described in Fig. 1, in this procedure peptides that are inconsistent (e.g., anti-correlate) between replicates are excluded. This implementation is not intended to address the ratio compression (Savitski et al., 2013) but to leverage the information available on the same peptide between the two replicates.

In fact, the peptide removal process is based on the calculation of the Pearson correlation coefficient between the same peptide abundances of two replicates as a pre-filtering step before summing the remaining peptide intensities to finally infer back the protein abundance.

Initially, the peptides are filtered by their Peptide Quality score (Q_s <  0.05) column, a mandatory input column in this case. Next, data are filtered to have a minimum of 2 peptides (shared or unique) per protein and per replicate such that the Pearson correlation coefficient (R²) can be calculated between matched peptides. Peptides with R² <  0.4 are removed and summed intensities are computed from the remaining peptides; although this cut-off can be modified by the user within DOSCHEDA, we observed that 0.4 is a reasonable default threshold that removes extreme peptide quantifications (e.g., low correlated and anti-correlated peptides) and keeps almost unchanged the proteome coverage, as shown in Fig. 2 R² <  0.4. Intuitively, larger R² values will reflect into a larger peptide removal and consequently proteins loss as shown by Figs. 2A–2D.

The R² can be adjusted by the user within DOSCHEDA allowing quick results comparison in a similar fashion of Fig. 2 and, empowering the analyst to fine tuning this threshold based on project aims.

Finally, only proteins with more than one unique peptide are retained for the downstream analysis. Table 3 summarizes all the possible scenarios handled by the peptide removal function.

The user should initially run the exploratory data analysis in DOSCHEDA up to the Principal Component Analysis (PCA) and the correlogram plot to evaluate the correlation between replicates. Only at this point the user can make an informed decision whether to apply the peptide removal function or continue to analyze the data without applying the removal process. Yet, being an open source tool the peptide removal process could be replaced by any other similar function that might be available in literature (see the DOSCHEDA Bioconductor vignette).

Additional Features. The server version of DOSCHEDA comprises additional uploads that are useful when researchers are comparing datasets, including: (i) intersections of the enriched proteins with a user uploaded GeneID list (e.g., protein kinesis); (ii) default mapping from Uniprot accession to GeneIDs using InterMine or in case the organism of interest is not present in the drop-down list, DOSCHEDA allows the user to specify the mapping file.

Furthermore, DOSCHEDA allows: (i) two normalization/scaling options (see DOSCHEDA manual); (ii) interactive 2D plots with user-defined x- and y- axes; (iii) quality control (QC) traffic lights flags; (iv) downloadable results and detailed report of the analyses.

Availability and requirements

DOSCHEDA server lives at https://doscheda.shinyapps.io/doscheda/ and it will be constantly maintained by the authors; the users by following the link above can upload and analyze their own datasets without additional requirements. Finally, to facilitate traceability and reproducibility DOSCHEDA is also available as an open source Bioconductor package.