Introduction to TPP2D for 2D-TPP analysis
Abstract
Thermal proteome profiling (TPP) (Mateus et al., 2020; Savitski et al., 2014) is an unbiased mass spectrometry-based method to assess protein-ligand interactions. It works by employing the cellular thermal shift assay (CETSA) (Molina et al., 2013) on a proteome-wide scale which monitors the profiles of proteins in cells over a temperature gradient and aims to detect shifts induced by ligand-protein interactions. 2D-TPP represents a refined version of the assay (Becher et al., 2016) which uses a concentration gradient of the ligand of interest over a temperature gradient. This package aims to analyze data retrieved from 2D-TPP experiments by a functional analysis approach.
General information
This package implements a method to detect ligand-protein
interactions from datasets obtained with the 2D-TPP assay. Please note
that methods for analyzing convential TPP datasets (e.g. single dose,
melting curve approach) can be found at: TPP
and NPARC
.
This vignette is not aiming to give an in-depth introduction to thermal proteome profiling, please refer to other sources for this purpose:
Note: if you use TPP2D in published
research, please cite:
Kurzawa, N.*, Becher, I.* et al. (2020) A computational method for detection of ligand-binding proteins from dose range thermal proteome profiles. Nat. Commun., 10.1038/s41467-020-19529-8
Installation
- Download the package from Bioconductor.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("TPP2D")Or install the development version of the package from Github.
- Load the package into R session.
Introduction
The 2D-TPP assay is usually set up in a way that for each temperature
different ligand concentrations (including a vehicle condition) are used
and two adjacent temperatures each are multiplexed in a single mass
spectrometry (MS) run. Typically up to 10 or 12 temperatures are used in
total that add up to 5 or 6 MS runs respectively (Figure 1).
This package aims to provide a tool for finding ligand-protein
interactions, i.e. proteins affected in their thermal stability by the
treatment used in the experiment) at a given false disscovery rate
(FDR). Please note that a change in thermal stability of a protein is
not a guarantee for it interacting with the molecule used as treatment.
However, we try to give the user additional information by specifying
whether an observed effect is likely due to stabilization or a change in
expression or solubility of a given protein to make the interpretation
of detected hits as easy as possible.
Step-by-step workflow
After having loaded dplyr and the TPP2D
package itself we start by loading an example dataset which is supplied
with the package. Therefore, we use the import2dDataset
function.
For this puporse we need to supply a config table that essentially
describes which experimental conditions the different TMT labels used
correspond to and supplies paths to the raw data files (note: since this
example dataset is included in the package it does not contain a “Path”
column, this is however mandatory if the data should be read in from
external raw files).
## Compound Experiment Temperature 126 127L 127H 128L 128H 129L 129H 130L 130H
## 1 Compound1 exp1 37.0 0 0.5 2 10 40 - - - -
## 2 Compound1 exp1 37.8 - - - - - 0 0.5 2 10
## 3 Compound1 exp2 40.4 0 0.5 2 10 40 - - - -
## 4 Compound1 exp2 44.0 - - - - - 0 0.5 2 10
## 5 Compound1 exp3 46.9 0 0.5 2 10 40 - - - -
## 6 Compound1 exp3 49.8 - - - - - 0 0.5 2 10
## 7 Compound1 exp4 52.9 0 0.5 2 10 40 - - - -
## 8 Compound1 exp4 55.5 - - - - - 0 0.5 2 10
## 9 Compound1 exp5 58.6 0 0.5 2 10 40 - - - -
## 10 Compound1 exp5 62.0 - - - - - 0 0.5 2 10
## 11 Compound1 exp6 65.4 0 0.5 2 10 40 - - - -
## 12 Compound1 exp6 66.3 - - - - - 0 0.5 2 10
## 131L RefCol
## 1 - 126
## 2 40 129L
## 3 - 126
## 4 40 129L
## 5 - 126
## 6 40 129L
## 7 - 126
## 8 40 129L
## 9 - 126
## 10 40 129L
## 11 - 126
## 12 40 129LWe then call the import function (note: we here supply a list of data frames for the “data” argument, replacing the raw data files that would be normally specified in the above mentioned column of the config table. If this is supplied the argument “data” can simply be ignored):
import_df <- import2dDataset(
configTable = config_tab,
data = raw_dat_list,
idVar = "protein_id",
intensityStr = "signal_sum_",
fcStr = "rel_fc_",
nonZeroCols = "qusm",
geneNameVar = "gene_name",
addCol = NULL,
qualColName = "qupm",
naStrs = c("NA", "n/d", "NaN"),
concFactor = 1e6,
medianNormalizeFC = TRUE,
filterContaminants = TRUE)## The following valid label columns were detected:
## 126, 127L, 127H, 128L, 128H, 129L, 129H, 130L, 130H, 131L.## Importing 2D-TPP dataset: exp1## Removing duplicate identifiers using quality column 'qupm'...## 20 out of 20 rows kept for further analysis.## Importing 2D-TPP dataset: exp1## Removing duplicate identifiers using quality column 'qupm'...## 20 out of 20 rows kept for further analysis.## Importing 2D-TPP dataset: exp2## Removing duplicate identifiers using quality column 'qupm'...## 20 out of 20 rows kept for further analysis.## Importing 2D-TPP dataset: exp2## Removing duplicate identifiers using quality column 'qupm'...## 20 out of 20 rows kept for further analysis.## Importing 2D-TPP dataset: exp3## Removing duplicate identifiers using quality column 'qupm'...## 15 out of 15 rows kept for further analysis.## Importing 2D-TPP dataset: exp3## Removing duplicate identifiers using quality column 'qupm'...## 15 out of 15 rows kept for further analysis.## Importing 2D-TPP dataset: exp4## Removing duplicate identifiers using quality column 'qupm'...## 14 out of 14 rows kept for further analysis.## Importing 2D-TPP dataset: exp4## Removing duplicate identifiers using quality column 'qupm'...## 14 out of 14 rows kept for further analysis.## Importing 2D-TPP dataset: exp5## Removing duplicate identifiers using quality column 'qupm'...## 9 out of 9 rows kept for further analysis.## Importing 2D-TPP dataset: exp5## Removing duplicate identifiers using quality column 'qupm'...## 9 out of 9 rows kept for further analysis.## Importing 2D-TPP dataset: exp6## Removing duplicate identifiers using quality column 'qupm'...## 4 out of 4 rows kept for further analysis.## Importing 2D-TPP dataset: exp6## Removing duplicate identifiers using quality column 'qupm'...## 4 out of 4 rows kept for further analysis.## Ratios were correctly computed!## Median normalizing fold changes...recomp_sig_df <- recomputeSignalFromRatios(import_df)
# resolve ambiguous protein names
preproc_df <- resolveAmbiguousProteinNames(recomp_sig_df)
# alternatively one could choose to run
# preproc_df <- resolveAmbiguousProteinNames(
# recomp_sig_df, includeIsoforms = TRUE)Please refer to the help page of the function to retrieve in-depth
description of the different arguments. Essentially the function needs
to know the names or prefixes of the columns in the raw data files, that
contain different informations like protein id or the raw or relative
signal intensities measured for the different TMT labels.
The imported synthetic dataset consists of 17 simulated protein 2D
thermal profiles (protein1-17) and 3 spiked-in true positives (tp1-3).
It represents a data frame with the columns:
| column | description | required |
|---|---|---|
| representative | protein identifier | Yes |
| qupm | number of unique quantified peptides | No |
| qusm | number of unique spectra | No |
| clustername | gene name | Yes |
| temperature | temperature incubated at | Yes |
| experiment | experiment identifier | No |
| label | TMT label | No |
| RefCol | RefCol | No |
| conc | treatment concentration | No |
| raw_value | raw reporter ion intensity sum | No |
| raw_rel_value | raw relative fold change compared to vehicle condition at the same temperature | No |
| log_conc | log10 treatment concentration | Yes |
| rel_value | median normalized fold change | No |
| value | recomputed reporter ion intensity | No |
| log2_value | recomputed log2 reporter ion intensity | Yes |
Here the column “required” indicates which of these columns is neccessary for usage of the downstream functions.
We then begin our actual data analysis by fitting two nested models to each protein profile: A null model that is expected when a protein profile remains unaffected by a given treatment and an alternative model which is a constrained sigmoidal dose-response model across all temperatures.
The goodness of fit of both models for each protein is then compared and a F-statistic is computed.
Then we create a null model using our dataset to be able to estimate the FDR for a given F-statistic in the next step.
set.seed(12, kind = "L'Ecuyer-CMRG")
null_model <- bootstrapNullAlternativeModel(
df = preproc_df,
params_df = model_params_df,
B = 2)## [1] "Warning: You have specificed B < 20, it is recommended to use at least B = 20 in order to obtain reliable results."
## | | | 0% | |==== | 5% | |======= | 10% | |========== | 15% | |============== | 20% | |================== | 25% | |===================== | 30% | |======================== | 35% | |============================ | 40% | |================================ | 45% | |=================================== | 50% | |====================================== | 55% | |========================================== | 60% | |============================================== | 65% | |================================================= | 70% | |==================================================== | 75% | |======================================================== | 80% | |============================================================ | 85% | |=============================================================== | 90% | |================================================================== | 95% | |======================================================================| 100%Please note that setting B = 2 (corresponsing to 2 permutations) is not enough to guarantee faithful FDR estimation, this has simply been set for fast demonstration purposes. We recommend to use at least B = 20 for applications in praxis.
To estimate the FDR for all given F-statistics and retrieve all significant hits at a set FDR α we use the following functions:
fdr_tab <- getFDR(
df_out = fstat_df,
df_null = null_model)
hits <- findHits(
fdr_df = fdr_tab,
alpha = 0.1)
hits %>%
dplyr::select(clustername, nObs, F_statistic, FDR)## # A tibble: 8 × 4
## clustername nObs F_statistic FDR
## <chr> <int> <dbl> <dbl>
## 1 tp2 30 53.9 0
## 2 tp1 20 46.2 0
## 3 tp3 50 31.4 0
## 4 protein10 50 2.35 0
## 5 protein15 50 1.91 0
## 6 protein16 40 1.34 0
## 7 protein1 20 1.08 0
## 8 protein11 20 0.531 0We can now plot our obtained result as a (one-sided) volcano plot:
Finally, we can fit and plot proteins that have come up as significant in our analysis by using:
or respectively for the H1 model:
An alternative option to visualize thermal profiles of proteins of
interest is to use plot2dTppFcHeatmap:
Acknowledgements
We thank the following people for valuable feedback and help with the
TPP2D package:
- André Mateus
- Constantin Ahlmann-Eltze
- Alexander Helmboldt
- Stefan Gade
- Dorothee Childs
- Nikos Ignatiadis
- Britta Velten
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