Contents

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1 Overview of drawProteins

This package has been created to allow the creation of protein schematics based on the data obtained from the Uniprot Protein Database.

The basic workflow is: 1. to provide one or more Uniprot IDs 2. get a list of feature from the Uniprot API 3. draw the basic chains of these proteins 4. add features as desired

drawProteins uses the package httr to interact with the Uniprot API and extract a JSON object into R. The JSON object is used to create a data.table.

The graphing package ggplot2 is then used to create the protein schematic.

2 Getting the data from Uniprot

Currently, drawProteins interacts with the [Uniprot database]http://www.uniprot.org/. At least one working Uniprot accession numbers must be provided. More than one can be provided but they must be separated by a single space. The spaces are replaced to create an url that can be used to query the Uniprot API

The get_features() function uses the Uniprot API to return the features of a protein - the chain, domain information and other annotated features such as “repeats” and “motifs”. Post-translational modifications, such as phosphorylations, are also provided.

The httr::content() function is then used to extract the content. From the get_features() function, this will provide lists of lists. The length of the parent lists corresponds to the number of accession numbers provided. Interestingly, the order sometimes appears different to that provided. Each of lists inside the parent list are a list of six - one for each protein - that contains names of the proteins and the features.

As an example, we will retrieve the details of a protein called Rel A or NF-kappaB, p65, a well studied transcription factor.

With internet access, this can be retreived from Uniprot with this code:

# accession numbers of rel A
    drawProteins::get_features("Q04206") ->
    rel_json
[1] "Download has worked"

3 Turning Uniprot data into a dataframe

The next step in the workflow is to convert the data from the Uniprot API into a dataframe that can be used with ggplot2.

The feature_to_dataframe() function will convert the list of lists of six provided by the get_features() function to a dataframe which can then be used to plot the schematics.

The feature_to_dataframe() function will also add an “order” value to allow plotting. The order goes from the bottom in the manner of a graph.

drawProteins::feature_to_dataframe(rel_json) -> rel_data

# show in console
head(rel_data[1:4])
                 type                         description begin end
featuresTemp    CHAIN            Transcription factor p65     1 551
featuresTemp.1 DOMAIN                                 RHD    19 306
featuresTemp.2 REGION Transcriptional activation domain 3   342 389
featuresTemp.3 REGION Transcriptional activation domain 1   415 476
featuresTemp.4 REGION Transcriptional activation domain 2   520 551
featuresTemp.5  MOTIF         Nuclear localization signal   301 304

4 Draw the protein chains and domains

The data can be plotted with ggplot2 using the geom_rect() and geom_label. The first step is to make canvas with draw_canvas which is based on the longest protein that is being drawin. This can be done using a pipe in the following way.

draw_canvas(rel_data) -> p
p

Then we can plot the protein chain. We use the draw_chain() function to which we have to provide the ggplot object p and the data which is called rel_data.

p <- draw_chains(p, rel_data)
p

Now, we add the domains which are drawn to scale in terms of their lengths. We use the draw_domains() function to which we have to provide the ggplot object p and the data which is called rel_data. The default is to label the chains. The labels can be removed using the argument label_chains = FALSE.

p <- draw_domains(p, rel_data)
p

To show this visualisation better, a white background helps as well as removing the y-axis and the grid. Also changing the size of the text using the base_size argument. This can be done with this code:

# white background and remove y-axis
p <- p + theme_bw(base_size = 20) + # white background
    theme(panel.grid.minor=element_blank(), 
        panel.grid.major=element_blank()) +
    theme(axis.ticks = element_blank(), 
        axis.text.y = element_blank()) +
    theme(panel.border = element_blank())
p

5 Checking the other features

draw_regions(p, rel_data) # adds activation domain

draw_repeat(p, rel_data) # doesn't add anything in this case

draw_motif(p, rel_data) # adds 9aa Transactivation domain & NLS

# add phosphorylation sites from Uniprot
draw_phospho(p, rel_data, size = 8)

6 Putting it all together

In this way it’s possible to chose the geoms that give the information desired in the way you like. Some customisation is possible as described below.

For Rel A, my recommendation would be the following workflow.

draw_canvas(rel_data) -> p
p <- draw_chains(p, rel_data)
p <- draw_domains(p, rel_data)
p <- draw_regions(p, rel_data)
p <- draw_motif(p, rel_data)
p <- draw_phospho(p, rel_data, size = 8) 

p <- p + theme_bw(base_size = 20) + # white backgnd & change text size
    theme(panel.grid.minor=element_blank(), 
        panel.grid.major=element_blank()) +
    theme(axis.ticks = element_blank(), 
        axis.text.y = element_blank()) +
    theme(panel.border = element_blank())
p