--- title: "Using drawProteins" author: - name: "Dr Paul Brennan" affiliation: - "Centre for Medical Education, School of Medicine, Cardiff University, Cardiff, Wales, United Kingdom" email: BrennanP@cardiff.ac.uk package: drawProteins date: "`r Sys.Date()`" output: BiocStyle::html_document vignette: > %\VignetteIndexEntry{Using drawProteins} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r load_packages, eval = TRUE, echo=FALSE} library(BiocStyle) library(drawProteins) library(httr) library(ggplot2) library(knitr) opts_chunk$set(comment=NA, fig.align = "center", out.width = "100%", dpi = 100) ``` # 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. # Getting the data from Uniprot Currently, drawProteins interacts with the [Uniprot database]. 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: ```{r download_rel_json, eval=TRUE, echo=TRUE} # accession numbers of rel A drawProteins::get_features("Q04206") -> rel_json ``` # 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. ```{r generate_dataframe} drawProteins::feature_to_dataframe(rel_json) -> rel_data # show in console head(rel_data[1:4]) ``` # 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. ```{r using_draw_canvas, fig.height=3, fig.wide = TRUE} 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`. ```{r using draw_chains, fig.height=3, fig.wide = TRUE} 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`. ```{r using draw_domains, fig.height=3, fig.wide = TRUE} 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: ```{r white_background, fig.height=3, fig.wide = TRUE} # 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 ``` # Checking the other features ```{r show_draw_regions, fig.height=3, fig.wide = TRUE} draw_regions(p, rel_data) # adds activation domain ``` ```{r show_draw_repeat, fig.height=3, fig.wide = TRUE} draw_repeat(p, rel_data) # doesn't add anything in this case ``` ```{r show_draw_motif, fig.height=3, fig.wide = TRUE} draw_motif(p, rel_data) # adds 9aa Transactivation domain & NLS ``` ```{r show_draw_phospho, fig.height=3, fig.wide = TRUE} # add phosphorylation sites from Uniprot draw_phospho(p, rel_data, size = 8) ``` # 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. ```{r relA_workflow, fig.height=3.5, fig.wide = TRUE} 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 ``` ### Adding titles to the plots Using ggplot2 then allows the addition of titles: ```{r add_titles, fig.height=4, fig.wide = TRUE} # add titles rel_subtitle <- paste0("circles = phosphorylation sites\n", "RHD = Rel Homology Domain\nsource:Uniprot") p <- p + labs(title = "Rel A/p65", subtitle = rel_subtitle) p ``` # Drawing schematic for multiple proteins With internet access, the script below shows the workflow for five proteins of the NFkappaB transcription factor family. ```{r five_NFkappaB, fig.height=10, fig.wide = TRUE} # accession numbers of five NF-kappaB proteins prot_data <- drawProteins::get_features("Q04206 Q01201 Q04864 P19838 Q00653") prot_data <- drawProteins::feature_to_dataframe(prot_data) p <- draw_canvas(prot_data) p <- draw_chains(p, prot_data) p <- draw_domains(p, prot_data) p <- draw_repeat(p, prot_data) p <- draw_motif(p, prot_data) p <- draw_phospho(p, prot_data, size = 8) # background and y-axis 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()) # add titles rel_subtitle <- paste0("circles = phosphorylation sites\n", "RHD = Rel Homology Domain\nsource:Uniprot") p <- p + labs(title = "Schematic of human NF-kappaB proteins", subtitle = rel_subtitle) # move legend to top p <- p + theme(legend.position="top") + labs(fill="") p ``` # Customising the draw functions Currently, it's possible to customise the chain colour and outline. It's possible to remove the labels. ```{r customising, fig.height=6, fig.wide = TRUE} data("five_rel_data") p <- draw_canvas(five_rel_data) p <- draw_chains(p, five_rel_data, label_chains = FALSE, fill = "hotpink", outline = "midnightblue") p ``` It's also possible to change the size and colour of the phosphorylation symbols. ```{r custom_phospho, fig.height=8, fig.wide = TRUE} p <- draw_canvas(five_rel_data) p <- draw_chains(p, five_rel_data, fill = "lightsteelblue1", outline = "grey", label_size = 5) p <- draw_phospho(p, five_rel_data, size = 10, fill = "red") p + theme_bw() ``` It's also possible to change the labels to a custom list. But remember that the plots are drawn from the bottom up. ```{r change_labels, fig.height=8, fig.wide = TRUE} p <- draw_canvas(five_rel_data) p <- draw_chains(p, five_rel_data, fill = "lightsteelblue1", outline = "grey", labels = c("p50/p105", "p50/p105", "p52/p100", "p52/p100", "Rel B", "c-Rel", "p65/Rel A"), label_size = 5) p <- draw_phospho(p, five_rel_data, size = 8, fill = "red") p + theme_bw() ``` # Session info Here is the output of `sessionInfo()` on the system on which this document was compiled: ```{r session_Info, echo=FALSE} sessionInfo() ```