Software for predicting epitope tag insertion sites in proteins.
Epictope is an R pipeline to identify epitope tag insertion sites for proteins of interest. It uses four features of protein structure; sequence conservation, secondary structure, disordered binding regions, and relative solvent accessabilty to predict suitable internal locations for tag insertion.
This repository contains the code source of the R Epictope package, step-by-step R Markdown and Jupyter notebooks to run the complete workflow, wrapped scripts for simplified workflow executation, and instructions to adjust the weight and effect of each considered feature. The package requires local installations of BLAST, MUSCLE, and DSSP to run (those can b eisntalled as a apckage with Epictope). You will need at elast 3GB of disk space.
Sequence conservation is used to guide internal epitope-tagging approaches. Regions of relatively low conservation are unlikely to be involved in the critical function of the protein. To identify these regions for a protein of interest, we first BLAST the query protein against the proteomes of a diverse set of model organisms. By default, we compare the query sequence against the proteomes of Mus musculus (mouse), Bos taurus (cow), Canis lupus familiaris (dog), Gallus gallus (chicken), Homo sapiens (human), Takifugu rubripes (pufferfish), and Xenopus tropicalis (western clawed frog). Using BLAST, we identify the highest scoring match in each organism, sorted by the lowest E-value. We then align the retrieved sequences with the query protein using MUSCLE, a multiple sequence alignment program, and calculate the shannon entropy at each position. We use Shannon entropy as a simple measure of the calculate the variability of amino acids at each position in the alignment. A lower Shannon entropy indicates low variability, or high sequence conservation at the position, and it should therefore be avoided for tag insertion. Conversly, a high Shannon entropy indicates a relatively low degree of sequence conservation, and potential suitability for tagging.
Relative Solvent Accessibility (RSA) is a measure of the surface area of a folded protein that is accessible to a solvent, typically the cytoplasmic fluid. It is calculated by dividing the solvent accessible surface area (SASA) of an amino acid by the maximum possible solvent accessible surface area for that residue. SASA values are assigned with Define Secondary Structure of Proteins (DSSP). The DSSP program defines secondary structure, geometrical features and solvent exposure of proteins, given atomic coordinates in Protein Data Bank (PDB) format. Values used for the maximum possible solvent accessible surface area were taken from this study. We use the Alphafold2 predicted structure from the European Bioinforamtics Institute (EBI) as the source PDB for DSSP calculations.
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Secondary structure is the local spatial conformation of the polypeptide backbone for the protein of interest. Certain structures, such as alpha helices or beta sheets, are more defined and disruption of these structure is likely to affect protein structure. As with solvent accessibility, we use DSSP to define the secondary structure of the protein from its PDB file. By default, we assign helices (GHI) and sheets (E) feature scores of 0. Hydrogen bonded turns (T), residues in isolated Beta bridges (B), and bends (S) scores of 0.5, and coils scores of 1. For all features, higher values indicate greater suitability for tag insertion.
Disordered binding regions are sections of a protein that do not have a well-defined structure on their own, but can undergo a disorder-to-order transition when they bind to specific protein partners. To avoid these regions, we use ANCHOR2, a tool that analyzes an amino acid sequence and returns a score of intrinsic disorder depending on a model of the estimated energy potential for residue interactions. To maintain consistency with other features, the disordered binding feature score is taken as 1 minus the ANCHOR2 score.
Installing Epictope and its dependencies will require at least 3Gb of disk space. Users should also be familar with using conda, a package manager for macOS/linux and Windows. Conda does not need to be used if users already have access to installations of BLAST, MUSCLE, and DSSP, either locally or on an HPC environment. For users familiar with R, Epictope can be run interactively through an R session or with an IDE such as RStudio.
To calculate the multiple sequence alignment and secondary characteristics, Epictope relies on local installs of BLAST, MUSCLE, and DSSP. These packages can be installed using Conda, an open-source package management system and environment management system that runs on Windows, macOS, and Linux. Conda installers can be found at the Anaconda website. Once installed, you may run the follow commands to install the requisite packages. These commands will create a conda environment named Epictope, and install the requisite packages into that environment.
For macOS/Linux, commands are issued at the terminal. For Windows, it is use the Anaconda Prompt. Detailed instructions for Windows can be found here
Dependencies can be installed using the provided Epictope_environment.yml using the following commands. A BIOLOGIST WON"T KNOW WHAT YOU ARE TALKING ABOUT. AT THE VERY LEAST LINK THE FILE
conda env create --file=Epictope_environment.yml
conda activate Epictope
Alternatively, dependencies can be installed separately by first creating an empty environment, activating the environment, and issuing install commands individually. This can resolve conflict issues that sometimes occur when installing via yml.
conda create -n Epictope
conda activate Epictope
conda install -c bioconda blast
conda install -c bioconda muscle
conda install -c salilab dssp
conda install -c conda-forge r-base
conda install -c conda-forge r-stringi
conda install -c conda-forge r-openssl
We also provide a simple wrapper scripts environment_install.sh for macOS/Linux and environment_install.bat for Windows, which issues the individual conda install commands as a single script.
# for macOS/Linux
chmod +x environment_install.sh
./environment_install.sh
# for Windows
C:\ProgramData\Anaconda3\Scripts\Activate
environment_install.bat
Method 1
conda env create --file=Epictope_environment.yml
conda activate Epictope
Method 2
chmod +x environment_install.sh
./environment_install.sh
Method 1
C:\ProgramData\Anaconda3\Scripts\Activate
conda env create --file=Epictope_environment.yml
conda activate Epictope
Method 2
C:\ProgramData\Anaconda3\Scripts\Activate
environment_install.bat
Epictope is distributed as a R package. You can install it from this github repository using the install_github function from eiher the remotes or devtools R packages. This function is equivalent to the install.packages() function in base R, and should be entered in R through an interactive session or an IDE like RStudio.
# using remotes
if (!requireNamespace("remotes", quietly = TRUE)) {
install.packages("remotes")
}
remotes::install_github("henrichung/epitope_tag")
# using devtools
if (!requireNamespace("devtools", quietly = TRUE)) {
install.packages("devtools")
}
devtools::install_github("henrichung/epitope_tag")
We also provide additional resources outside the main packages, such as scripts to automate conda installation, wrapper scripts for the Epictope workflow, and detailed step-by-step workflows as R Markdown Documents and Jupyter Notebooks. These can be downloaded from this repository through the webpage, or using git. Git is available on both MacOS/Linux and Windows machines
git clone https://github.com/henrichung/epitope_tag
Example workflows with the Epictope package are available in the vignettes folder. Workflows are available as both R Markdown Documents and Jupyter notebooks. These workflows go through the Epictope workflow step by step in an interactive session or an IDE.
Alternatively, the scripts install.R and single_score.R are provided in the scripts folder of this repo to enable one-command operation.
To run, download the install.R and single_score.R scripts from this repository either directly from the github page or using git clone.
- install.R - This script downloads the proteomes for the species used in the multiple sequence alignment. It then converts these sequences in useable files for BLAST. The script checks for and, if not existent, creates data folders in the current working directory to store these files. These file needs to be re-run if the user changes the species considered in the multiple sequence alignment.
- single_score.R - This script takes a UniprotID as input and performs the Epictope workflow for that protein. It retrieves the amino acid sequence and Alphafold2 predicted structure for the protein. It then BLASTs the protein against the proteomes of the animals used in the multiple sequence alignment, retrieves the highest scoring match (score measued by lowest E-value), and aligns the matched proteins along with the query in a multiple sequence alignment. It separately determines the secondary structure, solvent accessibility, and disordered binding regions for the protein, and finally combines all feature scores into a dataframe. The dataframe annotates each residue position with its feature scores and final tagging score. This file is saved to an /outputs folder with the name of the protein followed by '_score.csv'. For example, the protein used in the example below save a "outputs/P57102_score.csv" file.
From the terminal, these scripts can be run as follows.
Rscript install.R
Rscript single_score.R "P57102" # replace 'P57102' with the UniprotID for your protein of interest.
Each script can also be opened in an IDE such as Rstudio, and run interactively line by line.
The scoring function used by Epictope sums the calculated scores for each feature, assigning equal weight to thesecondary structure, disordered binding regions, and solvent accessabilty. Sequence conservation is weighted slightly higher, at 1.5 times the other features. The weight for each feature can be adjusted by the user using a "config.R" file. This file (shorthand for configuration) is used to adjust some of the tuneable parameters in Epictope. The configuration file allows the user to define the species used in the multiple sequence alignment, the values used to score the tag suitability of secondary structures, and the maximum solvent accessibility values to determine solvent accessibility. By default, Epictope will look for a config.R file in the working directory. If a file is not found, it will use default values. The example config.R value in scripts is populated with the default values used by Epictope.
ADD AN EXAMPLE HERE
Epictope is distributed open-source under the GPL3 license.
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