A package to predict emulsifying potential of peptides
Prediction of Emulsifying Peptides, based on protein sequences (in fasta format) and their corresponding results from NetSurfP-2 (http://www.cbs.dtu.dk/services/NetSurfP/). The NetSurfP-2 file should be in the NetSurfP-1 Format (retrieved when clicking 'Export All' in the upper right side of NetSurfP's 'Server Output' window).
Prerequisites and installation
The package can either be cloned from github and installed locally or installed with pip. In both cases, python-3.6 or higher needs to be installed on your PC. Additionally, it is recommended to install the package in a new environment.
The following commands are run in the command line.
1: Set up a new environment.
python3 -m venv EmulsiPred_env
2: Enter (activate) the environment.
3a: Install EmulsiPred within the activated environment with pip.
pip install EmulsiPred
3b: Install EmulsiPred by installing from github with pip.
pip install "git+https://github.com/MarcatiliLab/EmulsiPred.git"
After either running 3a or 3b EmulsiPred is installed within the activated environment (in our case EmulsiPred_env).
After installation, EmulsiPred can be run from the terminal or within a python script.
As mentioned above, EmulsiPred requires 2 inputs.
- A fasta file containing the protein sequences to check for emulsifiers (termed sequence.fsa).
- A NetSurfP file containing secondary structure information of the sequences in sequence.fsa (termed netsurfp.txt)
Additionally, there are also 3 variable parameters.
- o (out_dir): Output directory (default is the current directory).
- nr_seq: Results will only include peptides present in this number of sequences or higher (default 1).
- ls (lower score): Results will only include peptides with a score higher than this score (default 2).
EmulsiPred can be run directly in the terminal with the following command.
python -m EmulsiPred -s path/to/sequence.fsa -n path/to/netsurfp.txt -o path/to/out_dir --nr_seq 1 --ls 2
Furthermore, it can be imported and run in a python script.
import EmulsiPred as ep ep.EmulsiPred(sequences='path/to/sequence.fsa', netsurfp_results='path/to/netsurfp.txt', out_dir='path/to/out_dir', nr_seq=1, lower_score=2)
Interpretation of predictions
The predicted values are a relative ordering of the peptides by chance of being an emulsifier. In other words, a higher score implies a higher chance of being an emulsifier.
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