A package to predict emulsifying potential of peptides

# EmulsiPred

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.

    source EmulsiPred_env/bin/activate


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).

#### Running EmulsiPred

After installation, EmulsiPred can be run from the terminal or within a python script.

As mentioned above, EmulsiPred requires 2 inputs.

1. A fasta file containing the protein sequences to check for emulsifiers (termed sequence.fsa).
2. A NetSurfP file containing secondary structure information of the sequences in sequence.fsa (termed netsurfp.txt)

Additionally, there are also 3 variable parameters.

1. o (out_dir): Output directory (default is the current directory).
2. nr_seq: Results will only include peptides present in this number of sequences or higher (default 1).
3. 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.

## Project details

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