Update README.md

README.md

@@ -1,50 +1,21 @@
+[![unittests](https://github.com/haddocking/prodigy-cryst/actions/workflows/unittests.yml/badge.svg)](https://github.com/haddocking/prodigy-cryst/actions/workflows/unittests.yml)
 [![codecov](https://codecov.io/gh/haddocking/prodigy-cryst/branch/master/graph/badge.svg?token=KCGiAqKRnu)](https://codecov.io/gh/haddocking/prodigy-cryst)
 
-# Interface Classifier
+# PRODIGY-cryst
 
-Collection of scripts to predict whether an interface in a protein-protein
-complex is biological or crystallographic from its atomic coordinates.
+Collection of scripts to predict whether an interface in a protein-protein complex is biological or crystallographic from its atomic coordinates.
 
-## Quick & Dirty Installation
+## Installation
 
 ```bash
-git clone http://github.com/biopython/biopython.git
-cd biopython
-sudo python setup.py install # Alternatively, install locally but fix $PYTHONPATH
-
-wget https://github.com/mittinatten/freesasa/releases/download/1.0/freesasa-1.0.tar.gz
-tar -xzvf freesasa-1.0.tar.gz
-cd freesasa-1.0
-./configure && make && make install
-
-pip3 install scikit-learn==0.22
-
-git clone http://github.com/haddocking/interface-classifier
-
-# Edit the config.py to setup the paths to the freesasa binary and radii files
-
-# Have fun!
+> git clone http://github.com/haddocking/prodigy-cryst
+> python setup.py install
 ```
 
 ## Usage
 
 ```bash
-python interface_classifier.py <pdb file> [--selection <chain1><chain2>]
+prodigy_cryst <pdb file> [--selection <chain1><chain2>]
 ```
 
-Type --help to get a list of all the possible options of the script.
-
-## Dependencies
-
-- The scripts rely on [Biopython](www.biopython.org) to validate the PDB structures and calculate interatomic distances.
-- [freesasa](https://github.com/mittinatten/freesasa), with the parameter set used in NACCESS ([Chothia,1976](http://www.ncbi.nlm.nih.gov/pubmed/994183)), is also required for calculating the buried surface area. Both 2.x and 1.x version series are supported.
-- [scikit-learn](https://github.com/scikit-learn/scikit-learn) for Python 3 is necessary to load and use the classifier.
-
-To install and use the scripts, just clone the git repository or download the tarball zip
-archive. Make sure `freesasa`, Biopython and scikit-learn are accessible to the Python scripts
-through the appropriate environment variables ($PYTHONPATH).
-
-## License
-
-These utilities are open-source and licensed under the Apache License 2.0. For more information
-read the LICENSE file.
+Type --help to get a list of all the possible options.