Read the paper here
ALLO is a tool for dicriminating and prioritizing allosteric pockets. The tool comprises two methods: naive bayes (NB) and artificial neural networks (ANN). The former is used to classify a pocket as allosteric or orthosteric and the later is used to rank allosteric pockets from a set of pockets. The main program along with some helper scripts can be found in the directory src. Whereas data can be found in the directory data.
Python libraries: Numpy, scipy Output file from the program DoGSiteScorer.
An implementation of a naive bayes model.
labels input as allosteric (A) or orthostreic (O) Uses NB model (nb.py) Uses ao.tsv Usage: python predict_nb.py input_file.txt
example:
In your terminal, navigate to the directory src and execute the following command:
python predict_nb.py test_input/A_ASD0023_2_1N5M_1_desc.txt
the output file is written in the same directory as the input file.
An implementation of a neural network model.
ranks a set of pockets based on their allostery Uses ANN model (nn.py) Uses aplc.tsv Usage: python rank_nn.py input_file.txt
example:
In your terminal, navigate to the directory src and execute the following command:
python rank_nn.py test_input/AS091022202_3PJG_complex.txt
the output file is written in the same directory as the input file.