Protein-protein interaction predictor
Project description
PySprint
Re-implementation of SPRINT in Rust, a fast, modern programming language, and wrapped in a Python API.
Author
Francois Charih - francoischarih@sce.carleton.ca - Developer
To install PySPRINT
To install PySPRINT as a Python library, you must run the following commands:
$ pip3 install pysprintppi
Note: We support Python3.8+ on the Linux platform.
Using PySPRINT
To predict interactions, you must use the PySPRINT class which is instantiated as follows:
# Instantiate the PySPRINT PPI predictor
pysprint = PySPRINT(
"my_fasta_file_containing_my_sequences.fasta",
"my_hsp_file.hsp", #
"training_pairs_file.txt"
)
# Score the interactions (all-to-all predictions)
prediction_matrix = pysprint.score_all_interactions()
You can then do things with that prediction matrix:
# Retrieve the predictions for a given pair
print(prediction_matrix.get_score_for_pair("protein1", "protein2"))
# Retrieve the scores for all proteins that interact with ProteinX
print(prediction_matrix.get_scores_for_protein("ProteinX"))
# Save the prediction matrix
prediction_matrix.save("path_to_matrix.npy")
# Load a prediction matrix
prediction_matrix = prediction_matrix.load("path_to_matrix.npy")
Running PySPRINT with Docker
$ docker build -t pysprint .
$ docker run -v $(pwd):/pysprint pysprint:latest
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