PPI binding affinity Prediction through deep learning

Published: 19 April 2023| Version 1 | DOI: 10.17632/vsbd5m9f93.1
Contributor:
Pallavi M Shanthappa

Description

A prediction tool for predicting the binding affinities and dissociation constants of protein complexes using a machine learning and deep learning method based on the protein sequence is developed. The steps to run the tool is as follows. The first command set FLASK_APP=app.py sets the environment variable FLASK_APP to app.py, which is the name of the Flask application file. The second command set FLASK_ENV=development sets the environment variable FLASK_ENV to development, which indicates that Flask should run in development mode. The third command flask run -p 3121 explain is running the Flask application by executing the flask run command with the additional argument -p 3121, which specifies that the application should be run on port 3121.the Flask application is running on the local development server at the address http://127.0.0.1:3100. The message also indicates that the server is set to automatically restart when changes are made to the code (* Restarting with stat).

Files

Institutions

Amrita Vishwa Vidyapeetham

Categories

Binding Protein, Protein-Protein Interaction, Bond Dissociation Energy, Molecular Affinity

Licence