Polypeptide Secondary Structure Predictor using Machine Learning in Python
Python Polypeptide Prediction (p-cubed)
Authors: Isaias Ardaya, Minoru Nakano, Gayathri Ravindra, Queenie Tsang
Purpose and Overview of the Project
Purpose of our project is to predict protein secondary structures and visualize the result. Our product is a secondary structure protein prediction program, relying on the support vector machine module for machine learning.
The secondary structures of proteins can fall into 3 general categories: Alpha helix, beta sheet and coil. These secondary structures will largely determine the biological function of the protein. Current sequencing techniques make it so that we can determine the amino acid sequence of proteins at a quicker rate than the respective proteins 3-D structure. This system consists of three modules: StructureExplorer, StructurePrediction and p3editPDB/ProteinViewer.