A framework and predictor based on support vector machine and random walk graph kernel for scoring protein-protein interfaces.
iScore offers simple solutions to classify protein-protein interfaces using a support vector machine approach on graph kernels. The simplest way to use iScore is through dedicated binaries that hide the complexity of the approach and allows access to the code with simple command line interfaces. The two binaries are iscore.train and iscore.predict that respectively train a model using a trainging set and use this model to predict the near-native character of unkown conformations.
Scoring 3D protein-protein interaction models using deep learning
Fast and versatile Python package that leverages SQL queries to parse, manipulate and process biomolecular structure files. The structure files should be in the PDB format and are available on www.rcsb.org.
Generates consistent PSSM and PDB files for protein-protein complexes