iScore

A framework and predictor based on support vector machine and random walk graph kernel for scoring protein-protein interfaces. Tutorials are available at iscoredoc.readthedocs.io.

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233 commits | Last update: March 24, 2020

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What iScore can do for you

  • Provides a state-of-the-art scoring predictor trained on the data of Docking Benchmark 5 (https://zlab.umassmed.edu/benchmark/)
  • Provides easy-to-use interface to train a new scoring predictor
  • Easily extended with more different and various features of protein interface
  • Active and competitive player in the CAPRI challenge for 3D structure predictions of protein complexes (http://www.capri-docking.org)

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.

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Tags
  • Machine learning
  • Big data
Programming Language
  • Python
License
  • Apache-2.0
Source code

Participating organizations

Mentions

1 Journal article

Projects with iScore

Contributors

  • Cunliang Geng
    Netherlands eScience Center
  • Nicolas Renaud
    Netherlands eScience Center
  • Yong Jung
  • Li Xue
    Utrecht University
Contact person
Nicolas Renaud
Netherlands eScience Center
Mail Nicolas