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iScore

A framework and predictor based on support vector machine and random walk graph kernel for scoring protein-protein interfaces.

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242 commitsLast commit ≈ 30 months ago27 stars11 forks

Cite this software

DOI:

10.5281/zenodo.2630566

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.

Logo of iScore
Keywords
Big data
Machine learning
Protein-Protein Interaction
Programming languages
  • Python 90%
  • Cuda 8%
  • C++ 1%
  • Makefile 1%
License
</>Source code

Participating organisations

Natural Sciences & Engineering
Natural Sciences & Engineering
Life Sciences
Life Sciences
Netherlands eScience Center
Utrecht University

Reference papers

Mentions

Contributors

Cunliang Geng
Cunliang Geng
LX
Li Xue
Radboud University Nijmegen, Utrecht University
YJ
Yong Jung
Nicolas Renaud
Nicolas Renaud

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