Package: iScore
iScoreSupport Vector Machine on Graph Kernel for Ranking Protein-Protein Docking Models1. InstallationMinimal information to install the moduleCheck if commandmpiexecis available or not in your console. If not, download and installopenmpiormpich.Install iScore usingpip install iScorePossible problems:Ifpip install iScoregives problems on installingmpi4py, try to first installmpi4pyusingconda install mpi4pyand thenpip install iScore.2. DocumentationThe documentation of the package can be found at:https://iscoredoc.readthedocs.io3. Quick ExamplesiScore 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 areiscore.trainandiscore.predict(iscore.train.mpiandiscore.predict.mpifor parallel running) that respectively train a model using a training set and use this model to rank the docking models of a protein-protein complex.Requirements for preparing data:Use the following file structureroot/
|__train/
|    |__ pdb/
|    |__ pssm/
|    |__ class.lst
|__test/
      |__pdb/
      |__pssm/
      |__ class.lst (optional)Thepdbfolder contains the PDB files of docking models, andpssmcontains the PSSM files. Theclass.lstis a list of class ID and PDB file name for each docking model, like0 7CEI_10w.Check the package subfolders `example/train` and `example/test` to see how to prepare your files.PDB files and PSSM files must have consistent sequences.PSSMGencan be used to get consistent PSSM and PDB files. It is already installed along with iScore. CheckREADMEto see how to use it.Example 1. Use our trained modelYou can directly use our trained model to score your docking conformations. The model we provide is trained on docking benchmark version 4 (BM4) data, in total 234 different structures were used (117 positive and 117 negative). More details seethis paper.
You can find the model in the package subfoldermodel/training_set.tar.gz.To use this model go into yourtestsubfolder and type:# Without MPIiScore.predict# With MPImpiexec-n${NPROC}iScore.predict.mpiThe code will automatically detect the path of the model.This binary will output the binary class and decision value of the conformations in the test set in a text fileiScorePredict.txt.For the predicted iScore values, the lower value, the better quality of the conformation.Example 2. Train your own modelTo train the model simply go to yourtrainsubfolder and type:# Without MPIiScore.train# With MPImpiexec-n${NPROC}iScore.train.mpiThis binary will generate a archive file called by defaulttraining_set.tar.gzthat contains all the information needed to predict binary classes of a test set using the trained model.To use this model go into yourtestsubfolder and type:# Without MPIiScore.predict--archive../train/training_set.tar.gz# With MPImpiexec-n${NPROC}iScore.predict.mpi--archive../train/training_set.tar.gz4. CitationIf you use iScore software, please cite the following articles:Cunliang Geng, Yong Jung, Nicolas Renaud, Vasant Honavar, Alexandre M J J Bonvin, and Li C Xue.“iScore: A Novel Graph Kernel-Based Function for Scoring Protein-Protein Docking Models.” Bioinformatics, 2019,https://doi.org/10.1093/bioinformatics/btz496.Nicolas Renaud, Yong Jung, Vasant Honavar, Cunliang Geng, Alexandre M. J. J. Bonvin, and Li C. Xue.“iScore: An MPI Supported Software for Ranking Protein–Protein Docking Models Based on a Random Walk Graph Kernel and Support Vector Machines.” SoftwareX, 2020,https://doi.org/10.1016/j.softx.2020.100462.