iscore

iScore: an MPI supported software for ranking protein-protein docking models based on a random walk graph kernel and support vector machines

https://github.com/deeprank/iscore

Science Score: 77.0%

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Low similarity (18.2%) to scientific vocabulary

Last synced: 6 months ago · JSON representation ·

Repository

iScore: an MPI supported software for ranking protein-protein docking models based on a random walk graph kernel and support vector machines

Basic Info

Host: GitHub
Owner: DeepRank
License: apache-2.0
Language: Python
Default Branch: master
Homepage:
Size: 20.7 MB

Statistics

Stars: 31
Watchers: 3
Forks: 10
Open Issues: 12
Releases: 4

Created over 7 years ago · Last pushed over 3 years ago

Metadata Files

Readme License Citation Zenodo

iScore

Support Vector Machine on Graph Kernel for Ranking Protein-Protein Docking Models

alt text

1. Installation

Minimal information to install the module - Check if command mpiexec is available or not in your console. If not, download and install openmpi or mpich. - Install iScore using pip install iScore

Possible problems: - If pip install iScore gives problems on installing mpi4py, try to first install mpi4py using conda install mpi4py and then pip install iScore.

2. Documentation

The documentation of the package can be found at: - https://iscoredoc.readthedocs.io

3. Quick Examples

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 (iscore.train.mpi and iscore.predict.mpi for 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 structure root/ |__train/ | |__ pdb/ | |__ pssm/ | |__ class.lst |__test/ |__pdb/ |__pssm/ |__ class.lst (optional) The pdb folder contains the PDB files of docking models, and pssm contains the PSSM files. The class.lst is a list of class ID and PDB file name for each docking model, like 0 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. PSSMGen can be used to get consistent PSSM and PDB files. It is already installed along with iScore. Check README to see how to use it.

Example 1. Use our trained model

You 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 see this paper. You can find the model in the package subfolder model/training_set.tar.gz.

To use this model go into your test subfolder and type:

```bash

Without MPI

iScore.predict

With MPI

mpiexec -n ${NPROC} iScore.predict.mpi ```

The 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 file iScorePredict.txt.

  For the predicted iScore values, the lower value, the better quality of the conformation.

Example 2. Train your own model

To train the model simply go to your train subfolder and type:

```bash

Without MPI

iScore.train

With MPI

mpiexec -n ${NPROC} iScore.train.mpi ```

This binary will generate a archive file called by default training_set.tar.gz that contains all the information needed to predict binary classes of a test set using the trained model.

To use this model go into your test subfolder and type:

```bash

Without MPI

iScore.predict --archive ../train/training_set.tar.gz

With MPI

mpiexec -n ${NPROC} iScore.predict.mpi --archive ../train/training_set.tar.gz ```

4. Citation

If 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.

Owner

Name: DeepRank
Login: DeepRank
Kind: organization

Repositories: 19
Profile: https://github.com/DeepRank

Citation (CITATION.cff)

# YAML 1.2
---
abstract: "iScore allows to score protein-protein interface using graph kernels and support vector machine. "
authors:
  -
    affiliation: "Netherlands eScience Center"
    family-names: Renaud
    given-names: Nicolas

  -
    affiliation: "Netherlands eScience Center"
    family-names: Geng
    given-names: Cunliang
  -
    affiliation: "Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University"
    family-names: Xue
    given-names: Li


cff-version: 1.2.0
doi: "10.5281/zenodo.2630566"
keywords:
  - scoring
  - docking
  - "protein-protein complex"
  - "support vector machine"
  - "graph kernel"
  - "machine learning"

license: "Apache-2.0"
message: "If you use this software, please cite it using these metadata."
title: "iScore: Support Vector Machine on Graph Kernel for Ranking Protein-Protein Docking Models"
...

GitHub Events

Total

Watch event: 4

Last Year

Watch event: 4

Committers

Last synced: over 2 years ago

All Time

Total Commits: 245
Total Committers: 7
Avg Commits per committer: 35.0
Development Distribution Score (DDS): 0.363

Past Year

Commits: 0
Committers: 0
Avg Commits per committer: 0.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
Nicolas Renaud	n**d@t**l	156
Cunliang Geng	c**g@e**l	58
NicoRenaud	n**d@g**m	25
CunliangGeng	c**g@u**l	2
Abel Soares Siqueira	a**a@g**m	2
Li Xue	m**e@g**m	1
dependabot-preview[bot]	2****]	1

Committer Domains (Top 20 + Academic)

uu.nl: 1 esciencecenter.nl: 1 tudelft.nl: 1

Issues and Pull Requests

Last synced: 8 months ago

All Time

Total issues: 24
Total pull requests: 11
Average time to close issues: about 2 months
Average time to close pull requests: 2 months
Total issue authors: 8
Total pull request authors: 5
Average comments per issue: 1.25
Average comments per pull request: 0.55
Merged pull requests: 10
Bot issues: 1
Bot pull requests: 1

Past Year

Issues: 0
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

LilySnow (16)
CunliangGeng (2)
dependabot-preview[bot] (1)
NickEdmunds (1)
NicoRenaud (1)
zjq101 (1)
jiyanbio (1)
thuxugang (1)

Pull Request Authors

CunliangGeng (6)
NicoRenaud (2)
dependabot-preview[bot] (1)
dragon0113 (1)
abelsiqueira (1)

Top Labels

Issue Labels

bug (2) enhancement (2)

Pull Request Labels

dependencies (1)

Packages

Total packages: 1
Total downloads:
- pypi 69 last-month

Total dependent packages: 0
Total dependent repositories: 1
Total versions: 8
Total maintainers: 2

pypi.org: iscore

Scoring protein-protein interface using RWGK and SVM

Homepage: https://github.com/DeepRank/iScore
Documentation: https://iscore.readthedocs.io/
License: Apache Software License 2.0
Latest release: 0.3.5
published over 3 years ago

Versions: 8
Dependent Packages: 0
Dependent Repositories: 1
Downloads: 69 Last month

Rankings

Dependent packages count: 10.0%

Forks count: 10.2%

Stargazers count: 12.3%

Average: 15.5%

Dependent repos count: 21.7%

Downloads: 23.1%

Maintainers (2)

CunliangGeng nico_reno

Last synced: 7 months ago

Dependencies

setup.py pypi

biopython *
h5py *
h5xplorer *
libsvm *
matplotlib *
mpi4py *
numpy *
pdb2sql *
pssmgen *
scipy *

iscore

Science Score: 77.0%

Repository

Basic Info

Statistics

Metadata Files

README.md

iScore

1. Installation

2. Documentation

3. Quick Examples

Requirements for preparing data:

Example 1. Use our trained model

Without MPI

With MPI

Example 2. Train your own model

Without MPI

With MPI

Without MPI

With MPI

4. Citation

Owner

Citation (CITATION.cff)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

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Issue Labels

Pull Request Labels

Packages

pypi.org: iscore

Rankings

Maintainers (2)

Dependencies