Library to interact with Kripo fragment, fingerprint, pharmacophore and similarity data files.
Use kripo to generate fragments, pharmacophores and fingerprints from PDB files.
KRIPO stands for Key Representation of Interaction in POckets, see reference for more information.
- Pocket, binding site of the ligand in the protein of a crystal structure
- Fragment, part of the ligand
- Subpocket, part of the protein pocket which binds with the fragment
- Fingerprint, fingerprint of structure-based pharmacophore of subpocket
- Similarity matrix, similarities between all fingerprint pairs calculated using the modified tanimoto similarity index
- Kripo fragment identifier, used as identifier for fragment, subpocket and fingerprint
Requirements:
- RDKit, http://www.rdkit.org/docs/Install.html, to read SDF files and generate smile strings from molecules
- pip, version 8.0.0 or greater, for wheel support
- git, to clone kripodb repository during installation
pip install -U setuptools
pip install numpy
pip install git+https://github.com/3D-e-Chem/kripodb.git
To see available commands
kripodb --help
Commands to create all data files see update documentation.
Command to find fragments most similar to 3kxm_K74_frag1 fragment.
kripodb similar sim_all.h5 3kxm_K74_frag1 --cutoff 0.45
Commands to create similarity matrix see update documentation.
An example data set included in the data/ directory of this repo. See data/README.md for more information.
All fragments based on GPCR proteins compared with all proteins in PDB.
- kripo.gpcrandhits.sqlite - Fragments sqlite database
- kripo.gpcr.h5 - HDF5 file with similarity matrix
The data set has been published at
All fragments form all proteins-ligand complexes in PDB compared with all.
- Fragments sqlite database - Download from http://3d-e-chem.vu-compmedchem.nl/kripodb/fragments.sqlite
- Pharmacophores database - Download from http://3d-e-chem.vu-compmedchem.nl/kripodb/pharmacophores.h5
- Similarity matrix - Can be queried on webservice at http://3d-e-chem.vu-compmedchem.nl/kripodb. For build instructions see http://kripodb.readthedocs.io/en/latest/data-update.html
- Fragment fingerprints - See http://kripodb.readthedocs.io/en/latest/data-update.html for instructions how to convert to a similarity matrix
Date at which the data of the 3d-e-chem.vu-compmedchem.nl webservice was last updated can found at http://3d-e-chem.vu-compmedchem.nl/kripodb/version.txt
A data set with PDB entries till 23 December 2015 has been published at
The Knime-KripoDB-example.zip file is an example workflow showing how to use KripoDB python package inside KNIME (http://www.knime.org). It can be run by importing it into KNIME. Make sure the Python used by KNIME is the same as the Python with kripodb package installed.
The https://github.com/3D-e-Chem/knime-kripodb repo adds KripoDB code templates to KNIME.
Install the development deps with:
pip install -r requirements.txt
docker build -t 3dechem/kripodb .
Show the kripodb help with
docker run --rm 3dechem/kripodb kripodb --help
To calculate the mean bit density of the fingerprints in the fingerprints.sqlite file in the current working directory use following command.
docker run --rm -u $UID -v $PWD:/data 3dechem/kripodb kripodb meanbitdensity /data/fingerprints.sqlite
The Kripo data files can be queried using a web service.
Start webservice with:
kripodb serve data/similarities.h5 data/fragments.sqlite data/pharmacophores.h5
It will print the urls for the swagger spec and UI.
Note! The webservice returns a limited amount of results. To get all results use local files.
On http://3d-e-chem.vu-compmedchem.nl/kripodb/ui/ there is a KripoDB webservice with the full PDB fragment all vs all matrix. The date of the latest PDB record included in the webservice can be found in http://3d-e-chem.vu-compmedchem.nl/kripodb/version.txt
API and data update pipeline documentation can be found at http://kripodb.readthedocs.io/en/latest/.
KRIPO – a structure-based pharmacophores approach explains polypharmacological effects; Tina Ritschel, Tom JJ Schirris, and Frans GM Russel; J Cheminform. 2014; 6(Suppl 1): O26; Published online 2014 Mar 11; http://dx.doi.org/10.1186/1758-2946-6-S1-O26