About the nodes
The 3D-e-Chem nodes have been developed as part of the 3D-e-Chem project by Vrije Universiteit Amsterdam, Radboudumc Nijmegen and Netherlands eScience Center. The nodes complement existing cheminformatics and bioinformatics nodes to enable the efficient exploitation of structural and pharmacological protein-ligand interaction data from proteome-wide databases, as well as customized information systems focused on e.g. G Protein-Coupled Receptors (GPCRdb) and protein kinases (KLIFS). The 3D-e-Chem KNIME node toolbox provides building blocks for the design of flexible computer-aided drug discovery workflows, including ligand-based metabolism prediction (SyGMA), pharmacophore-based (align-it) and shape-based (shape-it) alignment of molecules, protein sequence analyses (ss-TEA), and structure-based protein binding site comparison and bioisosteric replacement for ligand design (KRIPO), molecular docking (PLANTS) and the visualization and alignment of protein-ligand complexes (Molviewer) and pharmacophores (Pharmacohore). The open source, freely available Virtual Machine, 3D-e-Chem-VM facilitates the efficient use of pre-configured 3D-e-Chem tools and other resources.
Current 3D-e-Chem nodes have been grouped as follows:
- GPCRDB, Extraction of sequence, structural, protein-ligand interaction, and site-directed mutagenesis information of GPCRs from the GPCRdb database.
- KLIFS, Extraction of sequence, structural, and protein-ligand interaction information of kinases from the KLIFS database.
- KRIPOdb, nodes to identify KRIPO (Key Representation of Interaction in Pockets) pharmacophore based similarities between protein binding sites and corresponding ligand substructures.
- Molviewer, Web-based 3D molecule viewer of ligands, proteins and pharmacophores.
- SyGMa, Systematic generation of potential phase 1 and phase 2 metabolite structures. The SyGMa node is a thin wrapper around the SyGMa Python library
- Pharmacophore aligner, readers and writers
- PLANTS, to set up, run, and analyse molecular docking simulations.
- Silicos-it, sets of nodes for pharmacophore-based (align-it) and shape-based (shape-it) alignment of molecules, property-based filtering of chemical databases (filter-it), and chemical scaffold analysis (strip-it).
- ss-TEA score, Entropy-based identification of receptor-specific ligand binding residues
- Modified Tanimoto distance measure that can be selected in the "Bit Vector Distance" node of the KNIME Distance matrix extension.
The GitHub repository of each node has a simple example workflow, including:
- Chemdb4VS workflow for the evaluation and optimization of virtual screening strategies
- GPCRdb example workflow for the extraction and combination of structural, sequence, and mutation data for specific receptors from GPCRdb
- KLIFS example workflow for the integrated analysis of structural kinase-ligand interactions, kinase binding sites, and kinase ligand features for a specific kinase in KLIFS.
- KRIPO example workflow for the identification of protein-ligand binding site similarities and possibilities for bioisosteric replacements by structure-based ligand design.
- Ligand-based cross-reactivity prediction
- Scaffold replacements for kinase ligand design
- Sequence-based ligand repurposing within a protein family
- Structure-based bioactivity data mapping of kinase inhibitors workflow.
- Structure-based GPCR-kinase cross-reactivity prediction workflow for off-target identification, ligand repurposing, or the discovery of ligands with a desired GPCR-kinase polypharmacology profile
- SyGMa example workflow for metabolite prediction.
Any questions about the nodes can be posted on the 3D-e-Chem KNIME forum. Several example workflows have been described in the 3D-e-Chem application note, and a list of 3D-e-Chem workflows can be found here.
About the 3D-e-Chem project
The 3D-e-Chem project, involving the Vrije Universiteit Amsterdam, Radboudumc Nijmegen and Netherlands eScience Center, develops technologies to improve the integration of ligand and protein data for structure-based prediction of protein-ligand selectivity and polypharmacology. The project uses the KNIME Analytics Platform to integrate different structural cheminformatics and bioinformatics technologies and datasets.
The source code can be accessed at https://github.com/3D-e-Chem. Each node has its own repository, for example, https://github.com/3D-e-Chem/knime-gpcrdb contains the source code for the GPCRDB nodes.
The 3D-e-Chem nodes are released under GNU GPL version 3 license.