KLIFS: a structural kinase-ligand interaction database

Kooistra, Albert J.; Kanev, Georgi K.; Linden, Oscar P.J. van; Leurs, Rob; Esch, Iwan J. P. de; Graaf, Chris de

doi:10.1093/nar/gkv1082

Cited by 153 publications

(176 citation statements)

References 32 publications

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“…Typically, three types of targeted kinase inhibitors, type-I, type-II and type-III, have been developed [10,11]. Type-I inhibitors are ATP-competitive and occupy the ATP-binding binding pocket, a highly conserved kinase catalytic scaffold with strong binding affinity for ATP.…”

Section: Introductionmentioning

confidence: 99%

Insights into the binding mode of MEK type-III inhibitors. A step towards discovering and designing allosteric kinase inhibitors across the human kinome

2017

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Protein kinases are critical drug targets for treating a large variety of human diseases. Type-III kinase inhibitors have attracted increasing attention as highly selective therapeutics. Thus, understanding the binding mechanism of existing type-III kinase inhibitors provides useful insights into designing new type-III kinase inhibitors. In this work, we have systematically studied the binding mode of MEK-targeted type-III inhibitors using structural systems pharmacology and molecular dynamics simulation. Our studies provide detailed sequence, structure, interaction-fingerprint, pharmacophore and binding-site information on the binding characteristics of MEK type-III kinase inhibitors. We hypothesize that the helix-folding activation loop is a hallmark allosteric binding site for type-III inhibitors. Subsequently, we screened and predicted allosteric binding sites across the human kinome, suggesting other kinases as potential targets suitable for type-III inhibitors.

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Section: Introductionmentioning

confidence: 99%

Insights into the binding mode of MEK type-III inhibitors. A step towards discovering and designing allosteric kinase inhibitors across the human kinome

2017

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“…This system has been augmented by new tools to use structural protein–ligand interaction data from KRIPO, 30 GPCRdb, 27 and KLIFS 28,29 databases and has been made publically available on GitHub (). The previously reported myChEMBL VM 33 provided a useful template to design the 3D-e-Chem-VM and a local copy of the ChEMBL database 19 can optionally be incorporated into the VM ().…”

Section: Introductionmentioning

confidence: 99%

“…This node can optionally add the sequence number and the KLIFS residue position 29 for each pocket residue to the table.Interaction Fingerprint Retriever: Retrieval of the interaction fingerprint of specific kinase-ligand complexes from KLIFS. The fingerprint has been corrected for gaps/missing residues within the KLIFS pocket thereby enabling all-against-all comparisons.…”

Section: Introductionmentioning

confidence: 99%

“…This node can optionally add the sequence number and the KLIFS residue position 29 for each pocket residue to the table.…”

Section: Introductionmentioning

confidence: 99%

“…The node returns the structure ID, kinase name, kinase ID, PDB-code, and all other structural annotation data within KLIFS (e.g., pocket sequence, resolution, quality, ligands, DFG conformation, targeted subpockets, waters). 29 Structures PDB Mapper: Maps a set of PDB-codes to structure IDs from KLIFS and provides all related structural information from KLIFS.Structures Retriever (MOL2): Retrieves from KLIFS a set of structures, (optionally the full complex, the protein, the pocket, or the ligand) in MOL2 format, based on a user-supplied set of Structure IDs. As output the node provides a table of aligned structures based on the KLIFS pocket definition.…”

Section: Introductionmentioning

confidence: 99%

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3D-e-Chem-VM: Structural Cheminformatics Research Infrastructure in a Freely Available Virtual Machine

McGuire

Verhoeven

Vass

et al. 2017

J. Chem. Inf. Model.

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3D-e-Chem-VM is an open source, freely available Virtual Machine () that integrates cheminformatics and bioinformatics tools for the analysis of protein–ligand interaction data. 3D-e-Chem-VM consists of software libraries, and database and workflow tools that can analyze and combine small molecule and protein structural information in a graphical programming environment. New chemical and biological data analytics tools and workflows have been developed for the efficient exploitation of structural and pharmacological protein–ligand interaction data from proteomewide databases (e.g., ChEMBLdb and PDB), as well as customized information systems focused on, e.g., G protein-coupled receptors (GPCRdb) and protein kinases (KLIFS). The integrated structural cheminformatics research infrastructure compiled in the 3D-e-Chem-VM enables the design of new approaches in virtual ligand screening (Chemdb4VS), ligand-based metabolism prediction (SyGMa), and structure-based protein binding site comparison and bioisosteric replacement for ligand design (KRIPOdb).

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