Faculty Opinions recommendation of KLIFS: a knowledge-based structural database to navigate kinase-ligand interaction space.

To obtain a better understanding of which coformers to combine for the successful formation of a cocrystal, techniques from data mining and network science are used to analyze the data contained in the Cambridge Structural Database (CSD). A network of coformers is constructed based on cocrystal entries present in the CSD and its properties are analyzed. From this network, clusters of coformers with a similar tendency to form cocrystals are extracted. The popularity of the coformers in the CSD is unevenly distributed: a small group of coformers is responsible for most of the cocrystals, hence resulting in an inherently biased data set. The coformers in the network are found to behave primarily in a bipartite manner, demonstrating the importance of combining complementary coformers for successful cocrystallization. Based on our analysis, it is demonstrated that the CSD coformer network is a promising source of information for knowledge-based cocrystal prediction.

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Multivariate PLS Modeling of Apicomplexan FabD-Ligand Interaction Space for Mapping Target-Specific Chemical Space and Pharmacophore Fingerprints

PLoS ONE ◽

10.1371/journal.pone.0141674 ◽

2015 ◽

Vol 10 (11) ◽

pp. e0141674 ◽

Cited By ~ 2

Author(s):

Ashalatha Sreshty Mamidi ◽

Prerna Arora ◽

Avadhesha Surolia

Keyword(s):

Chemical Space ◽

Interaction Space ◽

Ligand Interaction ◽

Specific Chemical ◽

Pls Modeling

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KLIFS: an overhaul after the first 5 years of supporting kinase research

Nucleic Acids Research ◽

10.1093/nar/gkaa895 ◽

2020 ◽

Vol 49 (D1) ◽

pp. D562-D569

Author(s):

Georgi K Kanev ◽

Chris de Graaf ◽

Bart A Westerman ◽

Iwan J P de Esch ◽

Albert J Kooistra

Keyword(s):

Kinase Inhibitors ◽

Ligand Interaction ◽

The Past ◽

Structural Database ◽

Approved Drugs ◽

Interaction Fingerprints ◽

Prime Target ◽

Complete Overhaul ◽

Programmatic Access ◽

Systematic Collection

Abstract Kinases are a prime target of drug development efforts with >60 drug approvals in the past two decades. Due to the research into this protein family, a wealth of data has been accumulated that keeps on growing. KLIFS—Kinase–Ligand Interaction Fingerprints and Structures—is a structural database focusing on how kinase inhibitors interact with their targets. The aim of KLIFS is to support (structure-based) kinase research through the systematic collection, annotation, and processing of kinase structures. Now, 5 years after releasing the initial KLIFS website, the database has undergone a complete overhaul with a new website, new logo, and new functionalities. In this article, we start by looking back at how KLIFS has been used by the research community, followed by a description of the renewed KLIFS, and conclude with showcasing the functionalities of KLIFS. Major changes include the integration of approved drugs and inhibitors in clinical trials, extension of the coverage to atypical kinases, and a RESTful API for programmatic access. KLIFS is available at the new domain https://klifs.net.

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Structural Chemogenomics Databases to Navigate Protein–Ligand Interaction Space

Comprehensive Medicinal Chemistry III ◽

10.1016/b978-0-12-409547-2.12298-x ◽

2017 ◽

pp. 444-471 ◽

Cited By ~ 1

Author(s):

G.K. Kanev ◽

A.J. Kooistra ◽

I.J.P de Esch ◽

C. de Graaf

Keyword(s):

Interaction Space ◽

Ligand Interaction ◽

Protein Ligand Interaction

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Life-science applications of the Cambridge Structural Database

Acta Crystallographica Section D Biological Crystallography ◽

10.1107/s090744490200358x ◽

2002 ◽

Vol 58 (6) ◽

pp. 879-888 ◽

Cited By ~ 50

Author(s):

Robin Taylor

Keyword(s):

Intermolecular Interactions ◽

Life Science ◽

Crystal Packing ◽

Three Dimensional ◽

Crystallographic Data ◽

Cambridge Structural Database ◽

Biologically Active ◽

Ligand Interaction ◽

Chemical Structures ◽

Structural Database

Several studies show that the molecular geometries and intermolecular interactions observed in small-molecule crystal structures are relevant to the modelling ofin vivosituations, although the influence of crystal packing is sometimes important and should always be borne in mind. Torsional distributions derived from the Cambridge Structural Database (CSD) can be used to map out potential-energy surfaces and thereby help identify experimentally validated conformational minima of molecules with several rotatable bonds. The use of crystallographic data in this way is complementary toin vacuotheoretical calculations since it gives insights into conformational preferences in condensed-phase situations. Crystallographic data also underpin many molecular-fragment libraries and programs for generating three-dimensional models from two-dimensional chemical structures. The modelling of ligand binding to metalloenzymes is assisted by information in the CSD on preferred coordination numbers and geometries. CSD data on intermolecular interactions are useful in structure-based inhibitor design both in indicating how probable a protein–ligand interaction is and what its geometry is likely to be. They can also be used to guide searches for bioisosteric replacements. Crystallographically derived information has contributed to many life-science software applications, including programs for locating binding `hot spots' on proteins, docking ligands into enzyme active sites,de novoligand design, molecular superposition and three-dimensional QSAR. Overall, crystallographic data in general, and the CSD in particular, are very significant tools for the rational design of biologically active molecules.

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