Kinase array design, back to front: Biaryl amides

Baldwin, Ian R.; Bamborough, Paul; Haslam, Claudine; Hunjan, Suchete S.; Longstaff, Tim; Mooney, Christopher J.; Patel, Shila; Quinn, Jo; Somers, Don O.

doi:10.1016/j.bmcl.2008.08.051

Cited by 22 publications

(19 citation statements)

References 13 publications

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“…Such a feature is actually found in the fluorescein scaffold and in our pharmacophore model. [84][85][86][87] However pharmacophore models of kinase inhibitors are also characterized by at least one hydrogen bond donor or acceptor not present in our model, therefore supporting the hypothesis of an allosteric binding site. We could also envisage a possible interaction of RB with other proteins that take part in the transport of neurotransmitters and are present in the system such as chloride, potassium channels or kinase such as pyruvate kinase 88 or phosphodiesterase.…”

Section: Vmat2 and Vacht Pharmacologysupporting

confidence: 74%

Rose Bengal analogs and vesicular glutamate transporters (VGLUTs)

Pietrancosta

Kessler

Favre-Besse

et al. 2010

Bioorganic & Medicinal Chemistry

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Section: Vmat2 and Vacht Pharmacologysupporting

confidence: 74%

Rose Bengal analogs and vesicular glutamate transporters (VGLUTs)

Pietrancosta

Kessler

Favre-Besse

et al. 2010

Bioorganic & Medicinal Chemistry

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“…For example, ligand 19B was originally designed for P38α (top 0.6%; ranked 9 among 1463 ligands) and was found to inhibit BRAF in a panel of kinase assay. 39 The DFG-out model identified 19B as one of the top hits (top 2%; ranked 27), with a docked pose similar to the crystallographic pose in P38α (Figure 6 E). Another example is ligand B1E, a BRAF(V600E) inhibitor by design (top 0.5%; ranked 6) that was found to inhibit SRC (top 0.8%; ranked 10) (Figure 6 D).…”

Section: Results and Discussionmentioning

confidence: 99%

DFGmodel: Predicting Protein Kinase Structures in Inactive States for Structure-Based Discovery of Type-II Inhibitors

Ung

Schlessinger

2014

ACS Chem. Biol.

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Protein kinases exist in equilibrium of active and inactive states, in which the aspartate-phenylalanine-glycine motif in the catalytic domain undergoes conformational changes that are required for function. Drugs targeting protein kinases typically bind the primary ATP-binding site of an active state (type-I inhibitors) or utilize an allosteric pocket adjacent to the ATP-binding site in the inactive state (type-II inhibitors). Limited crystallographic data of protein kinases in the inactive state hampers the application of rational drug discovery methods for developing type-II inhibitors. Here, we present a computational approach to generate structural models of protein kinases in the inactive conformation. We first perform a comprehensive analysis of all protein kinase structures deposited in the Protein Data Bank. We then develop DFGmodel, a method that takes either a known structure of a kinase in the active conformation or a sequence of a kinase without a structure, to generate kinase models in the inactive conformation. Evaluation of DFGmodel’s performance using various measures indicates that the inactive kinase models are accurate, exhibiting RMSD of 1.5 Å or lower. The kinase models also accurately distinguish type-II kinase inhibitors from likely nonbinders (AUC > 0.70), suggesting that they are useful for virtual screening. Finally, we demonstrate the applicability of our approach with three case studies. For example, the models are able to capture inhibitors with unintended off-target activity. Our computational approach provides a structural framework for chemical biologists to characterize kinases in the inactive state and to explore new chemical spaces with structure-based drug design.

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“…Back pocket-binding fragments are key to this approach because they provide the starting points for optimization. 16,17 Developing an effective and efficient assay is the main challenge in the application of FBDD to the back pocket. Typically, the activated kinase is used for enzymatic screening of compound libraries.…”

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confidence: 99%

A Back-to-Front Fragment-Based Drug Design Search Strategy Targeting the DFG-Out Pocket of Protein Tyrosine Kinases

Iwata

Oki

Okada

et al. 2012

ACS Med. Chem. Lett.

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We present a straightforward process for the discovery of novel back pocket-binding fragment molecules against protein tyrosine kinases. The approach begins by screening against the nonphosphorylated target kinase with subsequent counterscreening of hits against the phosphorylated enzyme. Back pocket-binding fragments are inactive against the phosphorylated kinase. Fragment molecules are of insufficient size to span both regions of the ATP binding pocket; thus, the outcome is binary (back pocket-binding or hinge-binding). Next, fragments with the appropriate binding profile are assayed in combination with a known hinge-binding fragment and subsequently with a known back pocket-binding fragment. Confirmation of back pocket-binding by Yonetani− Theorell plot analysis progresses candidate fragments to crystallization trials. The method is exemplified by a fragment screening campaign against vascular endothelial growth factor receptor 2, and a novel back pocket-binding fragment is presented.

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Kinase array design, back to front: Biaryl amides

Cited by 22 publications

References 13 publications

Rose Bengal analogs and vesicular glutamate transporters (VGLUTs)

Rose Bengal analogs and vesicular glutamate transporters (VGLUTs)

DFGmodel: Predicting Protein Kinase Structures in Inactive States for Structure-Based Discovery of Type-II Inhibitors

A Back-to-Front Fragment-Based Drug Design Search Strategy Targeting the DFG-Out Pocket of Protein Tyrosine Kinases

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