Discovery of ZAP70 inhibitors by high-throughput docking into a conformation of its kinase domain generated by molecular dynamics

Zhao, Hongtao; Caflisch, Amedeo

doi:10.1016/j.bmcl.2013.08.009

Cited by 121 publications

(82 citation statements)

References 24 publications

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“…The RMSD between the top‐ranked docked pose and the original conformation in the crystal was calculated. As shown in Table , docking with Glide SP protocol successfully restored the near‐native conformations of the co‐crystallized inhibitors for the twelve prepared receptors (4G3E_A, 4G3E_B, 4G3F_A, 4G3G_A, 4IDT_A, 4IDT_B, 4IDV_B, 4IDV_D, 5T8O_A, 5T8O_B, 5T8P_A, and 5T8P_B), with RMSD <1.0 Å. Among them 4IDV_D (chain D of 4IDV) has the lowest RMSD value.…”

Section: Resultsmentioning

confidence: 88%

“…The best two discrimination powers ( p = 1.39 × 10 −100 and 2.08 × 10 −69 ) were achieved using 5T8O_A and 4IDV_D as the docking receptors (Table , entry 12 and entry 13; the distributions of the docking scores are shown in Figure a and Figure b). We also tested the discrimination powers between known inhibitors and decoys by the other three scoring programme (i.e., Glide XP scoring, AutoDock Vina and LeDock). As depicted in Figure , these three scoring protocols afforded significantly lower discrimination performance (Figure c–h) than Glide SP scoring (Figure a,b).…”

Section: Resultsmentioning

confidence: 99%

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Identification of new NIK inhibitors by discriminatory analysis‐based molecular docking and biological evaluation

Cheng

Mei

Yan

et al. 2019

Archiv der Pharmazie

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NF-κB inducing kinase (NIK) is a key regulator in the noncanonical nuclear factor κB cells (NF-κB) signaling pathway. Dysregulation of NIK is often related with autoimmune disorders and malignancies. However, the number of reported NIK inhibitors is scarce. Discriminatory analysis-based molecular docking was used to examine the accuracy of the binding conformation and to estimate the binding affinity, leading to the identification of several new NIK inhibitors with moderate IC 50 (ranging from 48.9 to 103.4 μM). Among them, compound 5, the most potent one (IC 50 48.9 ± 6.9 μM), also showed moderate antiproliferation activity against cancer SW1990 cells, with an IC 50 value of 20.1 ± 6.0 μM. Further dynamic simulations were performed to provide more in-depth details on the binding conformation of compound 5 and the NIK protein, providing some structural clues for further optimization of compound 5 as a novel NIK inhibitor.

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Section: Resultsmentioning

confidence: 88%

Section: Resultsmentioning

confidence: 99%

Identification of new NIK inhibitors by discriminatory analysis‐based molecular docking and biological evaluation

Cheng

Mei

Yan

et al. 2019

Archiv der Pharmazie

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“…18 Then, the LeDock software was used to estimate the binding mode between PHI and MELK. 19 During docking calculations, 200 conformations were generated for molecular dynamics (MD) simulations using the optimal binding energy conformation. Prior to MD simulation, the partial atomic charges of PHI were estimated by the restrained electrostatic potential (RESP) method on the basis of HF/6-13G* basis set.…”

Section: Molecular Modeling Of Melk Bound With Phimentioning

confidence: 99%

<p>Phillygenin, a MELK Inhibitor, Inhibits Cell Survival and Epithelial–Mesenchymal Transition in Pancreatic Cancer Cells</p>

Chen

Yang

et al. 2020

OTT

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Introduction: Pancreatic cancer (PC) is one of the leading causes of cancer, with the lowest 5-year survival rate of all cancer types. Given the fast metastasis of PC and its resistance to surgery, radiotherapy, chemotherapy, and combinations thereof, it is imperative to develop more effective anti-PC drugs. Phillygenin (PHI) has been reported to exert anti-cancer, antibacterial, and anti-inflammatory properties. However, the mechanism of PHI in the development of PC is still unclear. Methods: The cytotoxicity of PHI in pancreatic cancer cells was evaluated by MTT assay, and clonogenic assay was used to test the anti-proliferation of PHI. The pro-apoptotic effect of PHI was detected by flow cytometry analysis. The changes of epithelial-mesenchymal transition (EMT) in pancreatic cancer cells treated with PHI were determined by Western blot. Transwell assay was used to test the migration and invasion of PC cells after treatment with PHI. Molecular docking was used to predict the potential binding site of candidate target with PHI. Results: PHI could inhibit the proliferation, migration, and EMT of PC cells (PANC-1 and SW1990) and induce its apoptosis. Analysis of the Cancer Genome Atlas database indicated that elevated MELK levels correlated with poor overall survival (OS) and disease-free survival (DFS) of PC patients. In addition, molecular modeling showed that PHI may potentially target the catalytic domain of maternal embryonic leucine zipper kinase (MELK). Overexpression of MELK muted the anti-PC effects of PHI. Conclusion: PHI holds promise as a potent candidate drug for the treatment of PC via targeted MELK.

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“…These data are consistent with studies that demonstrate that treatment with rapamycin (Rapa) in the context of allogeneic bone marrow transplantation delays pathogenic graft versus host disease rather than block it completely 32,33 . Targeting ZAP70 for a more complete block of early TCR signaling has been challenging due to high levels of homology in the active site and ATP binding domains of these kinases, which poses difficulties in developing highly selective pharmacological inhibitors of ZAP70 34 . Therefore, we explored whether a combination of drugs such as cyclosporine (CsA) and Rapa which block distinct TCR-induced signaling pathways result in a more complete suppression of metabolism and inflammatory functions of human T cells.…”

Section: Strength Of Tcr Stimulation Differentially Regulates Metabolmentioning

confidence: 99%

Integrated Metabolomic and Transcriptomic Profiling Reveals Novel Activation-Induced Metabolic Networks in Human T cells

Hiemer

Jatav²,

Jussif

et al. 2019

Preprint

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The targeting of metabolic pathways is emerging as an exciting new approach for modulating immune cell function and polarization states. In this study, carbon tracing and systems biology approaches integrating metabolomic and transcriptomic profiling data were used to identify adaptations in human T cell metabolism important for fueling pro-inflammatory T cell function.Results of this study demonstrate that T cell receptor (TCR) stimulation leads to a significant increase in glucose and amino acid metabolism that trigger downstream biosynthetic processes. Specifically, increased expression of several enzymes such as CTPS1, IL4I1, and ASL results in the reprogramming of amino acid metabolism. Additionally, the strength of TCR signaling resulted in different metabolic enzymes utilized by T cells to facilitate similar biochemical endpoints. Furthermore, this study shows that cyclosporine represses the pathways involved in amino acid and glucose metabolism, providing novel insights on the immunosuppressive mechanisms of this drug. To explore the implications of the findings of this study in clinical settings, conventional immunosuppressants were tested in combination with drugs that target metabolic pathways. Results showed that such combinations increased efficacy of conventional immunosuppressants. Overall, the results of this study provide a comprehensive resource for identifying metabolic targets for novel combinatorial regimens in the treatment of intractable immune diseases.

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Discovery of ZAP70 inhibitors by high-throughput docking into a conformation of its kinase domain generated by molecular dynamics

Cited by 121 publications

References 24 publications

Identification of new NIK inhibitors by discriminatory analysis‐based molecular docking and biological evaluation

Identification of new NIK inhibitors by discriminatory analysis‐based molecular docking and biological evaluation

<p>Phillygenin, a MELK Inhibitor, Inhibits Cell Survival and Epithelial–Mesenchymal Transition in Pancreatic Cancer Cells</p>

Integrated Metabolomic and Transcriptomic Profiling Reveals Novel Activation-Induced Metabolic Networks in Human T cells

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