Discovery of a VHL and HIF1α interaction inhibitor with in vivo angiogenic activity via structure-based virtual screening

Yang, Chao; Wang, Wanhe; Chen, Linmin; Liang, Jiaxin; Lin, Sheng; Lee, Simon Ming‐Yuen; Ma, Dik‐Lung; Leung, Chung‐Hang

doi:10.1039/c6cc04938a

Cited by 39 publications

(21 citation statements)

References 41 publications

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“…Among the different types of drug design methods, PBDD has the highest reliability [9]. A search for potential enzyme inhibitors for new drug development or therapeutic drug repurposing has been carried out with the aid of these methods, such as the study by Wu et al [10] and Yang et al [11], who identified mitoxantrone 1 as a new ATP-competitive inhibitor of NEDD8-activating enzyme (NAE) by virtual screening and an inhibitor of the VHL-HIF1α interaction via structure based virtual screening, respectively.…”

Section: Introductionmentioning

confidence: 99%

Identification of Novel Chemical Entities for Adenosine Receptor Type 2A Using Molecular Modeling Approaches

et al. 2020

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Adenosine Receptor Type 2A (A2AAR) plays a role in important processes, such as anti-inflammatory ones. In this way, the present work aimed to search for compounds by pharmacophore-based virtual screening. The pharmacokinetic/toxicological profiles of the compounds, as well as a robust QSAR, predicted the binding modes via molecular docking. Finally, we used molecular dynamics to investigate the stability of interactions from ligand-A2AAR. For the search for A2AAR agonists, the UK-432097 and a set of 20 compounds available in the BindingDB database were studied. These compounds were used to generate pharmacophore models. Molecular properties were used for construction of the QSAR model by multiple linear regression for the prediction of biological activity. The best pharmacophore model was used by searching for commercial compounds in databases and the resulting compounds from the pharmacophore-based virtual screening were applied to the QSAR. Two compounds had promising activity due to their satisfactory pharmacokinetic/toxicological profiles and predictions via QSAR (Diverset 10002403 pEC50 = 7.54407; ZINC04257548 pEC50 = 7.38310). Moreover, they had satisfactory docking and molecular dynamics results compared to those obtained for Regadenoson (Lexiscan®), used as the positive control. These compounds can be used in biological assays (in vitro and in vivo) in order to confirm the potential activity agonist to A2AAR.

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Section: Introductionmentioning

confidence: 99%

Identification of Novel Chemical Entities for Adenosine Receptor Type 2A Using Molecular Modeling Approaches

et al. 2020

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“…Natural products have long been regarded as a rich source of structural motifs for drug discovery 19. , 20.…”

Section: Introductionmentioning

confidence: 99%

Aurone derivatives as Vps34 inhibitors that modulate autophagy

Boyle

et al. 2019

Acta Pharmaceutica Sinica B

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We report in this study the identification of a natural product-like antagonist ( 1a ) of Vps34 as a potent autophagy modulator via structure-based virtual screening. Aurone derivative 1a strongly inhibited Vps34 activity in cell-free and cell-based assays. Significantly, 1a prevents autophagy in human cells induced either by starvation or by an mTOR inhibitor. In silico modeling and kinetic data revealed that 1a could function as an ATP-competitive inhibitor of Vps34. Moreover, it suppressed autophagy in vivo and without inducing heart or liver damage in mice. 1a could be utilized as a new motif for more selective and efficacious antagonists of Vps34 for the potential treatment of autophagy-related human diseases.

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“…HIF1α is a protein composed of 352 amino acid residues (Semenza 2007). Considering its important role in many cancers, developing a high efficient HIF1α inhibitors is becomes a priority (Yang et al 2016). After years of effort, many kinds of inhibitors were designed and tested.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the binding mode of a novel HIF1α inhibitor through molecular modelling

Zhao

Liu²,

Wang³

et al. 2018

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Hypoxia inducible factor-1 (HIF1) is an important transcription factor related with tumor metastasis. As a subunit of HIF1, HIF1α plays an important role in regulation of the hypoxic response. HIF1α inhibitor could be a promising treatment for certain cancers. In the present study, we try to model the binding mode of the recent reported new series of HIF1α inhibitors with the purpose of further improving the performance of these inhibitors. Molecular docking was first employed to predict the binding modes of the protein-ligand complexes, followed by molecular dynamics simulations and MMGBSA free energy calculations. According to the predicted binding modes, these molecules form two important hydrogen bonds with HIF1α. Moreover, if the molecules could form a stable π-π interaction with HIF1α, the potency of the inhibitors can be greatly improved. Hydrophobic interactions between the molecules and HIF1α are also a key factor, especially the interactions between the hydrophobic groups (benzimidazole) of the molecules and the hydrophobic residues inside the binding cave. Molecular modeling could be a useful method in the future drug design. In this study, our calculation can help to design and develop high potent HIF1α inhibitors in future.

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Discovery of a VHL and HIF1α interaction inhibitor with in vivo angiogenic activity via structure-based virtual screening

Cited by 39 publications

References 41 publications

Identification of Novel Chemical Entities for Adenosine Receptor Type 2A Using Molecular Modeling Approaches

Identification of Novel Chemical Entities for Adenosine Receptor Type 2A Using Molecular Modeling Approaches

Aurone derivatives as Vps34 inhibitors that modulate autophagy

Analysis of the binding mode of a novel HIF1α inhibitor through molecular modelling

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