Molecular docking based screening analysis of GSK3B

Ogunleye, Adewale J; Olanrewaju, Afeez J; Arowosegbe, Michael Aderibigbe; Omotuyi, Olaposi Idowu

doi:10.6026/97320630015201

Cited by 8 publications

(5 citation statements)

References 25 publications

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“…The beta isoform of the serine–threonine kinase glycogen synthase kinase 3 (GSK3β) is a promising drug target for several neurological diseases, including AD, Parkinson’s disease, schizophrenia, and bipolar disorder, as well as other diseases involving energy metabolism and cell death, like diabetes and cancer. 6 − 9 The reason that this particular kinase is so broadly applicable in several diseases is that it is a constitutively active protein kinase with nearly 40 protein substrates and is regulated by the Wnt, insulin, and brain-derived neurotropic factor (BDNF) signaling pathways. 10 Activated GSK3β has been found at high levels in the brains of AD patients and has been implicated in improper amyloid precursor protein (APP) processing and impaired lysosomal clearance of protein.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Discovery of GSK3β Inhibitors

et al. 2020

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Alzheimer’s disease (AD) is the most common cause of dementia, affecting approximately 35 million people worldwide. The current treatment options for people with AD consist of drugs designed to slow the rate of decline in memory and cognition, but these treatments are not curative, and patients eventually suffer complete cognitive injury. With the substantial amounts of published data on targets for this disease, we proposed that machine learning software could be used to find novel small-molecule treatments that can supplement the AD drugs currently on the market. In order to do this, we used publicly available data in ChEMBL to build and validate Bayesian machine learning models for AD target proteins. The first AD target that we have addressed with this method is the serine–threonine kinase glycogen synthase kinase 3 beta (GSK3β), which is a proline-directed serine–threonine kinase that phosphorylates the microtubule-stabilizing protein tau. This phosphorylation prompts tau to dissociate from the microtubule and form insoluble oligomers called paired helical filaments, which are one of the components of the neurofibrillary tangles found in AD brains. Using our Bayesian machine learning model for GSK3β consisting of 2368 molecules, this model produced a five-fold cross validation ROC of 0.905. This model was also used for virtual screening of large libraries of FDA-approved drugs and clinical candidates. Subsequent testing of selected compounds revealed a selective small-molecule inhibitor, ruboxistaurin, with activity against GSK3β (avg IC 50 = 97.3 nM) and GSK3α (IC 50 = 695.9 nM). Several other structurally diverse inhibitors were also identified. We are now applying this machine learning approach to additional AD targets to identify approved drugs or clinical trial candidates that can be repurposed as AD therapeutics. This represents a viable approach to accelerate drug discovery and do so at a fraction of the cost of traditional high throughput screening.

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Section: Introductionmentioning

confidence: 99%

Machine Learning for Discovery of GSK3β Inhibitors

et al. 2020

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“…[59] GSK3B is a known drug target for AD, which regulates several different cellular processes, and GSK3B dysregulation has been implicated in the pathogenesis of both AD. [60,61] Some investigators found that curcumin may inhibit Aβ-induced tau hyperphosphorylation involving PTEN/Akt/GSK-3β pathway, holding great potential in improving targeted drug delivery and the recovery of neuronal function in AD therapy. [62,63] NFKB1 is a transcriptional regulator that can be activated by various intracellular and extracellular stimuli, such as cytokines, oxidative radicals, UV irradiation, and bacterial or viral products.…”

Section: Discussionmentioning

confidence: 99%

A network pharmacology approach to identify the mechanisms and molecular targets of curcumin against Alzheimer disease

Wu¹,

Zheng²,

Tang³

et al. 2022

Medicine

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Background: Alzheimer disease (AD) is a degenerative brain disease, which may lead to severe memory loss and other cognitive disorders. However, few effective drugs are available in the clinic at present. Curcumin, a major ingredient of traditional Chinese medicine, Curcuma Longa, has various pharmacological activities. Therefore, exploring clinical drugs based on the inhibition of AD pathological features is imperative. Methods: First, we utilized the HERB database and Swisstarget Prediction database to get the related targets of curcumin and intersected with the AD targets. The intersection targets were used to construct the protein-protein interaction network and performed gene ontology and kyoto encyclopedia of genes and genomes analyses. Further, we obtained targets of curcumin against AD-related tau and aβ pathology via the AlzData database. These targets were applied to perform GEO and receiver operating characteristic analyses. Finally, the reliability of the core targets was evaluated using molecular docking technology. Results: We identified 49 targets of curcumin against AD, and kyoto encyclopedia of genes and genomes pathway enrichment analysis demonstrated that the Alzheimer disease pathway (has05010) was significantly enriched. Even more, we obtained 16 targets of curcumin-related Aβ and tau pathology. Among these targets, 8 targets involved the Alzheimer disease pathway and the biological process analyses showed that positive regulation of cytokine production (GO:0001819) was significantly enriched. Bioinformatic analyses indicated that HMOX1, CSF1R, NFKB1, GSK3B, BACE1, AR, or PTGS1 expression was significantly different compared to the control group in the AD patients. Finally, molecular docking studies suggested these genes have a good binding force with curcumin. Conclusions: In this study, we identified curcumin exerted the effect of treating AD by regulating multitargets and multichannels through the method of network pharmacology.

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“…GSK3B has been shown to be frequently up-regulated in many types of cancer ( Darrington et al, 2012 ; T. Zhang et al, 2019 ), and inhibition of it was considered efficient in suppressing tumor growth ( Edderkaoui et al, 2018 ; Wu et al, 2019 ). Moreover, there have been many studies on GSK3B inhibitors, including Metavert molecule in PAAD ( Edderkaoui et al, 2018 ), BT-000775 molecule in BRCA ( Ogunleye et al, 2019 ), BIO molecule in TNBCs (triple-negative breast cancers) ( Vijay et al, 2019 ), AR-A014418 and SB-216763 molecules in STSs (soft tissue sarcomas) ( Abe et al, 2020 ), etc.…”

Section: Discussionmentioning

confidence: 99%

m5C RNA Methylation Primarily Affects the ErbB and PI3K–Akt Signaling Pathways in Gastrointestinal Cancer

Xiang

Shen

et al. 2020

Front. Mol. Biosci.

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5-Methylcytosine (m5C) is a kind of methylation modification that occurs in both DNA and RNA and is present in the highly abundant tRNA and rRNA. It has an important impact on various human diseases including cancer. The function of m5C is modulated by regulatory proteins, including methyltransferases (writers) and special binding proteins (readers). This study aims at comprehensive study of the m5C RNA methylation-related genes and the main pathways under m5C RNA methylation in gastrointestinal (GI) cancer. Our result showed that the expression of m5C writers and reader was mostly up-regulated in GI cancer. The NSUN2 gene has the highest proportion of mutations found in GI cancer. Importantly, in liver cancer, higher expression of almost all m5C regulators was significantly associated with lower patient survival rate. In addition, the expression level of m5C-related genes is significantly different at various pathological stages. Finally, we have found through bioinformatics analysis that m5C regulatory proteins are closely related to the ErbB/PI3K–Akt signaling pathway and GSK3B was an important target for m5C regulators. Besides, the compound termed streptozotocin may be a key candidate drug targeting on GSK3B for molecular targeted therapy in GI cancer.

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Molecular docking based screening analysis of GSK3B

Cited by 8 publications

References 25 publications

Machine Learning for Discovery of GSK3β Inhibitors

Machine Learning for Discovery of GSK3β Inhibitors

A network pharmacology approach to identify the mechanisms and molecular targets of curcumin against Alzheimer disease

m5C RNA Methylation Primarily Affects the ErbB and PI3K–Akt Signaling Pathways in Gastrointestinal Cancer

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