Molecular docking based screening analysis of GSK3B

Exploring novel and potent glycogen synthase kinase-3β inhibitors through systematic drug designing approach

Significant implications of glycogen synthase kinase-3β (GSK-3β) have been reported in various neuronal disorders and malignant cancers. GSK-3β modulates diverse protein targets through phosphorylation, and its aberrant activity leads to neurological complications as well as tumour onset. Therefore, inhibiting GSK-3β activity through active-site fitting molecules may offer a favourable strategy for intercepting these disorders. This comprehensive study used multiple assays in tandem in order to explore the most potent GSK-3β inhibitor. Following structural similarity screening, 135 molecular docking and 135 standard MM-GBSA experiments were performed using AZD1080, a known inhibitor as standard. Among the 32 molecules demonstrating a stronger binding affinity than reference, only the two most potent molecules were chosen and their binding free energy was compared to AZD1080 using the Desmond trajectory clustering and eventual MM-GBSA. Additionally, the interaction status of these molecules and AZD1080 with GSK-3β was explored post-molecular dynamics. The stability of the strongest molecule (most potent) was evaluated in the active site of the above-mentioned kinase keeping its apo-form as reference. Notably, the e-Pharmacophores mapping was performed to address the level of complementarity of the most potent molecule and AZD1080 with the functional site of GSK-3β. Using various techniques, we identified the molecule with PubChem CID: 11167509 as the strongest molecule for obstructing GSK-3β, which may serve as a promising therapeutic after the meticulous evaluation on diverse models.

Unveiling key drivers of hepatocellular carcinoma: a synergistic approach with network pharmacology, machine learning-driven ligand discovery and dynamic simulations

Hepatocellular carcinoma (HCC) ranks fourth in cancer-related mortality worldwide. This study aims to uncover the genes and pathways involved in HCC through network pharmacology (NP) and to discover potential drugs via machine learning (ML)-based ligand screening. Additionally, toxicity prediction, molecular docking, and molecular dynamics (MD) simulations were conducted. NP study identified key genes related to HCC, particularly the enzymes AKT1 and GSK3β. Pathway analysis revealed that crucial pathways like PI3K-AKT and WNT signalling play pivotal roles in HCC progression. Using ML, potential inhibitors for AKT1 and GSK3β were identified, including CHEMBL2177361 and CHEMBL403354 for AKT1, and CHEMBL3652546 and CHEMBL4641631 for GSK3β. post-MD analyses, including RMSD, 2D-RMSD, RMSD cluster, RMSF, PCA, DCCM, residence time analysis, diffusion coefficient, autoencoder-based dimensionality reduction, FEL and MM/GBSA were performed to understand the protein-ligand interactions. The present study reveals the stable interactions of the inhibitors with AKT1 and GSK3β. The binding free energies of all the four complexes were -39.9, -46.8, -41.6, and -45.9 kcal/mol, respectively. This research provides novel insights into the genes and pathways involved in the progression and pathogenesis of HCC using bioinformatics tools. Furthermore, ML-based virtual screening identified potent inhibitors against the target proteins of HCC, such as AKT1 and GSK3β.

Transcriptional Regulation Analysis Provides Insight into the Function of GSK3β Gene in Diannan Small-Ear Pig Spermatogenesis

Glycogen synthase kinase-3β (GSK3β) not only plays a crucial role in regulating sperm maturation but also is pivotal in orchestrating the acrosome reaction. Here, we integrated single-molecule long-read and short-read sequencing to comprehensively examine GSK3β expression patterns in adult Diannan small-ear pig (DSE) testes. We identified the most important transcript ENSSSCT00000039364 of GSK3β, obtaining its full-length coding sequence (CDS) spanning 1263 bp. Gene structure analysis located GSK3β on pig chromosome 13 with 12 exons. Protein structure analysis reflected that GSK3β consisted of 420 amino acids containing PKc-like conserved domains. Phylogenetic analysis underscored the evolutionary conservation and homology of GSK3β across different mammalian species. The evaluation of the protein interaction network, KEGG, and GO pathways implied that GSK3β interacted with 50 proteins, predominantly involved in the Wnt signaling pathway, papillomavirus infection, hippo signaling pathway, hepatocellular carcinoma, gastric cancer, colorectal cancer, breast cancer, endometrial cancer, basal cell carcinoma, and Alzheimer's disease. Functional annotation identified that GSK3β was involved in thirteen GOs, including six molecular functions and seven biological processes. ceRNA network analysis suggested that DSE GSK3β was regulated by 11 miRNA targets. Furthermore, qPCR expression analysis across 15 tissues highlighted that GSK3β was highly expressed in the testis. Subcellular localization analysis indicated that the majority of the GSK3β protein was located in the cytoplasm of ST (swine testis) cells, with a small amount detected in the nucleus. Overall, our findings shed new light on GSK3β's role in DSE reproduction, providing a foundation for further functional studies of GSK3β function.

EPS8L3 promotes pancreatic cancer proliferation and metastasis by activating GSK3B

Background

We intended to investigate the role and regulatory mechanism of EPS8L3 in increase the development of pancreatic cancer (PC).

Methods

In order to analyze the relationship between EPS8L3 level and clinicopathological indicators of PC patients, qRT-PCR was used to detect the expression of EPS8L3 in tumor specimens of 40 PC patients. EPS8L3 knockdown models were then constructed in PC cell lines. Furthermore, the effect of EPS8L3 on PC cell function was analyzed by CCK-8 and Transwell assay. Dual luciferase reporter gene assay and recovery assay were used to further investigate the underlying mechanism.

Results

qRT-PCR results indicated that EPS8L3 was highly expressed in PC tissues compared with adjacent ones. At the same time, the incidence of distant metastasis was higher in PC patients with high EPS8L3 level. In vitro analysis such as CCK-8 and Transwell experimentations indicated that knockdown of EPS8L3 markedly inhibited the proliferative and metastatic ability. Bio-informatics together with luciferase report assay proposing that EPS8L3 can target GSK3B. Western Blot results revealed that knockdown of EPS8L3 markedly reduced the GSK3B expression in PC cells, and there was a positively associated between the two in PC cells. In addition, the recovery experimentation proved that EPS8L3 and GSK3B have a mutual regulation effect. Overexpression of GSK3B can reversal the prohibitive effect of EPS8L3 knockdown on the malignant development of PC cells, thereby jointly regulating the occurrence and development of PC.

Conclusions

EPS8L3 promotes the development of PC by regulating GSK3B, suggesting that EPS8L3 can be used as a biomarker for early diagnosis and treatment of PC.

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

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.

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

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.

Machine Learning for Discovery of GSK3β Inhibitors

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 IC50 = 97.3 nM) and GSK3α (IC50 = 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.