Molecular docking studies of angiogenesis target protein HIF-1α and genistein in breast cancer

Mukund, Vidya; Saddala, Madhu Sudhana; Farran, Batoul; Mannavarapu, Mastan; Alam, Afroz; Nagaraju, Ganji Purnachandra

doi:10.1016/j.gene.2019.03.062

Cited by 44 publications

(29 citation statements)

References 14 publications

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“…The response to hypoxia is mainly regulated by HIF-1α in tumors, and HIF-1α is involved in tumorigenesis, growth, and metastasis in BC. 38,39 Furthermore, HIF-1α directly binds to and upregulates the expression of TWIST-1. 18,19,40 In addition, increased matrix stiffness activates and upregulates the expression of TWIST-1 in BC.…”

Section: Discussionmentioning

confidence: 99%

<p>HIF-1α, TWIST-1 and ITGB-1, associated with Tumor Stiffness, as Novel Predictive Markers for the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer</p>

Zhang

Gao

et al. 2020

CMAR

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Purpose: To investigate the relationship between hypoxia-inducible factor 1-alpha (HIF-1α), Twist family BHLH transcription factor 1 (TWIST-1), and β1 integrin (ITGB-1) expression and tumor stiffness, and evaluate performance of HIF-1α, TWIST-1, and ITGB-1 alone and in combination with Ki-67 for predicting pathological responses to neoadjuvant chemotherapy (NACT) in breast cancer (BC). Patients and Methods: This was a prospective cohort study of 104 BC patients receiving NACT. Tumor stiffness and oxygen score (OS) were evaluated before NACT by shear-wave elastography and optical imaging; HIF-1α, TWIST-1, ITGB-1, and Ki-67 expression were quantitatively assessed by immunohistochemistry of paraffin-embedded tumor samples obtained by core needle biopsy. Indexes were compared among different residual cancer burden (RCB) groups, and associations of HIF-1α, TWIST-1, ITGB-1, and Ki-67 with tumor stiffness and OS were examined. The value of HIF-1α, TWIST-1, ITGB-1, and Ki-67, and a possible new combined index (predRCB) for predicting NACT responses was assessed by receiver operating characteristic (ROC) curves. Results: HIF-1α, TWIST-1, and ITGB-1 expression were positively correlated with tumor stiffness and negatively with OS. Area under the ROC curves (AUCs) measuring the performance of HIF-1α, TWIST-1, ITGB-1, and Ki-67 for predicting responses to NACT were 0.81, 0.85, 0.79, and 0.80 for favorable responses, and 0.83, 0.86, 0.84, and 0.85 for resistant responses, respectively. PredRCB showed better prediction than the other individual indexes for favorable responses (AUC = 0.88) and resistant responses (AUC = 0.92). Conclusion: HIF-1α, TWIST-1, ITGB-1, and Ki-67 performed well in predicting favorable responses and resistance to NACT, and predRCB improved the predictive power of the individual indexes. These results support individualized treatment of BC patients receiving NACT.

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Section: Discussionmentioning

confidence: 99%

<p>HIF-1α, TWIST-1 and ITGB-1, associated with Tumor Stiffness, as Novel Predictive Markers for the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer</p>

Zhang

Gao

et al. 2020

CMAR

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“…We uploaded the G6PD protein as input to predict the ligand binding active sites. The CASTp server predicted the key amino acids for binding interactions to the inhibitor/activator [31].…”

Section: Active Site Predictionmentioning

confidence: 99%

Discovery of Small-Molecule Activators for Glucose-6-Phosphate Dehydrogenase (G6PD) Using Machine Learning Approaches

Saddala

Lennikov

2020

IJMS

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Glucose-6-Phosphate Dehydrogenase (G6PD) is a ubiquitous cytoplasmic enzyme converting glucose-6-phosphate into 6-phosphogluconate in the pentose phosphate pathway (PPP). The G6PD deficiency renders the inability to regenerate glutathione due to lack of Nicotine Adenosine Dinucleotide Phosphate (NADPH) and produces stress conditions that can cause oxidative injury to photoreceptors, retinal cells, and blood barrier function. In this study, we constructed pharmacophore-based models based on the complex of G6PD with compound AG1 (G6PD activator) followed by virtual screening. Fifty-three hit molecules were mapped with core pharmacophore features. We performed molecular descriptor calculation, clustering, and principal component analysis (PCA) to pharmacophore hit molecules and further applied statistical machine learning methods. Optimal performance of pharmacophore modeling and machine learning approaches classified the 53 hits as drug-like (18) and nondrug-like (35) compounds. The drug-like compounds further evaluated our established cheminformatics pipeline (molecular docking and in silico ADMET (absorption, distribution, metabolism, excretion and toxicity) analysis). Finally, five lead molecules with different scaffolds were selected by binding energies and in silico ADMET properties. This study proposes that the combination of machine learning methods with traditional structure-based virtual screening can effectively strengthen the ability to find potential G6PD activators used for G6PD deficiency diseases. Moreover, these compounds can be considered as safe agents for further validation studies at the cell level, animal model, and even clinic setting.

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“…Deblandre et al [33] suggested that neurl2 (neuralized-like protein 2) interacts with XDelta1 (xenopus delta1) and regulates Notch signaling. This signaling is involved in pathologic [34,35], which is associated with tumor growth and ischemic stroke [36]. Ding et al [37] suggested that phosphorylation of the plek (pleckstrin) gene increases proinflammatory cytokine secretion by mononuclear phagocytes in diabetes mellitus.…”

Section: Discussionmentioning

confidence: 99%

RNA-Seq reveals differential expression profiles and functional annotation of genes involved in retinal degeneration in Pde6c mutant Danio rerio

et al. 2020

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Background: Retinal degenerative diseases affect millions of people and represent the leading cause of vision loss around the world. Retinal degeneration has been attributed to a wide variety of causes, such as disruption of genes involved in phototransduction, biosynthesis, folding of the rhodopsin molecule, and the structural support of the retina. The molecular pathogenesis of the biological events in retinal degeneration is unclear; however, the molecular basis of the retinal pathological defect can be potentially determined by gene-expression profiling of the whole retina. In the present study, we analyzed the differential gene expression profile of the retina from a wild-type zebrafish and phosphodiesterase 6c (pde6c) mutant. Results: The datasets were downloaded from the Sequence Read Archive (SRA), and adaptors and unbiased bases were removed, and sequences were checked to ensure the quality. The reads were further aligned to the reference genome of zebrafish, and the gene expression was calculated. The differentially expressed genes (DEGs) were filtered based on the log fold change (logFC) (±4) and p-values (p < 0.001). We performed gene annotation (molecular function [MF], biological process [BP], cellular component [CC]), and determined the functional pathways Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway for the DEGs. Our result showed 216 upregulated and 3527 downregulated genes between normal and pde6c mutant zebrafish. These DEGs are involved in various KEGG pathways, such as the phototransduction (12 genes), mRNA surveillance (17 genes), phagosome (25 genes), glycolysis/gluconeogenesis (15 genes), adrenergic signaling in cardiomyocytes (29 genes), ribosome (20 genes), the citrate cycle (TCA cycle; 8 genes), insulin signaling (24 genes), oxidative phosphorylation (20 genes), and RNA transport (22 genes) pathways. Many more of all the pathway genes were down-regulated, while fewer were up-regulated in the retina of pde6c mutant zebrafish. Conclusions: Our data strongly indicate that, among these genes, the above-mentioned pathways' genes as well as calcium-binding, neural damage, peptidase, immunological, and apoptosis proteins are mostly involved in the retinal and neural degeneration that cause abnormalities in photoreceptors or retinal pigment epithelium (RPE) cells.

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Molecular docking studies of angiogenesis target protein HIF-1α and genistein in breast cancer

Cited by 44 publications

References 14 publications

<p>HIF-1α, TWIST-1 and ITGB-1, associated with Tumor Stiffness, as Novel Predictive Markers for the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer</p>

<p>HIF-1α, TWIST-1 and ITGB-1, associated with Tumor Stiffness, as Novel Predictive Markers for the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer</p>

Discovery of Small-Molecule Activators for Glucose-6-Phosphate Dehydrogenase (G6PD) Using Machine Learning Approaches

RNA-Seq reveals differential expression profiles and functional annotation of genes involved in retinal degeneration in Pde6c mutant Danio rerio

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