New natural compound inhibitors of PDGFRA (platelet-derived growth factor receptor α) based on computational study for high−grade glioma therapy

Yang, Wenzhuo; Wang, Shengnan; Zhang, Xiangmao; Zhang, Menghan; Chen, Hongyu; Cui, Junxiang; Li, Jinyang; Peng, Fei; Zhu, Mingqin; Yu, Bin; Li, Yifan; Yang, Liu; Min, Wanwan; Xue, Mengru; Lin, Pei; Zhu, Hai‐Liang; Wu, Bo; Gu, Yinghao

doi:10.3389/fnins.2022.1060012

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…Studies on molecular docking of ligands and proteins were conducted using the CDOCKER module of Discovery Studio 2019. Using the CHARMm force field, CDOCKER is a method that generates very accurate molecular docking statistics ( Wu et al, 2021 ; Yang et al, 2022 ). During docking, the CDOCKER algorithm is based on a simulated annealing protocol where the receptor remains rigid and the ligand is allowed to bend and dock with protein residues within the binding site to find the most suitable binding mode ( Li H. et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Development of an MCL-1-related prognostic signature and inhibitors screening for glioblastoma

et al. 2023

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Introduction: The effect of the conventional treatment methods of glioblastoma (GBM) is poor and the prognosis of patients is poor. The expression of MCL-1 in GBM is significantly increased, which shows a high application value in targeted therapy. In this study, we predicted the prognosis of glioblastoma patients, and therefore constructed MCL-1 related prognostic signature (MPS) and the development of MCL-1 small molecule inhibitors.Methods: In this study, RNA-seq and clinical data of 168 GBM samples were obtained from the TCGA website, and immunological analysis, differential gene expression analysis and functional enrichment analysis were performed. Subsequently, MCL-1-associated prognostic signature (MPS) was constructed and validated by LASSO Cox analysis, and a nomogram was constructed to predict the prognosis of patients. Finally, the 17931 small molecules downloaded from the ZINC15 database were screened by LibDock, ADME, TOPKAT and CDOCKER modules and molecular dynamics simulation in Discovery Studio2019 software, and two safer and more effective small molecule inhibitors were finally selected.Results: Immunological analysis showed immunosuppression in the MCL1_H group, and treatment with immune checkpoint inhibitors had a positive effect. Differential expression gene analysis identified 449 differentially expressed genes. Build and validate MPS using LASSO Cox analysis. Use the TSHR HIST3H2A, ARGE OSMR, ARHGEF25 build risk score, proved that low risk group of patients prognosis is better. Univariate and multivariate analysis proved that risk could be used as an independent predictor of patient prognosis. Construct a nomogram to predict the survival probability of patients at 1,2,3 years. Using a series of computer-aided techniques, two more reasonable lead compounds ZINC000013374322 and ZINC000001090002 were virtually selected. These compounds have potential inhibitory effects on MCL-1 and provide a basis for the design and further development of MCL-1 specific small molecule inhibitors.Discussion: This study analyzed the effect of MCL-1 on the prognosis of glioblastoma patients from the perspective of immunology, constructed a new prognostic model to evaluate the survival rate of patients, and further screened 2 MCL-1 small molecule inhibitors, which provides new ideas for the treatment and prognosis of glioblastoma.

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Section: Methodsmentioning

confidence: 99%

Development of an MCL-1-related prognostic signature and inhibitors screening for glioblastoma

et al. 2023

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Repurposing Antipsychotic Agents Against Targets of Angiogenesis Pathways for Cancer Therapy: An in-silico Approach

Kanmodi,

Bankole,

Oddiri

et al. 2023

CDDT

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Background: Antipsychotics interfere with virtually all hallmarks of cancer, including angiogenesis. Vascular endothelial growth factor receptors (VEGFRs) and platelet-derived growth receptors (PDGFRs) play crucial roles in angiogenesis and represent targets of many anti-cancer agents. We assessed and compared the binding effects of antipsychotics and receptor tyrosine kinase inhibitors (RTKIs) on VEGFR2 and PDGFRα. Methods: FDA-approved antipsychotics and RTKIs were retrieved from DrugBank. VEGFR2 and PDGFRα structures were obtained from Protein Data Bank and loaded on Biovia Discovery Studio software to remove nonstandard molecules. Molecular docking was carried out using PyRx and CB-Dock to determine the binding affinities of protein-ligand complexes. Results: Risperidone exerted the highest binding effect on PDGFRα (-11.0 Kcal/mol) as compared to other antipsychotic drugs and RTKIs. Risperidone also demonstrated a stronger binding effect on VEGFR2 (-9.6 Kcal/mol) than the RTKIs, pazopanib (-8.7 Kcal/mol), axitinib (-9.3 Kcal/mol), vandetanib (-8.3 Kcal/mol), lenvatinib ( -7.6 Kcal/mol) and sunitinib (-8.3 Kcal/mol). Sorafenib (an RTKI), however, exhibited the highest VEGFR2 binding affinity of 11.7 Kcal/mol. Conclusion: Risperidone's superior binding affinity with PDGFRα when compared to all reference RTKIs and antipsychotic drugs, as well as its stronger binding effect on VEGFR2 over the RTKIs, sunitinib, pazopanib, axitinib, vandetanib, and lenvatinib, imply that it could be repurposed to inhibit angiogenic pathways and subjected to pre-clinical and clinical trials for cancer therapy.

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Integrative analysis of single-cell transcriptomic and multilayer signaling networks in glioma reveal tumor progression stage

Atanaki,

Mirsadeghi,

Manesh

et al. 2024

Front. Genet.

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IntroductionTumor microenvironments (TMEs) encompass complex ecosystems of cancer cells, infiltrating immune cells, and diverse cell types. Intercellular and intracellular signals within the TME significantly influence cancer progression and therapeutic outcomes. Although computational tools are available to study TME interactions, explicitly modeling tumor progression across different cancer types remains a challenge.MethodsThis study introduces a comprehensive framework utilizing single-cell RNA sequencing (scRNA-seq) data within a multilayer network model, designed to investigate molecular changes across glioma progression stages. The heterogeneous, multilayered network model replicates the hierarchical structure of biological systems, from genetic building blocks to cellular functions and phenotypic manifestations.ResultsApplying this framework to glioma scRNA-seq data allowed complex network analysis of different cancer stages, revealing significant ligand‒receptor interactions and key ligand‒receptor-transcription factor (TF) axes, along with their associated biological pathways. Differential network analysis between grade III and grade IV glioma highlighted the most critical nodes and edges involved in interaction rewiring. Pathway enrichment analysis identified four essential genes—PDGFA (ligand), PDGFRA (receptor), CREB1 (TF), and PLAT (target gene)—involved in the Receptor Tyrosine Kinases (RTK) signaling pathway, which plays a pivotal role in glioma progression from grade III to grade IV.DiscussionThese genes emerged as significant features for machine learning in predicting glioma progression stages, achieving 87% accuracy and 93% AUC in a 3-year survival prediction through Kaplan-Meier analysis. This framework provides deeper insights into the cellular machinery of glioma, revealing key molecular relationships that may inform prognosis and therapeutic strategies.

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New natural compound inhibitors of PDGFRA (platelet-derived growth factor receptor α) based on computational study for high−grade glioma therapy

Cited by 4 publications

References 33 publications

Development of an MCL-1-related prognostic signature and inhibitors screening for glioblastoma

Development of an MCL-1-related prognostic signature and inhibitors screening for glioblastoma

Repurposing Antipsychotic Agents Against Targets of Angiogenesis Pathways for Cancer Therapy: An in-silico Approach

Integrative analysis of single-cell transcriptomic and multilayer signaling networks in glioma reveal tumor progression stage

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