Dataset Information

Exploring Potentilla nepalensis Phytoconstituents: Integrated Strategies of Network Pharmacology, Molecular Docking, Dynamic Simulations, and MMGBSA Analysis for Cancer Therapeutic Targets Discovery.

ABSTRACT: Potentilla nepalensis belongs to the Rosaceae family and has numerous therapeutic applications as potent plant-based medicine. Forty phytoconstituents (PCs) from the root and stem through n-hexane (NR and NS) and methanolic (MR and MS) extracts were identified in earlier studies. However, the PCs affecting human genes and their roles in the body have not previously been disclosed. In this study, we employed network pharmacology, molecular docking, molecular dynamics simulations (MDSs), and MMGBSA methodologies. The SMILES format of PCs from the PubChem was used as input to DIGEP-Pred, with 764 identified as the inducing genes. Their enrichment studies have shown inducing genes' gene ontology descriptions, involved pathways, associated diseases, and drugs. PPI networks constructed in String DB and network topological analyzing parameters performed in Cytoscape v3.10 revealed three therapeutic targets: TP53 from MS-, NR-, and NS-induced genes; HSPCB and Nf-kB1 from MR-induced genes. From 40 PCs, two PCs, 1b (MR) and 2a (MS), showed better binding scores (kcal/mol) with p53 protein of -8.6 and -8.0, and three PCs, 3a, (NR) 4a, and 4c (NS), with HSP protein of -9.6, -8.7, and -8.2. MDS and MMGBSA revealed these complexes are stable without higher deviations with better free energy values. Therapeutic targets identified in this study have a prominent role in numerous cancers. Thus, further investigations such as in vivo and in vitro studies should be carried out to find the molecular functions and interlaying mechanism of the identified therapeutic targets on numerous cancer cell lines in considering the PCs of P. nepalensis.

SUBMITTER: Praveen M

PROVIDER: S-EPMC10819299 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Exploring <i>Potentilla nepalensis</i> Phytoconstituents: Integrated Strategies of Network Pharmacology, Molecular Docking, Dynamic Simulations, and MMGBSA Analysis for Cancer Therapeutic Targets Discovery.

Praveen Mallari M Ullah Ihsan I Buendia Ricardo R Khan Imran Ahmad IA Sayed Mian Gul MG Kabir Rahmul R Bhat Mashooq Ahmad MA Yaseen Muhammad M

Pharmaceuticals (Basel, Switzerland) 20240119 1

<i>Potentilla nepalensis</i> belongs to the Rosaceae family and has numerous therapeutic applications as potent plant-based medicine. Forty phytoconstituents (PCs) from the root and stem through n-hexane (NR and NS) and methanolic (MR and MS) extracts were identified in earlier studies. However, the PCs affecting human genes and their roles in the body have not previously been disclosed. In this study, we employed network pharmacology, molecular docking, molecular dynamics simulations (MDSs), an ...[more]

PMID: 38276007

Similar Datasets

Project description:Cancer is one of the predominant causes of death worldwide. The new trend nowadays is to exploit natural products with the hope of developing new anticancer agents with fewer side effects. Propolis is one of these natural products which showed effectiveness in cancer treatment. The aim of this study is to understand the multi-level mechanism of action of propolis constituents in cancer treatment using an integrated approach of network pharmacology-based analysis, molecular docking and in vitro cytotoxicity testing. An inhouse database of chemical constituents from Egyptian propolis was compiled and assessed for its ADME properties using the QikProp module in the Schrodinger software. STITCH, UniProt, STRING, KEGG and DAVID databases were used for construction of constituent-target gene, gene-pathway, and constituent-target gene-pathway networks with the aid of Cytoscape 3.8.2. The network pharmacology-based analysis showed that the hit propolis constituents related to cancer targets were genistein, luteolin, benzoic acid, quercetin and vanillic acid, whereas the main cancer-associated targets were CYP1A1, CYP19A1, ESR1, NOS3, CASP3 and AKT1. Twenty-four cancer-related pathways were recognized where the most enriched ones were pathways in cancer and estrogen signaling pathway. The most enriched biological processes involved in the mechanism of action of propolis constituents in cancer treatment were negative regulation of the apoptotic process and the metabolic process and negative regulation of cellular glucuronidation. Molecular docking analysis of the top hit compounds against the most enriched target proteins in the constructed networks was carried out using the Maestro interface of the Schrodinger software. Among hit compounds, quercetin and genistein exhibited the most stabilized interaction. Finally, confirmation of the potential anticancer activity of propolis was assured by in vitro cytotoxicity testing of propolis extract on human prostate cancer (DU-145), breast adenocarcinoma (MCF-7) and colorectal adenocarcinoma (Caco-2) cell lines. This study presents deeper insights about propolis molecular mechanisms of action in cancer for the first time using an integrated approach of network pharmacology, molecular docking and in vitro testing.

Project description:BackgroundDiabetes has become a serious global public health problem. With the increasing prevalence of type 2 diabetes mellitus (T2DM), the incidence of complications of T2DM is also on the rise. Sitagliptin, as a targeted drug of DPP4, has good therapeutic effect for T2DM. It is well known that sitagliptin can specifically inhibit the activity of DPP4 to promote insulin secretion, inhibit islet β cell apoptosis and reduce blood glucose levels, while other pharmacological mechanisms are still unclear, such as improving insulin resistance, anti-inflammatory, anti-oxidative stress, and anti-fibrosis. The aim of this study was to explore novel targets and potential signaling pathways of sitagliptin for T2DM.MethodsFirstly, network pharmacology was applied to find the novel target most closely related to DPP4. Semi-flexible molecular docking was performed to confirm the binding ability between sitagliptin and the novel target, and molecular dynamics simulation (MD) was carried to verify the stability of the complex formed by sitagliptin and the novel target. Furthermore, surface-plasmon resonance (SPR) was used to explored the affinity and kinetic characteristics of sitagliptin with the novel target. Finally, the molecular mechanism of sitagliptin for T2DM was predicted by the enrichment analysis of GO function and KEGG pathway.ResultsIn this study, we found the cell surface receptor-angiotensin-converting enzyme 2 (ACE2) most closely related to DPP4. Then, we confirmed that sitagliptin had strong binding ability with ACE2 from a static perspective, and the stability of sitagliptin-ACE2 complex had better stability and longer binding time than BAR708-ACE2 in simulated aqueous solution within 50 ns. Significantly, we have demonstrated a strong affinity between sitagliptin and ACE2 on SPR biosensor, and their kinetic characteristics were "fast binding/fast dissociation". The guiding significance of clinical administration: low dose can reach saturation, but repeated administration was needed. Finally, there was certain relationship between COVID-19 and T2DM, and ACE2/Ang-(1-7)/Mas receptor (MasR) axis may be the important pathway of sitagliptin targeting ACE2 for T2DM.ConclusionThis study used different methods to prove that ACE2 may be another novel target of sitagliptin for T2DM, which extended the application of ACE2 in improving diabetes mellitus.

Project description:The treatment of cognitive impairment in schizophrenia is an unaddressed need due to the absence of novel treatments. Recent studies demonstrated that fingolimod and siponimod have neuroprotective effects in several neuropsychiatric disorders; however, their pharmacological mechanisms are unclear. The objective of this study was to identify potential molecular mechanisms of fingolimod and siponimod for improving cognition of schizophrenia through network pharmacology and molecular docking. The putative target genes of ingredients, schizophrenia, and impaired cognition were obtained from online databases, including SwissTargetPrediction, PharmMapper, GeneCards, CTD, DisGeNET, and OMIM. A protein-protein interaction network was constructed to identify core targets. The DAVID database was used for GO and KEGG pathway enrichment analyses. An ingredient-target-pathway-disease network was constructed using Cytoscape. Finally, the interactions between ingredients and core targets were assessed with molecular docking. The analysis revealed 260 targets shared by fingolimod and siponimod, 257 unique targets for fingolimod, and 88 unique targets for siponimod. Two signaling pathways were involved in fingolimod-mediated improvements in the cognition of schizophrenia, including the PI3K-Akt and MAPK signaling pathways. The core targets that regulated these two pathways included IL1B, AKT1, TNF, IL6, INS, BCL2, and BDNF. The MAPK signaling pathway was involved in siponimod-mediated improvement in the cognition of schizophrenia. The MAPK pathway was regulated by three core targets, namely TNF, AKT1, and CASP3. Docking scores ranged from -5.0 to -10.4 kcal/mol. Our analysis revealed that fingolimod regulates the PI3K-Akt and MAPK signaling pathways via the core targets IL1B, AKT1, TNF, IL6, INS, BCL2, and BDNF, and siponimod regulates the MAPK signaling pathways via the core targets AKT1, TNF, and CASP3 to improve the cognition of schizophrenia. Our results provide potential targets and a theoretical basis for the design of new drugs to treat the impaired cognition of schizophrenia.

Dataset Information

Exploring Potentilla nepalensis Phytoconstituents: Integrated Strategies of Network Pharmacology, Molecular Docking, Dynamic Simulations, and MMGBSA Analysis for Cancer Therapeutic Targets Discovery.

Publications

Exploring <i>Potentilla nepalensis</i> Phytoconstituents: Integrated Strategies of Network Pharmacology, Molecular Docking, Dynamic Simulations, and MMGBSA Analysis for Cancer Therapeutic Targets Discovery.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets