Network pharmacology of bioactives from Sorghum bicolor with targets related to diabetes mellitus

2021

Biology

Self Cite

Zanthoxylum piperitum fruits (ZPFs) have been demonstrated favorable clinical efficacy on rheumatoid arthritis (RA), but its compounds and mechanisms against RA have not been elucidated. This study was to investigate the compounds and mechanisms of ZPFs to alleviate RA via network pharmacology. The compounds from ZPFs were detected by gas chromatography–mass spectrometry (GC-MS) and screened to select drug-likeness compounds through SwissADME. Targets associated with bioactive compounds or RA were identified utilizing bioinformatics databases. The signaling pathways related to RA were constructed; interactions among targets; and signaling pathways-targets-compounds (STC) were analyzed by RPackage. Finally, a molecular docking test (MDT) was performed to validate affinity between targets and compounds on key signaling pathway(s). GC-MS detected a total of 85 compounds from ZPFs, and drug-likeness properties accepted all compounds. A total of 216 targets associated with compounds 3377 RA targets and 101 targets between them were finally identified. Then, a bubble chart exhibited that inactivation of MAPK (mitogen-activated protein kinase) and activation of PPAR (peroxisome proliferator-activated receptor) signaling pathway might be key pathways against RA. Overall, this work suggests that seven compounds from ZPFs and eight targets might be multiple targets on RA and provide integrated pharmacological evidence to support the clinical efficacy of ZPFs on RA.

Section: Introductionmentioning

confidence: 99%

Uncovering Mechanisms of Zanthoxylum piperitum Fruits for the Alleviation of Rheumatoid Arthritis Based on Network Pharmacology

2021

Biology

Self Cite

“…The ligand molecules were docked with target proteins utilizing autodock4 by setting-up 4 energy range and 8 exhaustiveness as default to obtain 10 different poses of ligand molecules [26]. The 2D binding interactions were identi ed through LigPlot+ v.2.2 (https://www.ebi.ac.uk/thorntonsrv/software/LigPlus/).…”

Section: Potential Key Compounds -Target Protein Docking Simulation Smentioning

confidence: 99%

“…The relative peak area of each compound in the chromatogram was calculated on each compound percentage. ChemStation integrater algorithms used as the concept of integration [26].…”

Section: Gc-ms Conditionmentioning

confidence: 99%

See 1 more Smart Citation

A network pharmacology analysis on drug-like compounds from Ganoderma lucidum for alleviation of Atherosclerosis

2021

Preprint

Self Cite

Background: Ganoderma lucidum (GL) is known as a potent alleviator against chronic inflammatory disease like atherosclerosis (AS), but its critical bioactive compounds and their mechanisms against AS have not been unveiled. This research aimed to identify the key compounds(s) and mechanism(s) of GL against AS through network pharmacology.Methods: The compounds from GL were identified by gas chromatography-mass spectrum (GC-MS), and SwissADME screened their physicochemical properties. Then, the gene(s) associated with the screened compound(s) or AS related genes were identified by public databases, and we selected the overlapping genes using a Venn diagram. The networks between overlapping genes and compounds were visualized, constructed, and analyzed by RStudio. Finally, we performed molecular docking test (MDT) to identify key gene(s), compound(s) on AutoDockVina.Results: A total of 35 compounds in GL was detected via GC-MS, and 34 compounds (accepted by the Lipinski's rule) were selected as drug-like compounds (DLCs). A total of 34 compounds were connected to the number of 785 genes and 2,606 AS-related genes were identified by DisGeNET and Online Mendelian Inheritance in Man (OMIM). The final 98 overlapping genes were extracted between the compounds-genes network and AS-related genes. On Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, the number of 27 signaling pathways were sorted out, and a hub signaling pathway (MAPK signaling pathway), a core gene (PRKCA), and a key compound (Benzamide, 4-acetyl-N-(2,6-dimethylphenyl)) were selected among the 27 signaling pathways via MDT. Conclusion: Overall, we found that the identified 3 DLCs from GL have potent anti-inflammatory efficacy, improving AS by inactivating the MAPK signaling pathway.

“…NP is a collective, systemic, and holistic approach to investigate the relation of molecule(s) and target (s), nd the optimal molecule(s) on target protein(s), and provide a crucial hint for identifying the mechanism of a potential lead molecule(s) [25][26][27] . Moreover, Zhang B. et al described that NP accelerates the decoding TCM (Traditional Chinese Medicine) from an empirical-based therapy to an evidence-based therapy system, which improves modern drug discovery strategies 28 .…”

Section: Introductionmentioning

confidence: 99%

A Hub Signaling Pathway of Antimicrobial-Antifungal-Anticancer Peptides Axis With Cationic Residue Amino Acids on N, C- Terminals Under 500 Dalton Rule Via Network Pharmacology

2021

Preprint

Self Cite

Background: Short cationic peptides (SCPs) with therapeutic efficacy of Antimicrobial peptides (AMPs), Antifungal peptides (AFPs), and Anticancer peptides (ACPs) are known as enhancement of host defense system. Here, we investigated the uppermost peptide(s), hub signaling pathway(s), and its associated target(s) through network pharmacology. Method: Firstly, we selected SCPs with positive amino acid residues on N-, C- terminals under 500 Dalton via RStudio. Secondly, EMBOSS pepstats, PASTA 2.0 and Aggrescan were used to remove non- AMPs, after that, ADAM, dbAMP, DBAASP v3.0, and MLAMP were utilized for AMPs selection. AMPs-targets were identified from both SEA and STP databases. The overlapping targets between the bacteria-responsive targets (TTD and OMIM) and AMPs-targets were visualized by VENNY 2.1. Thirdly, AFPs were filtered through Antifp tool, and TTD and OMIM selected fungal responsive targets. The overlapping targets between AFPs-targets and fungal-responsive targets were visualized by VENNY 2.1. Fourthly, the overlapping targets between cancer-related targets (TTD and OMIM) and fungal-responsive targets were visualized by VENNY 2.1. Fifthly, signaling pathway analysis of overlapping targets was performed via RStudio. Finally, molecular docking study (MDS) was carried out to discover the most potent peptides on a hub signaling pathway. Results: A total of 1,833 SCPs were identified, and AMPs, AFPs, and ACPs filtration suggested that 197 peptides-30 targets, 81 peptides-6 targets, and 59 peptides-4 targets are connected, respectively. The AMPs-AFPs-ACPs axis indicated that 27 peptides-2 targets are associated. Each hub signaling pathway for enhancement of host defense system was "Inactivation of Rap1 signaling pathway on AMPs", "Activation of Notch signaling pathway on AMPs-AFPs axis", and "Inactivation of HIF-1 signaling pathway on AMPs-AFPs-ACPs axis". The most potent peptides were assessed via MDS; finally, HPIK on STAT3, HVTK on NOS2 manifested the HIF-1 signaling pathway's highest affinity. Furthermore, the two peptides have better affinity scores than standard selective inhibitors (Stattic, 1400W). Conclusion: Overall, the most potent SCPs for the host defense system were HPIK on STAT3 and HVTK on NOS2, which might inactivate the HIF-1 signaling pathway.