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Record W2990537500 · doi:10.5539/ijc.v12n1p61

Molecular Docking Studies to Understand the Potential Role of Ginger Compounds (6-Gingerol and 6-Shogaol) on Anti-Angiogenic and Anti-Lymphangiogenic Mechanisms

2019· article· en· W2990537500 on OpenAlexvenueno aff
Santhoshi Rani Nanchari, Vijayalakshmi Venkatesan

Bibliographic record

VenueInternational Journal of Chemistry · 2019
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicGinger and Zingiberaceae research
Canadian institutionsnot available
FundersIndian Council of Medical Research
KeywordsChemistryLymphangiogenesisAngiogenesisDocking (animal)PharmacologyIn silicoBiochemistryCancer researchMetastasisCancerBiology

Abstract

fetched live from OpenAlex

Background: 6-Gingerol and 6-Shogaol are novel biologically active phenol compounds isolated from rhizomes of Ginger (Zingiber officinale Roscoe), which has a potential role as anti-inflammatory, anti-oxidant and apoptotic. Till date there are no scientific reports on the functional properties of Ginger against the molecular mechanisms of angiogenesis, lymphangiogenesis, and metastasis. Hence, in the present study we have explored the feasibility of active ginger compounds (6-Gingerol and 6-Shogaol) to validate their molecular mechanisms on angiogenesis and lymphangiogenesis in breast cancer progression through in silico approach. Methodology: Studies have been targeted to find the interactions between selected protein receptors, which play a pivotal role in angiogenesis and lymphangiogenesis and ligands of Ginger compounds (6-Gingerol and 6-Shogaol) by using Accelrys discovery studio 2.5, followed by analysis of data. Results: Based on the in silico approaches, we found the best interactions between ginger compounds (6-Gingerol and 6-Shogaol) and targeted protein molecules as shown less than 3.10 A0H-bond distance to indicate higher binding affinity and stronger interactions and high docking scores. We demonstrate docking interactions of 6-Gingerol with the proteins involved in angiogenesis like VEGF-A (3QTK), VEGFR-1 (5ABD), VEGFR-2/VEGF-E COMPLEX (3V6B, Angiopoietin-2 (4JZC), PDGF-B (4QCI), KDR (5EW3) and with the proteins involved in lymphangiogenesis such as VEGF-C(2XIX), VEGF-C in complex with domains of 2 and 3 of VEGFR2 (2X1W), NRP2(4QDS) and Neuropilin-1/VEGF-A complex (4DEQ). Similarly, our data shows that 6-Shogaol also interacts with angiogenic specific proteins, like [VEGF-A (3QTK), VEGFR-1 (5ABD), VEGFR-2/VEGF-E COMPLEX (3V6B), Angiopoietin-2 (4JZC), PDGF-B (4QCI), KDR (5EW3)] and lymphangiogenesis [VEGF-C(2XIX), VEGF-C in complex with domains of 2 and 3 of VEGFR2 (2X1W), NRP2(4QDS) and Neuropilin-1/VEGF-A complex (4DEQ)]. Discussion: In silico approaches suggest a stronger binding affinity between the ginger compounds (6-Gingerol and 6-Shogaol) and selected proteins critical in angiogenesis and lymphangiogenesis. The present study underlines the feasibility of neutraceuticals to target the pathways participating in breast cancer progression through neovascularization. Our results also advocate 6-Gingerol to be more potent inhibitor of lymphangiogenesis assessed by its binding efficacy with VEGF-C and NRP2 (4QDS) as compared against 6-Shogaol.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.414
Teacher spread0.362 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2019
Admission routes1
Has abstractyes

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