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Systems pharmacology and molecular docking strategies prioritize natural molecules as cardioprotective agents

机译：系统药理学和分子对接策略优先考虑天然分子作为心脏保护剂

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摘要

Natural compounds can interact with multiple cellular target proteins and may be prioritized as drug leads. There is a need for prioritization of compounds that protect cardiovascular systems from pathological conditions. Here we prioritize morin, veratric acid, piperine, syringic acid, vanillic acid, diosgenin, diosmetin and sinapic acid that were already identified as cardioprotective molecules in our previous studies through multi-level data integration. In this study, initially we predict targets of the above-mentioned compounds by reverse pharmacophore (PharmMapper) and structural similarity based target-screening methods. We also explored the compound-target pathways (Biocarta and KEGG) and disease relationships. Further, we chose public microarray transcriptomic data from GEO to prioritize important pathogenic targets (heart failure, cardiac hypertrophy, vascular dysfunction and atherosclerosis), and we explored the interaction potential of the above compounds on the targets via blind docking (AutoDock Vina). Moreover, the multi target action of compounds was revealed by target information retrieved from large-scale text mining and organized databases (HIT and TCMID). The drug likeness profile and toxicity prediction was achieved based on Lipinski's rule and structural similarity search (ProTox). The observed results have demonstrated that the multi target potential and less toxic nature mean these molecules can be prioritized as lead compounds for cardiovascular diseases.
机译：天然化合物可以与多个细胞靶蛋白相互作用，并且可以优先考虑药物引线。需要优先保护保护心血管系统免受病理条件的化合物。在这里，我们优先考虑Morin，藜酸，哌啶，注射酸，香草酸，Diosgenin，Diosmetin和SINAPIC酸，这些酸已经通过多级数据集成在我们之前的研究中被鉴定为心脏保护分子。在该研究中，最初，我们通过反向药镜（药物涂料）和基于结构相似性的靶筛选方法预测上述化合物的靶标。我们还探索了复合靶途径（Biocarta和Kegg）和疾病关系。此外，我们选择从地理学中的公共微阵列转录组数据，以优先考虑重要的致病靶（心力衰竭，心脏肥大，血管功能障碍和动脉粥样硬化），我们通过盲解码（自动困难vina）探讨了上述化合物对目标的相互作用电位。此外，通过从大规模文本挖掘和有组织的数据库检索的目标信息（命中和TCMID）检索的目标信息，揭示了化合物的多目标作用。基于Lipinski的规则和结构相似性搜索（Protox）实现了药物肖像曲线和毒性预测。所观察结果表明，多目标电位和较小的毒性是指这些分子可以优先考虑作为心血管疾病的铅化合物。

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  • 来源
    《RSC Advances》 |2015年第94期|共14页
  • 作者

    Manivannan Jeganathan; Silambarasan Thangarasu; Kadarkarairaj Rajendran; Raja Boobalan;

  • 作者单位

    Anna Univ AU KBC Res Ctr Vasc Biol Lab Madras 600044 Tamil Nadu India;

    Annamalai Univ Dept Biochem &

    Biotechnol Cardiovasc Biol Lab Annamalainagar 608002 Tamil Nadu India;

    Anna Univ AU KBC Res Ctr Vasc Biol Lab Madras 600044 Tamil Nadu India;

    Annamalai Univ Dept Biochem &

    Biotechnol Cardiovasc Biol Lab Annamalainagar 608002 Tamil Nadu India;

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  • 正文语种 eng
  • 中图分类 化学;
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