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首页> 外文期刊>Medicinal chemistry >In silico Screening for Identification of Novel Anti-malarial Inhibitors by Molecular Docking, Pharmacophore Modeling and Virtual Screening
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In silico Screening for Identification of Novel Anti-malarial Inhibitors by Molecular Docking, Pharmacophore Modeling and Virtual Screening

机译：通过分子对接，药效团建模和虚拟筛选进行计算机筛选以鉴定新型抗疟疾抑制剂

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Objective: Drug resistance from affordable drugs has increased the number of deaths from malaria globally. This problem has raised the requirement to design new drugs against multi-drug-resistant Plasmodium falciparum parasite. Methods: In the current project, we have focused on four important proteins of Plasmodium falciparum and used them as receptors against a dataset of four anti-malarial drugs. In silico analysis of these receptors and ligand dataset was carried out using Autodock 4.2. A pharmacophore model was also established using Ligandscout. Results: Analysis of docking experiments showed that all ligands bind efficiently to four proteins of Plasmodium falciparum. We have used ligand-based pharmacophore modeling and developed a pharmacophore model that has three hydrophobic regions, two aromatic rings, one hydrogen acceptor and one hydrogen donor. Using this pharmacophore model, we have screened a library of 50,000 compounds. The compounds that shared features of our pharmacophore model and exhibited interactions with the four proteins of our receptors dataset are short-listed. Conclusion: As there is a need of more anti-malarial drugs, therefore, this research will be helpful in identifying novel anti-malarial drugs that exhibited bindings with four important proteins of Plasmodium falciparum. The hits obtained in this study can be considered as useful leads in anti-malarial drug discovery.
机译：目标：负担得起的药物产生的耐药性在全球范围内增加了死于疟疾的人数。这个问题提出了设计针对多药耐药性恶性疟原虫的新药的要求。方法：在当前项目中，我们集中研究了恶性疟原虫的四种重要蛋白质，并将其用作针对四种抗疟疾药物数据集的受体。使用Autodock 4.2对这些受体和配体数据集进行计算机分析。还使用Ligandscout建立了药效团模型。结果：对接实验的分析表明，所有配体均与恶性疟原虫的四种蛋白质有效结合。我们已经使用了基于配体的药效团模型，并开发了具有三个疏水区域，两个芳香环，一个氢受体和一个氢供体的药效团模型。使用该药效团模型，我们筛选了50,000种化合物的库。入选了具有我们药效团模型特征并与我们的受体数据集的四种蛋白质相互作用的化合物。结论：由于需要更多的抗疟药，因此，这项研究将有助于鉴定出与恶性疟原虫的四个重要蛋白结合的新型抗疟药。在这项研究中获得的命中可以被认为是抗疟药物发现中的有用线索。

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