探索 SAM-I 核糖开关抑制剂:采用多级 CADD 方法为新靶标发现配体

Nada Elkholy , Reham Hassan , Loay Bedda , Mohamed A. Elrefaiy , Reem K. Arafa
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引用次数: 0

摘要

由于抗生素耐药性的出现,针对核糖开关(负责重要基因表达的调控元件)的研究正成为抗菌药物研发新时代的中心议题。S-腺苷蛋氨酸-I(SAM-I)核糖开关在天然配体 SAM 的调节下,以负反馈的方式终止转录。SAM-I 核糖开关对细菌具有特异性,主要存在于炭疽杆菌等革兰氏阳性细菌中。通过分析 SAM-I 核糖开关适配体与其天然配体 SAM 的共晶体结构的相互作用,明确了实现结合所需的化学结构特征。根据这些特征,我们建立了基于结构和配体的药理模型,用于筛选 OTAVA 化学库和 Pubchem 数据库。为了进一步提高筛选效果,SAM 的理化性质被用作第二个筛选标准。对前述步骤输出的化合物进行能量最小化,并使用 MOE, v.2019.01 将能量最低的构象结构与 SAM-I 进行对接。使用 S 分数和配体相互作用来评估最佳命中。结果发现了 8 种有前景的化合物,并使用 GROMACS 2020.3 软件包与 SAM-I aptamer 进行了分子动力学(MD)模拟,确认了稳定的结合相互作用和与 SAM 相似的结合能量。此外,还使用 SWISS-ADME 评估了这 8 个新药的药代动力学和类药物特性。根据综合计算方法和 PK/Tox 评估,化合物 20 最有前途,因此可将其作为一个先导物,在今后的评估和优化中作为一种以新生物分子为靶点、激发新作用机制的候选新抗菌剂。
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Exploration of SAM-I riboswitch inhibitors: In-Silico discovery of ligands to a new target employing multistage CADD approaches

Targeting riboswitches, regulatory elements responsible for the expression of essential genes, is taking central stage in the new era of antibacterial medications discovery due to the emergence of antibiotic resistance. The S-Adenosyl methionine-I (SAM-I) riboswitch works through transcription termination in a negative feedback manner modulated by the natural ligand SAM. SAM-I riboswitch is specific to bacteria and found mainly in gram-positive bacteria such as Bacillus anthracis. Analyzing the interactions of the co-crystallized structure of SAM-I riboswitch aptamer with its native ligand SAM clarified the needed chemical structural features to achieve binding. Acknowledging those features, structure-based and ligand-based pharmacophore models were built for filtration use in screening the OTAVA Chemical library and the Pubchem database. For further filtration enhancement, the physicochemical properties of SAM were used as a second filtration criterion. Compounds obtained as output from previous steps were energy minimized, and the lowest energy conformer structures were docked to SAM-I using MOE, v.2019.01. S-score and ligand interactions were used to assess the best hits. This yielded eight promising compounds to which molecular dynamics (MD) simulations with SAM-I aptamer were applied using GROMACS 2020.3 package affirming stable binding interactions and binding energetics similar to SAM. Moreover, pharmacokinetic and drug-like properties of those eight hits were assessed using SWISS-ADME. According to the combined computational methods and PK/Tox assessment, compound 20 was the most promising and thus can be considered a lead for future evaluation and optimization as a candidate new antibacterial agent targeting a new biomolecule eliciting a new mechanism of action.

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Artificial intelligence chemistry
Artificial intelligence chemistry Chemistry (General)
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