针对具有广泛连接体库的标的药基模拟:SARS-CoV-2 Mpro的案例研究
L J Córdova-Bahena1, S M Pérez-Tapia2, Marco A Velasco-Velázquez3
1School of Medicine, Universidad Nacional Autónoma de México (UNAM); Investigadores por México, Secretaría de Ciencia, Humanidades, Tecnología e Innovación (SECIHTI).
我们使用ConPhar开发了一种新协议,从许多连接体中构建共识的药模型. 该方法通过确定生物标的关键分子相互作用,有助于发现新的候选药物.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 结构生物学是结构生物学.
背景情况:
- 药模型代表了目标相互作用的基本分子特征.
- 共识药理通过整合来自多个连接体的数据来提高预测能力.
- 从大型,多样化的连接体集中产生强大的药存在挑战.
研究的目的:
- 通过使用ConPhar工具构建共识药理的协议.
- 展示本协议在虚拟查中的应用.
- 为了识别SARS-CoV-2主要蛋白酶 (Mpro) 的新型配体.
主要方法:
- 利用ConPhar,一个开源的信息学工具,用于药物特征识别和聚类.
- 开发了一种用于制药分子模型生成和改进的协议.
- 将该协议应用于与目标共同结晶的100种非共价Mpro抑制剂.
主要成果:
- 为SARS-CoV-2 Mpro.生成了一个共识药模型.
- 该模型准确地捕获了Mpro催化区域的关键相互作用.
- 通过虚拟选成功识别了潜在的新配体.
结论:
- 基于ConPhar的协议提供了一个强大的方法来生成共识药理.
- 这一策略广泛适用于各种生物标,可获得带绑定数据.
- 该方法支持合理的药物发现,并简化了新药候选药物的识别.
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