通过计算评估性PTP1B调节器的结合决定因素来解构性
Adele Hardie1, Benjamin P Cossins2,3, Silvia Lovera4
1EaStChem School of Chemistry, Joseph Black Building, University of Edinburgh, Edinburgh, EH9 3FJ, UK.
从碎片命中中预测异质药物潜力是具有挑战性的. 这项研究引入了带有方向分子动力学 (sMD) 的马尔科夫状态模型 (MSM) 工作流程,以评估全调节器的能力,帮助基于片段的药物发现 (FBDD).
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 生物物理学的生物物理.
背景情况:
- 基于碎片的药物发现 (FBDD) 识别了最初的击中分子.
- 预测非正极体断片结合剂的全极体调节器潜力仍然是一个挑战.
- 性结合的功能效应并不总是仅仅从结合亲和力来预测的.
研究的目的:
- 开发和验证一个计算工作流来评估碎片结合剂的性潜力.
- 为了引导碎片撞击成有效的全调节器的阐述.
- 提高基于片段的药物发现活动的成功率.
主要方法:
- 利用定向分子动力学 (sMD) 来探索蛋白质构造空间.
- 从sMD采样的形状构建了马尔科夫状态模型 (MSM).
- 将工作流应用于蛋白质氨酸酸酶1B (PTP1B) 连接体的数据集.
主要成果:
- 在MSM-sMD工作流程中,已知全抑制剂被正确分类.
- 全抑制剂的解构类似物显示抑制活性降低.
- 对MSM的分析揭示了与功能结果相关的关键蛋白质-连接物安排.
结论:
- 拟议的MSM-sMD方法有效地评估药物发现中的全性潜力.
- 这种方法提供了对结构-活性关系的洞察力,用于全调制.
- 工作流可以增强FBDD中碎片到化合物的进展.
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