蛋白酶抑制剂复合物的实验和计算的抑制常数的相关性
Peter Goettig1,2, Xingchen Chen2, Jonathan M Harris2
1Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, Paracelsus Medical University, Strubergasse 21, 5020 Salzburg, Austria.
International journal of molecular sciences
|February 24, 2024
概括
这项研究引入了一种计算方法来预测抑制剂的功效,与实验数据保持一致. 这种方法有助于发现各种蛋白质标的强效抑制剂,包括蛋白酶及其他.
科学领域:
- 计算化学是一种计算化学.
- 生物物理学的生物物理.
- 药物发现 药物发现
背景情况:
- 预测抑制剂功效对于潜在的治疗化合物的选至关重要.
- 准确预测结合亲和力有助于识别有效的合成和天然候选药物.
研究的目的:
- 开发和验证用于计算抑制剂强度 (Ki和KD值) 的预测工作流.
- 评估工作流程在不同蛋白酶家族和其他蛋白质相互作用系统中的适用性.
主要方法:
- 利用YASARA的FoldX插件,从蛋白酶抑制剂复合体的PDB结构计算自由相互作用能量 (ΔG).
- 使用 PRODIGY 服务器获取相应的分离常数 (KD).
- 与各种蛋白酶类型的实验数据相关联的计算值.
主要成果:
- 计算的抑制值与实验数据有很好的一致性,特别是对于血清蛋白酶.
- PRODIGY服务器数据显示,在囊,酸盐和金属蛋白酶中,一致性更高.
- 鉴定出一种更为硬的 Pro14 变种的花素抑制剂 (SFTI-1),具有潜在更高的功效.
- 建议一种希鲁丁变体作为强有力的血栓激素抑制剂的基础.
结论:
- 开发的计算工作流准确地预测了蛋白酶抑制剂复合体的抑制功效.
- 该方法具有多功能性,适用于各种蛋白质-蛋白质相互作用,包括抗体-抗原和效应器-受体系统.
- 这种方法有助于在药物发现和理解蛋白质相互作用方面进行高效的选.
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