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一种针对分子表面的D-氨基酸氧化酶抑制剂的基于结构的虚拟选和对接后分析

Yusuke Kato^1,2, Kiyoshi Fukui²

¹Research Center for Advanced Analysis, National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan.

Journal of biomolecular structure & dynamics

|December 25, 2025

概括

基于结构的虚拟查发现了一种针对外表面区域的新型D-氨基酸氧化酶 (DAO) 抑制剂. 分子动力学和聚类精细的结合姿势,证实了药物发现的稳定,独特的结合模式.

科学领域:

计算化学是一种计算化学.
药物发现药物发现
生物化学生物化学

背景情况:

D-氨基酸氧化酶 (DAO) 是治疗干预的目标.
鉴定新型结合位点的抑制剂,如DAO的外表面,由于缺乏已知的结合几何形状,这是一项挑战.
基于结构的虚拟查 (SBVS) 是识别新药候选者的强大工具.

研究的目的:

通过多步骤的SBVS运动,识别针对D-氨基酸氧化酶 (DAO) 外表面区域的新型抑制剂.
改进对接结果并确定已识别的抑制剂最稳定的结合姿势.
评估对姿势改进的对接后分析方法的性能.

主要方法:

通过使用三个对接引擎,在一个拥有16万个组合图书馆上进行了大规模的SBVS活动.
应用了基于可药性的过和共识评分来选择化合物.
利用分子动力学 (MD) 模拟和基于机器学习的层次聚类来进行后对接分析.
使用分子力学/一般化Born表面积 (MM/GBSA) 方法计算结合的自由能量.

主要成果:

鉴定出一种具有对DAO显著抑制活性的化合物.
三个不同的对接姿势汇聚到一个共同的稳定结合状态,具有低的结合自由能量.
已识别的抑制剂与与基质结合部位不同的表面区域结合.
最接近的邻居和组平均集群显示,姿势分类和具有约束力的自由能量计算之间的一致性很高.

结论:

一个集成的SBVS策略,包括对接后分析,对于识别表面结合抑制剂是有效的.
模拟MD和集群帮助提炼对接姿势和确定稳定的绑定模式.
这种方法促进了对DAO等具有挑战性的目标的新型抑制剂的发现.

关键词:

D-氨基酸氧化酶的作用基于结构的虚拟选.有约束力的自由能源.药物查对药物进行查.一个层次化的集群.机器学习是机器学习.分子动力学分子动力学蛋白质表面蛋白质表面蛋白质

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