Mapping of Protein Binding Sites using clustering algorithms-Development of a pharmacophore based drug discovery tool
Mapping of Protein Binding Sites using clustering algorithms-Development of a pharmacophore based drug discovery tool
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DOI：
10.1016/j.jmgm.2022.108228
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发表时间：
2022-06-03
影响因子：
2.9
通讯作者：
Fayne, Darren
中科院分区：
文献类型：
--
作者：
Braun, Jessica;Fayne, Darren
发现与特定蛋白质结合位点结合的新的热门小分子可能是一项艰巨的任务。为了支持现有的程序，已经开发了一种概念验证方法，以使用K-Means聚类算法处理碎片泛滥的X射线蛋白质结构，以便推导出结合位点的药效团模型。该新方法包括在串行和并行版本中实现几种K-Means初始化方法。此外，还实现了两种初始化方法所需的参数优化，这是确定其有效性和性能所必需的。采用图论算法比较聚类衍生的药效团和X射线配基结构衍生的药效团，以确认它们相互映射。最初的概念验证方法验证是使用雄激素受体(AR)进行的。
Discovering new hit small molecules binding to a specific protein binding site can be a difficult task. In support of existing procedures, a proof of concept methodology has been developed to process fragment flooded X-ray protein structures using the K-means clustering algorithm in order to derive pharmacophore models of the binding site. The novel method includes the implementation of several K-means initialisation methods in serial and parallel versions. Furthermore, required parameter optimisations for two initialisation methods was achieved, which was necessary to determine their validity and performance. A graph theory algorithm was adapted to compare the clustering-derived pharmacophores with X-ray ligand structure-derived pharmacophores to confirm that they mapped to each other. Initial proof of concept method validation was demonstrated using the Androgen Receptor (AR).