Identification of binding sites and favorable ligand binding moieties by virtual screening and self-organizing map analysis
Identification of binding sites and favorable ligand binding moieties by virtual screening and self-organizing map analysis
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DOI：
10.1186/s12859-015-0518-z
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发表时间：
2015-03-21
影响因子：
3
通讯作者：
Bouvier, Guillaume
中科院分区：
文献类型：
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作者：
Harigua-Souiai, Emna;Cortes-Ciriano, Isidro;Bouvier, Guillaume
背景：识别蛋白质表面的药物空腔是基于结构的药物设计的关键步骤。空腔必须呈现合适的大小和形状，以及适当的化学互补性与ligands.Results：我们提出了一种新的空腔预测方法，分析结果的虚拟筛选的特定的配体或片段库通过自组织映射。我们证明了两个彻底研究的蛋白质，它成功地确定了它们的活性位点（AS）和相关的二级结合位点（BS）的方法。此外，已知的活性配体映射AS比非活性的。有趣的是，对接原始片段库带来了更多的见解。然后，我们系统地将该方法应用于DUD-E数据库中的102个目标，在SOM的前三个一致聚类中，AS的识别率为90%，并且在82%的情况下，AS是第一个。通过碎片的化学分解的进一步分析改进了BS预测。化学亚结构，是代表的活性配体优先映射在AS。结论：新的方法提供了有价值的信息，相关BS和化学特征，促进生物活性。
Background: Identifying druggable cavities on a protein surface is a crucial step in structure based drug design. The cavities have to present suitable size and shape, as well as appropriate chemical complementarity with ligands.Results: We present a novel cavity prediction method that analyzes results of virtual screening of specific ligands or fragment libraries by means of Self-Organizing Maps. We demonstrate the method with two thoroughly studied proteins where it successfully identified their active sites (AS) and relevant secondary binding sites (BS). Moreover, known active ligands mapped the AS better than inactive ones. Interestingly, docking a naive fragment library brought even more insight. We then systematically applied the method to the 102 targets from the DUD-E database, where it showed a 90% identification rate of the AS among the first three consensual clusters of the SOM, and in 82% of the cases as the first one. Further analysis by chemical decomposition of the fragments improved BS prediction. Chemical substructures that are representative of the active ligands preferentially mapped in the AS.Conclusion: The new approach provides valuable information both on relevant BSs and on chemical features promoting bioactivity.