Singh Nidhi, Chevé Gwénaël, Ferguson David M, McCurdy Christopher R
Department of Medicinal Chemistry and Laboratory for Applied Drug Design and Synthesis, The University of Mississippi, University, MS 38677, USA.
J Comput Aided Mol Des. 2006 Jul-Aug;20(7-8):471-93. doi: 10.1007/s10822-006-9067-x. Epub 2006 Sep 29.
Combined ligand-based and target-based drug design approaches provide a synergistic advantage over either method individually. Therefore, we set out to develop a powerful virtual screening model to identify novel molecular scaffolds as potential leads for the human KOP (hKOP) receptor employing a combined approach. Utilizing a set of recently reported derivatives of salvinorin A, a structurally unique KOP receptor agonist, a pharmacophore model was developed that consisted of two hydrogen bond acceptor and three hydrophobic features. The model was cross-validated by randomizing the data using the CatScramble technique. Further validation was carried out using a test set that performed well in classifying active and inactive molecules correctly. Simultaneously, a bovine rhodopsin based "agonist-bound" hKOP receptor model was also generated. The model provided more accurate information about the putative binding site of salvinorin A based ligands. Several protein structure-checking programs were used to validate the model. In addition, this model was in agreement with the mutation experiments carried out on KOP receptor. The predictive ability of the model was evaluated by docking a set of known KOP receptor agonists into the active site of this model. The docked scores correlated reasonably well with experimental pK (i) values. It is hypothesized that the integration of these two independently generated models would enable a swift and reliable identification of new lead compounds that could reduce time and cost of hit finding within the drug discovery and development process, particularly in the case of GPCRs.
基于配体和基于靶点的药物设计方法相结合,相较于单独使用任何一种方法都具有协同优势。因此,我们着手开发一种强大的虚拟筛选模型,采用组合方法来识别新型分子骨架,作为人类κ-阿片受体(hKOP)的潜在先导物。利用一组最近报道的结构独特的κ-阿片受体激动剂萨尔文诺林A的衍生物,开发了一个药效团模型,该模型由两个氢键受体和三个疏水特征组成。使用CatScramble技术对数据进行随机化处理,对模型进行交叉验证。使用在正确分类活性和非活性分子方面表现良好的测试集进行进一步验证。同时,还生成了一个基于牛视紫红质的“激动剂结合型”hKOP受体模型。该模型提供了有关基于萨尔文诺林A的配体假定结合位点的更准确信息。使用了几个蛋白质结构检查程序来验证该模型。此外,该模型与在κ-阿片受体上进行的突变实验结果一致。通过将一组已知的κ-阿片受体激动剂对接至该模型的活性位点来评估模型的预测能力。对接分数与实验pK(i)值具有合理的相关性。据推测,这两个独立生成的模型相结合,将能够快速可靠地识别新的先导化合物,从而减少药物发现和开发过程中寻找活性化合物的时间和成本,特别是在GPCR的情况下。