Feng Jun, Sanil Ashish, Young S Stanley
National Institute of Statistical Sciences, P.O. Box 14006, Research Triangle Park, North Carolina 27709-4006, USA.
J Chem Inf Model. 2006 May-Jun;46(3):1352-9. doi: 10.1021/ci050427v.
The binding of a small molecule to a protein is inherently a 3D matching problem. As crystal structures are not available for most drug targets, there is a need to be able to infer from bioassay data the key binding features of small molecules and their disposition in space, the pharmacophore. Fingerprints of 3D features and a modification of Gibbs sampling to align a set of known flexible ligands, where all compounds are active, are used to discern possible pharmacophores. A clique detection method is used to map the features back onto the binding conformations. The complete algorithm is described in detail, and it is shown that the method can find common superimposition for several test data sets. The method reproduces answers very close to the crystal structure and literature pharmacophores in the examples presented. The basic algorithm is relatively fast and can easily deal with up to 100 compounds and tens of thousands of conformations. The algorithm is also able to handle multiple binding mode problems, which means it can superimpose molecules within the same data set according to two different sets of binding features. We demonstrate the successful use of this algorithm for multiple binding modes for a set of D2 and D4 ligands.
小分子与蛋白质的结合本质上是一个三维匹配问题。由于大多数药物靶点没有晶体结构，因此需要能够从生物测定数据中推断小分子的关键结合特征及其在空间中的布局，即药效团。利用三维特征指纹和吉布斯采样的一种改进方法来对齐一组已知的柔性配体（所有化合物均具有活性），以识别可能的药效团。使用团簇检测方法将这些特征映射回结合构象。详细描述了完整的算法，结果表明该方法能够为几个测试数据集找到共同的叠加方式。在所给出的示例中，该方法得出的结果与晶体结构和文献中的药效团非常接近。基本算法相对较快，能够轻松处理多达100种化合物和数万个构象。该算法还能够处理多种结合模式问题，这意味着它可以根据两组不同的结合特征在同一数据集中叠加分子。我们展示了该算法在一组D2和D4配体的多种结合模式中的成功应用。
