Structure-based identification of small molecule binding sites using a free energy model
Structure-based identification of small molecule binding sites using a free energy model
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DOI：
10.1021/ci600229z
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发表时间：
2006-11-27
影响因子：
5.6
通讯作者：
Cheng, Alan C.
中科院分区：
文献类型：
--
作者：
Coleman, Ryan G.;Salzberg, Anna C.;Cheng, Alan C.
我们分别证明了蛋白质上给定结合部位的最大类药物亲和力可以仅根据结合部位的结构来计算，方法是使用基于解溶的自由能模型以及类药物配体属于特定的物理化学性质范围的概念。在这里，我们提出了一种方法，其中我们重新计算的药物亲和力作为一个附加自由能，以促进搜索整个蛋白质表面的药物样结合部位。在许多情况下，得分最高的贴片代表了已知的可药物靶标的配体结合部位，但不是困难的靶标。这种方法与其他方法的不同之处在于，它不是简单地识别体积最大的口袋，而是识别可能服从于类药物小分子结合的口袋。将该方法与一种名为SCA(统计耦合分析)的功能残基预测方法相结合，可以预测p38α激酶上潜在的可药物变构结合位点。
We separately have shown that the maximal druglike affinity of a given binding site on a protein can be calculated on the basis of the binding-site structure alone by using a desolvation-based free energy model along with the notion that druglike ligands fall into certain physiochemical property ranges. Here, we present an approach where we reformulate the calculated druggability affinity as an additive free energy to facilitate the searching of whole protein surfaces for druglike binding sites. The highest-scoring patches in many cases represent known ligand-binding sites for druggable targets, but not for difficult targets. This approach differs from other approaches in that it does not simply identify pockets with the greatest volume but instead identifies pockets that are likely to be amenable to druglike small-molecule binding. Combining the method with a functional residue prediction method called SCA (statistical coupling analysis) results in the prediction of potentially druggable allosteric binding sites on p38 alpha kinase.