González-Ruiz Domingo, Gohlke Holger
Fachbereich Biowissenschaften, Molekulare Bioinformatik, Goethe-Universität, Max-von-Laue-Str. 9, 60438 Frankfurt am Main, Germany.
J Chem Inf Model. 2009 Oct;49(10):2260-71. doi: 10.1021/ci900188r.
Lead optimization benefits from including structural knowledge of the target. We present a new method that exploits quantitatively NMR amide proton chemical shift perturbations (CSP) on the protein side for protein-ligand docking. The approach is based on a hybrid scoring scheme consisting of a weighted sum of DrugScore, describing protein-ligand interactions, and Kendall's rank correlation coefficient, which scores ligand poses with respect to their agreement with experimental CSP data. For back-calculating CSP for a ligand pose, an efficient empirical model considering only ring-current effects is applied. The hybrid scoring scheme has been implemented in AutoDock. Compared to previous approaches, the rank correlation provides a measure that is more robust against the presence of outliers in back-calculated CSP data. Furthermore, our methods exploit CSP information at docking time and not for postfiltering, resulting in an enhanced generation of native-like solutions. As we exploit CSP information quantitatively, the experimental information effectively contributes to orient the ligand in the binding site. When applied to 70 protein-ligand complexes with computed CSP reference data, the docking success rate increases from 71%, if no CSP information is used, to 99% at the highest CSP weighting factor tested. Global optimization, thus, performs satisfactorily on the hybrid docking energy landscape. We next applied the approach to three test cases with experimental CSP reference data. Without CSP information, neither of the complexes is successfully docked. Including CSP information with the same CSP weighting factor, as determined above, leads to successful docking in all three cases. Only then native-like ligand configurations are generated at two of the three complexes. Binding site movements of up to 2 A are found to not deteriorate the docking success. The approach will be particularly important for protein-ligand complexes that are difficult to predict computationally, such as ligands binding to flat interface regions.
先导化合物优化受益于纳入靶点的结构知识。我们提出了一种新方法，该方法利用蛋白质侧链上的核磁共振酰胺质子化学位移扰动（CSP）进行蛋白质-配体对接的定量分析。该方法基于一种混合评分方案，该方案由描述蛋白质-配体相互作用的DrugScore加权和以及肯德尔等级相关系数组成，肯德尔等级相关系数根据配体构象与实验CSP数据的一致性对其进行评分。为了对配体构象进行CSP的反向计算，应用了一种仅考虑环电流效应的高效经验模型。该混合评分方案已在AutoDock中实现。与先前的方法相比，等级相关提供了一种对反向计算的CSP数据中异常值的存在更具鲁棒性的度量。此外，我们的方法在对接时利用CSP信息，而不是用于后筛选，从而增强了类似天然构象的解决方案的生成。由于我们定量利用CSP信息，实验信息有效地有助于在结合位点中定向配体。当应用于70个具有计算CSP参考数据的蛋白质-配体复合物时，如果不使用CSP信息，对接成功率为71%，在测试的最高CSP加权因子下提高到99%。因此，全局优化在混合对接能量景观上表现令人满意。接下来，我们将该方法应用于三个具有实验CSP参考数据的测试案例。没有CSP信息时，没有一个复合物能成功对接。使用上述确定的相同CSP加权因子纳入CSP信息，在所有三个案例中都能成功对接。只有在三个复合物中的两个中才生成类似天然的配体构型。发现高达2埃的结合位点移动不会降低对接成功率。该方法对于难以通过计算预测的蛋白质-配体复合物，如与平坦界面区域结合的配体，将特别重要。
