Department of Bioscience and Bioinformatics, Kyushu Institute of Technology (KIT), 680-4 Kawazu, Iizuka 820-8502, Japan.
J Chem Inf Model. 2010 Jun 28;50(6):1179-88. doi: 10.1021/ci1000532.
Intensive research has been performed on computational design of kinase inhibitors using molecular dynamics simulations, docking and quantitative structure-activity relationship (QSAR) analyses, all of which have their own limitations. In this paper, we report the application of proteochemometrics, a ligand-target modeling approach, to the recognition of stable and unstable kinase-inhibitor complexes using support vector machines (SVM) classifiers. The algorithm consists of creating topological autocorrelation descriptors for kinases and inhibitors and then development of SVM models to relate the feature vectors to the stability class (stable or unstable) of hypothetical protein-inhibitor complexes. The approach based on the autocorrelation features was compared with fragment-based approach and the former was found to outperform the later. The final classifier could recognize 82% of data to be stable or unstable using jackknife type of validation and test set prediction. Analysis of substructure classification showed a very homogeneous behavior of the model on the whole target-ligand space. The predictor is available online at http://gibk21.bse.kyutech.ac.jp/AUTOkinI/SVMpredictor.html.
已经对使用分子动力学模拟、对接和定量构效关系 (QSAR) 分析进行激酶抑制剂的计算设计进行了深入研究，但这些方法都有其自身的局限性。在本文中，我们报告了应用基于配体-靶标建模方法的计算化学计量学，使用支持向量机 (SVM) 分类器来识别稳定和不稳定的激酶-抑制剂复合物。该算法包括为激酶和抑制剂创建拓扑自相关描述符，然后开发 SVM 模型，将特征向量与假设的蛋白-抑制剂复合物的稳定性类别（稳定或不稳定）相关联。基于自相关特征的方法与基于片段的方法进行了比较，前者的性能优于后者。最终的分类器使用刀切型验证和测试集预测可以识别 82%的数据为稳定或不稳定。亚结构分类分析表明，模型在整个靶标-配体空间上具有非常均匀的行为。该预测器可在线获得，网址为 http://gibk21.bse.kyutech.ac.jp/AUTOkinI/SVMpredictor.html。
