A Critical Comparative Assessment of Predictions of Protein-Binding Sites for Biologically Relevant Organic Compounds
A Critical Comparative Assessment of Predictions of Protein-Binding Sites for Biologically Relevant Organic Compounds
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DOI：
10.1016/j.str.2011.02.015
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发表时间：
2011-05-11
期刊：
影响因子：
5.7
通讯作者：
Kurgan, Lukasz
中科院分区：
文献类型：
--
作者：
Chen, Ke;Mizianty, Marcin J.;Kurgan, Lukasz
蛋白质功能注释和合理的药物发现依赖于小分子有机化合物结合位点的知识，然而现有的结合位点预测器的质量从未得到系统的评估。我们在一个新的基准数据集上评估了十种具有代表性的几何、能量、线程和共识方法的预测，该数据集考虑了具有多个生物相关配体结合位点的apo和holo蛋白质结构。统计测试表明，当模板与输入蛋白质有很高的相似性时，基于线程的FindSite的性能优于其他预测因子。然而，当相似度较低时，FindSite与一些基于几何、能量和共识的方法是等价的或更差的。我们证明，基于几何、能量和共识的预测因子受益于全息结构的使用，并且排名前四的方法FindSite、Q-SiteFinder、CON CALE和Meta Pocket对于较大的结合位点表现得更好。这四种方法的预测是互补的，我们的简单元预测比最好的单一预测更好。
Protein function annotation and rational drug discovery rely on the knowledge of binding sites for small organic compounds, and yet the quality of existing binding site predictors was never systematically evaluated. We assess predictions of ten representative geometry-, energy-, threading-, and consensus-based methods on a new benchmark data set that considers apo and holo protein structures with multiple binding sites for biologically relevant ligands. Statistical tests show that threading-based Findsite outperforms other predictors when its templates have high similarity with the input protein. However, Findsite is equivalent or inferior to some geometry-, energy-, and consensus-based methods when the similarity is lower. We demonstrate that geometry-, energy-, and consensus-based predictors benefit from the usage of holo structures and that the top four methods, Findsite, Q-SiteFinder, Con Cavity, and Meta Pocket, perform better for larger binding sites. Predictions from these four methods are complementary, and our simple meta-predictor improves over the best single predictor.