Identification of DNA-binding proteins using structural, electrostatic and evolutionary features.
Identification of DNA-binding proteins using structural, electrostatic and evolutionary features.
复制标题
DOI：
10.1016/j.jmb.2009.02.023
复制
发表时间：
2009-04-10
影响因子：
5.6
通讯作者：
Ben-Tal N
中科院分区：
文献类型：
--
作者：
Nimrod G;Szilágyi A;Leslie C;Ben-Tal N
DNA结合蛋白（DBP）通常参与细胞生命周期的各种关键过程。因此，这些蛋白质的鉴定和表征是非常重要的。我们在这里提出了一个随机森林分类器，用于识别具有已知三维结构的蛋白质中的DBP。首先，蛋白质表面上的进化保守区域（补丁）的集群使用Patcherosis算法进行检测;以前的研究表明，这些区域通常是蛋白质的功能重要区域。接下来，我们使用静电势、基于簇的氨基酸保守模式和补丁的二级结构内容等特征，以及包括其偶极矩在内的整个蛋白质的特征来训练分类器。在138种DNA结合蛋白和110种不结合DNA的蛋白的数据集上使用10倍交叉验证，分类器实现了0.90的灵敏度和特异性，这总体上优于先前公布的方法的性能。此外，当我们在11个未出现在原始数据集中的新DBP上测试5种不同的方法时，只有我们的方法注释正确。将所得分类器应用于已知结构和未知功能的757种蛋白质的集合。在这些蛋白质中，预计有218种与DNA结合，我们预计其中一些蛋白质会使用新的结构基序与DNA相互作用。互补计算工具的使用支持了这样一种观点，即它们中至少有一些确实与DNA结合。
DNA binding proteins (DBPs) often take part in various crucial processes of the cell's life cycle. Therefore, the identification and characterization of these proteins are of great importance. We present here a random forests classifier for identifying DBPs among proteins with known three-dimensional structures. First, clusters of evolutionarily conserved regions (patches) on the protein's surface are detected using the PatchFinder algorithm; previous studies showed that these regions are typically the proteins' functionally important regions. Next, we train a classifier using features like the electrostatic potential, cluster-based amino acid conservation patterns and the secondary structure content of the patches, as well as features of the whole protein including its dipole moment. Using 10-fold cross validation on a dataset of 138 DNA-binding proteins and 110 proteins which do not bind DNA, the classifier achieved a sensitivity and a specificity of 0.90, which is overall better than the performance of previously published methods. Furthermore, when we tested 5 different methods on 11 new DBPs which did not appear in the original dataset, only our method annotated all correctly. The resulting classifier was applied to a collection of 757 proteins of known structure and unknown function. Of these proteins, 218 were predicted to bind DNA, and we anticipate that some of them interact with DNA using new structural motifs. The use of complementary computational tools supports the notion that at least some of them do bind DNA.
登录
查看更多内容
Pfam：氏族、网络工具和服务。
影响因子：
14.9
作者：
Finn, Robert D.;Mistry, Jaina;Schuster-Bockler, Benjamin;Griffiths-Jones, Sam;Hollich, Volker;Lassmann, Timo;Moxon, Simon;Marshall, Mhairi;Khanna, Ajay;Durbin, Richard;Eddy, Sean R.;Sonnhammer, Erik L. L.;Bateman, Alex
通讯作者：
Bateman, Alex
影响因子：
14.9
作者：
Bhardwaj N;Langlois RE;Zhao G;Lu H
通讯作者：
Lu H
影响因子：
14.9
作者：
Jones, S;Shanahan, HP;Thornton, JM
通讯作者：
Thornton, JM
影响因子：
56.9
作者：
Dueber, Erin L. Cunningham;Corn, Jacob E.;Berger, James M.
通讯作者：
Berger, James M.
影响因子：
5.6
作者：
Ahmad, S;Sarai, A
通讯作者：
Sarai, A