Residue-level prediction of DNA-binding sites and its application on DNA-binding protein predictions
Residue-level prediction of DNA-binding sites and its application on DNA-binding protein predictions
复制标题
DOI：
10.1016/j.febslet.2007.01.086
复制
发表时间：
2007-03-06
期刊：
影响因子：
3.5
通讯作者：
Lu, Hui
中科院分区：
文献类型：
--
作者：
Bhardwaj, Nitin;Lu, Hui
蛋白质 - DNA相互作用对许多细胞活动至关重要，例如表达调控和DNA修复。氨基酸和核苷酸之间的这些相互作用具有高度特异性，结合位点的任何异常都可能使相互作用完全失效。在这项研究中，我们有三个针对蛋白质表面DNA结合残基的目标：开发一种快速可靠地识别DNA结合位点的自动化方法；通过距离相关的优化来改进预测；利用这些预测来识别DNA结合蛋白。我们使用一种基于支持向量机（SVM）的方法来利用DNA结合残基的特征，将它们与非结合残基区分开来。用于区分的特征包括残基的特性、电荷、溶剂可及性、平均电位、其所嵌入的二级结构、相邻残基以及在阳离子斑块中的位置。从50种蛋白质中收集的这些特征用于训练SVM。然后在另一组37种蛋白质上进行测试，这比以往研究中使用的任何测试集都要大得多。测试集中不仅其成对的蛋白质之间，而且与训练集中的蛋白质之间的序列同一性不超过20%，从而消除了由于同源性导致的任何不必要的冗余。这个集合中还包含具有训练集中未出现的未知DNA结合结构类别的蛋白质。利用上述特征，在不依赖同源性或进化信息的情况下，实现了66%的准确率，敏感性和特异性较为平衡。然后我们开发了一种后处理方案，利用预测残基的相对位置来改进预测。随后实现了平衡的成功，平均敏感性、特异性和准确率分别达到71.3%、69.3%和70.5%。平均净预测率也约为70%。最后，我们表明预测的DNA结合残基的数量可用于区分DNA结合蛋白和非DNA结合蛋白，准确率为78%。这里呈现的结果表明，机器学习可应用于DNA结合残基的自动识别，并且随着更多特征的添加，成功率可以提高。这种功能位点预测方案可用于指导后续工作，例如定点诱变和大分子对接。（c）2007由爱思唯尔B.V.代表欧洲生物化学会联合会出版。
Protein-DNA interactions are crucial to many cellular activities such as expression-control and DNA-repair. These interactions between amino acids and nucleotides are highly specific and any aberrance at the binding site can render the interaction completely incompetent. In this study, we have three aims focusing on DNA-binding residues on the protein surface: to develop an automated approach for fast and reliable recognition of DNA-binding sites; to improve the prediction by distance-dependent refinement; use these predictions to identify DNA-binding proteins. We use a support vector machines (SVM)based approach to harness the features of the DNA-binding residues to distinguish them from non-binding residues. Features used for distinction include the residue's identity, charge, solvent accessibility, average potential, the secondary structure it is embedded in, neighboring residues, and location in a cationic patch. These features collected from 50 proteins are used to train SVM. Testing is then performed on another set of 37 proteins, much larger than any testing set used in previous studies. The testing set has no more than 20% sequence identity not only among its pairs, but also with the proteins in the training set, thus removing any undesired redundancy due to homology. This set also has proteins with an unseen DNA-binding structural class not present in the training set. With the above features, an accuracy of 66% with balanced sensitivity and specificity is achieved without relying on homology or evolutionary information. We then develop a post-processing scheme to improve the prediction using the relative location of the predicted residues. Balanced success is then achieved with average sensitivity, specificity and accuracy pegged at 71.3%, 69.3% and 70.5%, respectively. Average net prediction is also around 70%. Finally, we show that the number of predicted DNA-binding residues can be used to differentiate DNA-binding proteins from non-DNA-binding proteins with an accuracy of 78%. Results presented here demonstrate that machine-learning can be applied to automated identification of DNA-binding residues and that the success rate can be ameliorated as more features are added. Such functional site prediction protocols can be useful in guiding consequent works such as site-directed mutagenesis and macromolecular docking. (c) 2007 Published by Elsevier B.V. on behalf of the Federation of European Biochemical Societies.