Background: The DNA-binding proteins is an important process in multiple biomolecular functions. However, the tradition experimental methods for DNA-binding proteins identification are still time consuming and extremely expensive.

Objective: In past several years, various computational methods have been developed to detect DNAbinding proteins. However, most of them do not integrate multiple information.

Methods: In this study, we propose a novel computational method to predict DNA-binding proteins by two steps Multiple Kernel Support Vector Machine (MK-SVM) and sequence information. Firstly, we extract several feature and construct multiple kernels. Then, multiple kernels are linear combined by Multiple Kernel Learning (MKL). At last, a final SVM model, constructed by combined kernel, is built to predict DNA-binding proteins.

Results: The proposed method is tested on two benchmark data sets. Compared with other existing method, our approach is comparable, even better than other methods on some data sets.

Conclusion: We can conclude that MK-SVM is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

背景：DNA结合蛋白是多种生物分子功能的重要过程。然而，用于DNA结合蛋白鉴定的传统实验方法仍然是耗时且极其昂贵的。

目的：在过去的几年中，已经开发出各种计算方法来检测DNA结合蛋白。但是，它们中的大多数不集成多个信息。

方法：在这项研究中，我们提出了一种通过两步多核支持向量机（MK-SVM）和序列信息预测DNA结合蛋白的新颖计算方法。首先，我们提取几个特征并构造多个内核。然后，通过多核学习（MKL）将多个核线性组合。最后，通过组合核构建最终的SVM模型，以预测DNA结合蛋白。

结果：所提出的方法在两个基准数据集上进行了测试。与其他现有方法相比，我们的方法具有可比性，甚至在某些数据集上也优于其他方法。

结论：我们可以得出结论，MK-SVM比普通SVM更适合作为DNA结合蛋白鉴定的分类器。