DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. With the rapid development of next generation of sequencing technique, the number of protein sequences are unprecedentedly increasing. Thus it is necessary to develop computational methods to identify the DNA-binding protein from the protein sequence information. In this study, a novel method is presented which combines the support vector machine and the auto-cross covariance transformation. The protein sequence represented in the form of amino acids or the physical-chemical properties of amino acids are converted into a series of fixed-length vectors by Kmer composition and the auto-cross covariance transformation. The sequence order effect can be effectively capture by this scheme. These vectors are then inputted to support vector machine to discriminate the DNA-binding proteins from the non DNA-binding ones. The proposed method achieves the overall accuracy of 75.23% and Matthew correlation coefficient of 0.5 by a rigorous jackknife test. The independent test shows that the proposed method outperforms most of the existing methods. These results demonstrate that the proposed method provides the state-of-the-art performance for the prediction of DNA-binding proteins.
['Qiwen Dong', 'Shanyi Wang', 'Kai Wang', 'Xuan Liu', 'Bin Liu']
Identification of DNA-binding proteins by auto-cross covariance transformation