LAUSR

DNA-binding proteins are crucial to alternative splicing, methylation and the structural composition of the DNA. The existing experimental methods for identifying DNA-binding proteins are expensive and time-consuming and thus it is necessary to develop a fast and accurate computational method to address the problem. In this paper, we report a novel predictor MsDBP, a DNA-binding protein prediction method that combines the multi-scale sequence feature into a deep neural network. First of all, instead of developing a narrow-application structured-based method, we are committed to a sequenced-based predictor. Secondly, instead of characterizing the whole protein directly, we divide the protein into subsequences with different lengths and then encode them into a vector based on composition information. In this way, the multi-scale sequence feature can be obtained. Finally, a branch of dense layers is applied for learning diverse multi-level abstract features to discriminate DNA-binding proteins. When MsDBP is tested on the independent dataset PDB2272, it achieves an overall accuracy of 66.99% with the SE of 70.69%. In addition, we also perform extensive experiments to compare the proposed method with other existing methods. The results indicate that MsDBP would be a useful tool for the identification of DNA-binding proteins. MsDBP is freely available at a web server on http://47.100.203.218/MsDBP/.