MsDBP: Exploring DNA-Binding Proteins by Integrating Multiscale Sequence Information via Chou’s Five-Step Rule

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;
thus, it is necessary to develop a fast and accurate computational
method to address the problem. In this Article, we report a novel
predictor MsDBP, a DNA-binding protein prediction method that combines
the multiscale 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. Second, 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 multiscale
sequence feature can be obtained. Finally, a branch of dense layers
is applied for learning multilevel abstract features to discriminate
DNA-binding proteins. When MsDBP is tested on the independent data
set 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/.