期刊
JOURNAL OF PROTEOME RESEARCH
卷 18, 期 8, 页码 3119-3132出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jproteome.9b00226
关键词
DNA-binding proteins; multiscale features; dense layers
资金
- Anhui Provincial Natural Science Foundation [1708085QF143]
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 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/.
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