PreDNA: accurate prediction of DNA-binding sites in proteins by integrating sequence and geometric structure information

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Abstract

Motivation:

Protein–DNA interactions often take part in various crucial processes, which are essential for cellular function. The identification of DNA-binding sites in proteins is important for understanding the molecular mechanisms of protein–DNA interaction. Thus, we have developed an improved method to predict DNA-binding sites by integrating structural alignment algorithm and support vector machine–based methods.

Results:

Evaluated on a new non-redundant protein set with 224 chains, the method has 80.7% sensitivity and 82.9% specificity in the 5-fold cross-validation test. In addition, it predicts DNA-binding sites with 85.1% sensitivity and 85.3% specificity when tested on a dataset with 62 protein–DNA complexes. Compared with a recently published method, BindN+, our method predicts DNA-binding sites with a 7% better area under the receiver operating characteristic curve value when tested on the same dataset. Many important problems in cell biology require the dense non-linear interactions between functional modules be considered. Thus, our prediction method will be useful in detecting such complex interactions.

Availability:

The PreDNA webserver is freely available at: http://202.207.14.178/predna/index.aspx

Contact:

qzli@imu.edu.cn

Supplementary information:

Supplementary data are available at Bioinformatics online

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