A Protein-DNA Binding Site Prediction Method Based on Multi-View Feature Fusion of Adjacent Residue

The interaction between proteins and DNA occurs widely during the replication and transcription of DNA and other life activities. Therefore, the identification of protein- and DNA-binding sites is important for the study of protein function and drug design. Accurate prediction of binding sites has become a challenging and significant task. Although numerous studies have been conducted, prediction is challenging. In this study, a new protein-DNA binding site prediction method was proposed. This method is based on neighboring residue correlations. It uses an improved feature representation method that weighted combines several protein characteristics after a series of processing of the features and chooses a support vector machine as the prediction engine. Experiments on benchmark datasets and independent test datasets show that the proposed method has better predictability than other protein-DNA binding site predictors. This method is complementary to the existing protein-DNA binding site predictors and will be useful in the field of biotechnology.

Automated summary

The interaction between proteins and DNA is crucial in various biological processes. Identifying protein-DNA binding sites is important for understanding protein function and designing drugs. This study proposes a new method for predicting protein-DNA binding sites, based on neighboring residue correlations and improved feature representation. Experimental results demonstrate that this method outperforms other predictors and has significant implications in the field of biotechnology.