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Predicting Hot Spot Residues at Protein–DNA Binding Interfaces Based on Sequence Information

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Abstract

Hot spot residues at protein–DNA binding interfaces are hugely important for investigating the underlying mechanism of molecular recognition. Currently, there are a few tools available for identifying the hot spot residues in the protein–DNA complexes. In addition, the three-dimensional protein structures are needed in these tools. However, it is well known that the three-dimensional structures are unavailable for most proteins. Considering the limitation, we proposed a method, named SPDH, for predicting hot spot residues only based on protein sequences. Firstly, we obtained 133 features from physicochemical property, conservation, predicted solvent accessible surface area and structure. Then, we systematically assessed these features based on various feature selection methods to obtain the optimal feature subset and compared the models using four classical machine learning algorithms (support vector machine, random forest, logistic regression, and k-nearest neighbor) on the training dataset. We found that the variability of physicochemical property features between wild and mutative types was important on improving the performance of the prediction model. On the independent test set, our method achieved the performance with AUC of 0.760 and sensitivity of 0.808, and outperformed other methods. The data and source code can be downloaded at https://github.com/xialab-ahu/SPDH.

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Acknowledgements

The authors acknowledge the High-performance Computing Platform of Anhui University for providing computing resources. This work was supported by the National Natural Science Foundation of China (11835014 and 21601001), the Recruitment Program for Leading Talent Team of Anhui Province (2019-16), and the China Postdoctoral Science Foundation (2018M630699).

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  1. Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China

    Lingsong Yao & Yannan Bin

  2. School of Computer Science and Technology, Anhui University, Hefei, 230601, Anhui, China

    Huadong Wang

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Yao, L., Wang, H. & Bin, Y. Predicting Hot Spot Residues at Protein–DNA Binding Interfaces Based on Sequence Information. Interdiscip Sci Comput Life Sci 13, 1–11 (2021). https://doi.org/10.1007/s12539-020-00399-z

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