Prediction of hot spots in protein-DNA binding interfaces based on discrete wavelet transform and wavelet packet transform

被引:1
作者
Sun, Yu [1 ,3 ]
Wu, Hongwei [1 ,3 ]
Xu, Zhengrong [1 ,3 ]
Yue, Zhenyu [1 ,3 ]
Li, Ke [1 ,2 ,3 ]
机构
[1] Anhui Agr Univ, Sch Informat & Comp, Hefei 230036, Anhui, Peoples R China
[2] Anhui Univ, Informat Mat & Intelligent Sensing Lab Anhui Prov, Hefei 230601, Anhui, Peoples R China
[3] Anhui Agr Univ, Anhui Prov Engn Lab Beidou Precis Agr Informat, Hefei 230036, Anhui, Peoples R China
基金
中国国家自然科学基金;
关键词
Protein-DNA complexes; Hot spot; Synthetic minority over-sampling technique; Discrete wavelet transform; Wavelet packet transform; Light gradient boosting machine; SELECTION; SOLVENT; ENERGY;
D O I
10.1186/s12859-023-05263-7
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Background: Identification of hot spots in protein-DNA binding interfaces is extremely important for understanding the underlying mechanisms of protein-DNA interactions and drug design. Since experimental methods for identifying hot spots are time-consuming and expensive, and most of the existing computational methods are based on traditional protein-DNA features to predict hot spots, unable to make full use of the effective information in the features.Results: n this work, a method named WTL-PDH is proposed for hot spots prediction. To deal with the unbalanced dataset, we used the Synthetic Minority Over-sampling Technique to generate minority class samples to achieve the balance of dataset. First, we extracted the solvent accessible surface area features and structural features, and then processed the traditional features using discrete wavelet transform and wavelet packet transform to extract the wavelet energy information and wavelet entropy information, and obtained a total of 175 dimensional features. In order to obtain the best feature subset, we systematically evaluate these features in various feature selection strategies. Finally, light gradient boosting machine (LightGBM) was used to establish the model.Conclusions: ur method achieved good results on independent test set with AUC, MCC and F1 scores of 0.838, 0.533 and 0.750, respectively. WTL-PDH can achieve generally better performance in predicting hot spots when compared with state-of-the-art methods. The dataset and source code are available at .
引用
收藏
页数:16
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