Prediction of hot spots in protein–DNA binding interfaces based on supervised isometric feature mapping and extreme gradient boosting

BMC Bioinformatics

Sijia Zhang¹  Junfeng Xia¹  Yannan Bin¹  Di Yan²  Ke Li³ 

[1] Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, 230601, Hefei, Anhui, China;Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, 230601, Hefei, Anhui, China;School of Life Sciences, Anhui University, 230601, Hefei, Anhui, China;School of Information and Computer, Anhui Agricultural University, 230036, Hefei, Anhui, China;Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, 230601, Hefei, Anhui, China;

关键词: Protein–DNA complexes;    Hot spot;    Supervised isometric feature mapping;    Extreme gradient boosting;   

DOI  :  10.1186/s12859-020-03683-3

来源: Springer

【 摘 要 】

BackgroundIdentification of hot spots in protein-DNA interfaces provides crucial information for the research on protein-DNA interaction and drug design. As experimental methods for determining hot spots are time-consuming, labor-intensive and expensive, there is a need for developing reliable computational method to predict hot spots on a large scale.ResultsHere, we proposed a new method named sxPDH based on supervised isometric feature mapping (S-ISOMAP) and extreme gradient boosting (XGBoost) to predict hot spots in protein-DNA complexes. We obtained 114 features from a combination of the protein sequence, structure, network and solvent accessible information, and systematically assessed various feature selection methods and feature dimensionality reduction methods based on manifold learning. The results show that the S-ISOMAP method is superior to other feature selection or manifold learning methods. XGBoost was then used to develop hot spots prediction model sxPDH based on the three dimensionality-reduced features obtained from S-ISOMAP.ConclusionOur method sxPDH boosts prediction performance using S-ISOMAP and XGBoost. The AUC of the model is 0.773, and the F1 score is 0.713. Experimental results on benchmark dataset indicate that sxPDH can achieve generally better performance in predicting hot spots compared to the state-of-the-art methods.

【 授权许可】

CC BY