An improved DNA-binding hot spot residues prediction method by exploring interfacial neighbor properties

Abstract

Background DNA-binding hot spots are dominant and fundamental residues that contribute most of the binding free energy yet accounting for a small portion of protein–DNA interfaces. As experimental methods for identifying hot spots are time-consuming and costly, high-efficiency computational approaches are emerging as alternative pathways to experimental methods. Results Herein, we present a new computational method, termed inpPDH, for hot spot prediction. To improve the prediction performance, we extract hybrid features which incorporate traditional features and new interfacial neighbor properties. To remove redundant and irrelevant features, feature selection is employed using a two-step feature selection strategy. Finally, a subset of 7 optimal features are chosen to construct the predictor using support vector machine. The results on the benchmark dataset show that this proposed method yields significantly better prediction accuracy than those previously published methods in the literature. Moreover, a user-friendly web server for inpPDH is well established and is freely available at http://bioinfo.ahu.edu.cn/inpPDH. Conclusions We have developed an accurate improved prediction model, inpPDH, for hot spot residues in protein–DNA binding interfaces by given the structure of a protein–DNA complex. Moreover, we identify a comprehensive and useful feature subset including the proposed interfacial neighbor features that has an important strength for identifying hot spot residues. Our results indicate that these features are more effective than the conventional features considered previously, and that the combination of interfacial neighbor features and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spot residues in protein–DNA complexes.

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

“…Hydrogen bonds affect protein–DNA recognition [ 14 , 19 , 35 ]. Here, we used HBPLUS [ 36 ] to calculate the hydrogen bonds of protein–DNA complexes.…”

“…PrPDH [11] was a method based on 114-dimensional features, which used random forests (VSURF) [12] for feature selection and support vector machine (SVM) [13] as classifier to predict hot spot residues in protein-DNA binding interfaces. inpPDH [14] extracted the traditional features and new interface adjacent property features, used the two-step feature selection strategies for feature selection, and finally built the prediction model based on SVM. sxPDH [15] used supervised isometric feature mapping (S-ISOMAP) [16] and extreme gradient boosting (XGBoost) [17] to predict hot spots in protein-DNA complexes based on features extracted from PrPDH.…”

Prediction of Protein-DNA Interface Hot Spots Based on Empirical Mode Decomposition and Machine Learning

“…Machine learning methods, including PrPDH [ 9 ] and WTL-PDH [ 10 ], focus on identifying hot spot residues at the protein-DNA binding interface or predicting hot spots through extensive attribute extraction. InpPDH [ 11 ] implements an SVM-based prediction model, employing both traditional and novel neighboring interface attribute features. Moreover, sxPDH [ 12 ] combines supervised isometric feature mapping with extreme gradient boosting based on PrPDH feature extraction to predict hot spot regions.…”