Next Article in Journal
Droplet Digital PCR Detection of the Erythropoietin Transgene from Horse Plasma and Urine for Gene-Doping Control
Previous Article in Journal
The Origin of a Coastal Indigenous Horse Breed in China Revealed by Genome-Wide SNP Data
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Genes 2019, 10(3), 242;

XGBPRH: Prediction of Binding Hot Spots at Protein–RNA Interfaces Utilizing Extreme Gradient Boosting

School of Computer Science and Engineering, Central South University, Changsha 410075, China
School of Computer and Data Science, Henan University of Urban Construction, Pingdingshan 467000, China
Author to whom correspondence should be addressed.
Received: 16 January 2019 / Revised: 14 March 2019 / Accepted: 15 March 2019 / Published: 21 March 2019
(This article belongs to the Section Technologies and Resources for Genetics)
PDF [2042 KB, uploaded 27 March 2019]


Hot spot residues at protein–RNA complexes are vitally important for investigating the underlying molecular recognition mechanism. Accurately identifying protein–RNA binding hot spots is critical for drug designing and protein engineering. Although some progress has been made by utilizing various available features and a series of machine learning approaches, these methods are still in the infant stage. In this paper, we present a new computational method named XGBPRH, which is based on an eXtreme Gradient Boosting (XGBoost) algorithm and can effectively predict hot spot residues in protein–RNA interfaces utilizing an optimal set of properties. Firstly, we download 47 protein–RNA complexes and calculate a total of 156 sequence, structure, exposure, and network features. Next, we adopt a two-step feature selection algorithm to extract a combination of 6 optimal features from the combination of these 156 features. Compared with the state-of-the-art approaches, XGBPRH achieves better performances with an area under the ROC curve (AUC) score of 0.817 and an F1-score of 0.802 on the independent test set. Meanwhile, we also apply XGBPRH to two case studies. The results demonstrate that the method can effectively identify novel energy hotspots. View Full-Text
Keywords: hot spots; protein–RNA interfaces; XGBoost; two-step feature selection hot spots; protein–RNA interfaces; XGBoost; two-step feature selection

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Deng, L.; Sui, Y.; Zhang, J. XGBPRH: Prediction of Binding Hot Spots at Protein–RNA Interfaces Utilizing Extreme Gradient Boosting. Genes 2019, 10, 242.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Genes EISSN 2073-4425 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top