Research Papers:

Predicting hot spots in protein interfaces based on protrusion index, pseudo hydrophobicity and electron-ion interaction pseudopotential features

Junfeng Xia, Zhenyu Yue, Yunqiang Di, Xiaolei Zhu _ and Chun-Hou Zheng

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Oncotarget. 2016; 7:18065-18075. https://doi.org/10.18632/oncotarget.7695

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Junfeng Xia1,2, Zhenyu Yue3, Yunqiang Di4, Xiaolei Zhu3, Chun-Hou Zheng2,4

1Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institute of Health Sciences, Anhui University, Hefei, Anhui 230601, China

2Co-Innovation Center for Information Supply and Assurance Technology, School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China

3School of Life Sciences, Anhui University, Hefei, Anhui 230601, China

4College of Electrical Engineering and Automation, Anhui University, Hefei, Anhui 230601, China

Correspondence to:

Xiaolei Zhu, e-mail: xlzhu_mdl@hotmail.com

Keywords: hot spot, protrusion index, pseudo hydrophobicity, electron-ion interaction pseudopotential, cancer driver mutation

Received: November 02, 2015     Accepted: February 11, 2016     Published: February 25, 2016


The identification of hot spots, a small subset of protein interfaces that accounts for the majority of binding free energy, is becoming more important for the research of drug design and cancer development. Based on our previous methods (APIS and KFC2), here we proposed a novel hot spot prediction method. For each hot spot residue, we firstly constructed a wide variety of 108 sequence, structural, and neighborhood features to characterize potential hot spot residues, including conventional ones and new one (pseudo hydrophobicity) exploited in this study. We then selected 3 top-ranking features that contribute the most in the classification by a two-step feature selection process consisting of minimal-redundancy-maximal-relevance algorithm and an exhaustive search method. We used support vector machines to build our final prediction model. When testing our model on an independent test set, our method showed the highest F1-score of 0.70 and MCC of 0.46 comparing with the existing state-of-the-art hot spot prediction methods. Our results indicate that these features are more effective than the conventional features considered previously, and that the combination of our and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spots in protein interfaces.

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