Boosting Prediction Performance of Protein-Protein Interaction Hot Spots by Using Structural Neighborhood Properties

Author(s):  
Lei Deng ◽  
Jihong Guan ◽  
Xiaoming Wei ◽  
Yuan Yi ◽  
Shuigeng Zhou
2013 ◽  
Vol 20 (11) ◽  
pp. 878-891 ◽  
Author(s):  
Lei Deng ◽  
Jihong Guan ◽  
Xiaoming Wei ◽  
Yuan Yi ◽  
Qiangfeng Cliff Zhang ◽  
...  

2014 ◽  
Vol 42 (W1) ◽  
pp. W290-W295 ◽  
Author(s):  
Lei Deng ◽  
Qiangfeng Cliff Zhang ◽  
Zhigang Chen ◽  
Yang Meng ◽  
Jihong Guan ◽  
...  

Biochemistry ◽  
2017 ◽  
Vol 56 (12) ◽  
pp. 1768-1784 ◽  
Author(s):  
Degang Liu ◽  
David Xu ◽  
Min Liu ◽  
William Eric Knabe ◽  
Cai Yuan ◽  
...  

2020 ◽  
Vol 21 (7) ◽  
pp. 2274 ◽  
Author(s):  
Aijun Deng ◽  
Huan Zhang ◽  
Wenyan Wang ◽  
Jun Zhang ◽  
Dingdong Fan ◽  
...  

The study of protein-protein interaction is of great biological significance, and the prediction of protein-protein interaction sites can promote the understanding of cell biological activity and will be helpful for drug development. However, uneven distribution between interaction and non-interaction sites is common because only a small number of protein interactions have been confirmed by experimental techniques, which greatly affects the predictive capability of computational methods. In this work, two imbalanced data processing strategies based on XGBoost algorithm were proposed to re-balance the original dataset from inherent relationship between positive and negative samples for the prediction of protein-protein interaction sites. Herein, a feature extraction method was applied to represent the protein interaction sites based on evolutionary conservatism of proteins, and the influence of overlapping regions of positive and negative samples was considered in prediction performance. Our method showed good prediction performance, such as prediction accuracy of 0.807 and MCC of 0.614, on an original dataset with 10,455 surface residues but only 2297 interface residues. Experimental results demonstrated the effectiveness of our XGBoost-based method.


Biochemistry ◽  
2015 ◽  
Vol 54 (40) ◽  
pp. 6162-6175 ◽  
Author(s):  
Yan Wang ◽  
Huili Yao ◽  
Yuan Cheng ◽  
Scott Lovell ◽  
Kevin P. Battaile ◽  
...  

2011 ◽  
Vol 33 (2) ◽  
pp. 359-363 ◽  
Author(s):  
Alessia David ◽  
Rozami Razali ◽  
Mark N. Wass ◽  
Michael J.E. Sternberg

2018 ◽  
Vol 40 (9) ◽  
pp. 1045-1056 ◽  
Author(s):  
Dading Huang ◽  
Yifei Qi ◽  
Jianing Song ◽  
John Z. H. Zhang

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hocheol Lim ◽  
Ayoung Baek ◽  
Jongwan Kim ◽  
Min Sung Kim ◽  
Jiaxin Liu ◽  
...  

Abstract The prevalence of a novel β-coronavirus (SARS-CoV-2) was declared as a public health emergency of international concern on 30 January 2020 and a global pandemic on 11 March 2020 by WHO. The spike glycoprotein of SARS-CoV-2 is regarded as a key target for the development of vaccines and therapeutic antibodies. In order to develop anti-viral therapeutics for SARS-CoV-2, it is crucial to find amino acid pairs that strongly attract each other at the interface of the spike glycoprotein and the human angiotensin-converting enzyme 2 (hACE2) complex. In order to find hot spot residues, the strongly attracting amino acid pairs at the protein–protein interaction (PPI) interface, we introduce a reliable inter-residue interaction energy calculation method, FMO-DFTB3/D/PCM/3D-SPIEs. In addition to the SARS-CoV-2 spike glycoprotein/hACE2 complex, the hot spot residues of SARS-CoV-1 spike glycoprotein/hACE2 complex, SARS-CoV-1 spike glycoprotein/antibody complex, and HCoV-NL63 spike glycoprotein/hACE2 complex were obtained using the same FMO method. Following this, a 3D-SPIEs-based interaction map was constructed with hot spot residues for the hACE2/SARS-CoV-1 spike glycoprotein, hACE2/HCoV-NL63 spike glycoprotein, and hACE2/SARS-CoV-2 spike glycoprotein complexes. Finally, the three 3D-SPIEs-based interaction maps were combined and analyzed to find the consensus hot spots among the three complexes. As a result of the analysis, two hot spots were identified between hACE2 and the three spike proteins. In particular, E37, K353, G354, and D355 of the hACE2 receptor strongly interact with the spike proteins of coronaviruses. The 3D-SPIEs-based map would provide valuable information to develop anti-viral therapeutics that inhibit PPIs between the spike protein of SARS-CoV-2 and hACE2.


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