Using protein binding site prediction to improve protein docking

Gene ◽  
2008 ◽  
Vol 422 (1-2) ◽  
pp. 14-21 ◽  
Author(s):  
Bingding Huang ◽  
Michael Schroeder
2009 ◽  
Vol 10 (1) ◽  
Author(s):  
Joachim Giard ◽  
Jérôme Ambroise ◽  
Jean-Luc Gala ◽  
Benoît Macq

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Bin Liu ◽  
Bingquan Liu ◽  
Fule Liu ◽  
Xiaolong Wang

Identification of protein binding sites is critical for studying the function of the proteins. In this paper, we proposed a method for protein binding site prediction, which combined the order profile propensities and hidden Markov support vector machine (HM-SVM). This method employed the sequential labeling technique to the field of protein binding site prediction. The input features of HM-SVM include the profile-based propensities, the Position-Specific Score Matrix (PSSM), and Accessible Surface Area (ASA). When tested on different data sets, the proposed method showed promising results, and outperformed some closely relative methods by more than 10% in terms of AUC.


2012 ◽  
Vol 28 (11) ◽  
pp. 2729-2734
Author(s):  
WANG Pan-Wen ◽  
◽  
GONG Xin-Qi ◽  
LI Chun-Hua ◽  
CHEN Wei-Zu ◽  
...  

2010 ◽  
Vol 50 (10) ◽  
pp. 1906-1913 ◽  
Author(s):  
Nejc Carl ◽  
Janez Konc ◽  
Blaž Vehar ◽  
Dušanka Janežič

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