A novel index of protein-protein interface propensity improves interface residue recognition.
BACKGROUND
Protein-protein interface holds important information of protein-protein interactions which play key roles in most biological processes. In the past few years, a lot of efforts have been made to improve interface residue recognition by characterizing protein-protein interfaces and extracting relevant features. However, most previous studies were carried out in a qualitative level, and there are also some inconsistencies between them.


RESULTS
In the present work, to improve interface residue recognition, we built a novel quantitative residue protein-protein interface propensity index (QIPI) and gained a comprehensive picture of protein-protein interface through analyzing protein-protein interfaces on our comprehensive protein-protein interfaces dataset (Astral2.05-40-4506). Furthermore, in order to assess the effect of QIPI in improving the protein-protein interface prediction, we developed an interface residue recognition method SPR (Single domain based Patch Recognition) based on the QIPI. The evaluation results proved that our novel QIPI is able to improve the interface residue recognition.


CONCLUSIONS
Through a comprehensive quantitative analysis of protein-protein interface, we constructed a novel quantitative protein-protein interface propensity index (QIPI), which could be easily applied to improve the interface residue recognition and helpful in understanding the protein-protein interface.


BACKGROUND
QIPI and SPR are available to non-commercial users at our website: http://www.scbit.org/QIPI/ .
Shanghai Center for Bioinformation Technology, 1278 Keyuan Road, Shanghai, 2012035, People's Republic of China.|Shanghai Industrial Technology Institute, 1278 Keyuan Road, Shanghai, 201203, People's Republic of China.
Wentao
W
Dai
Suzhou Institute of Systems Medicine, Suzhou, Jiangsu, 215123, China.
Aiping
A
Wu
Shanghai Center for Bioinformation Technology, 1278 Keyuan Road, Shanghai, 2012035, People's Republic of China.
Liangxiao
L
Ma
Shanghai Center for Bioinformation Technology, 1278 Keyuan Road, Shanghai, 2012035, People's Republic of China.|Shanghai Industrial Technology Institute, 1278 Keyuan Road, Shanghai, 201203, People's Republic of China.|Shanghai Engineering Research Center of Pharmaceutical Translation, 1278 Keyuan Road, Shanghai, 201203, People's Republic of China.
Yi-Xue
YX
Li
Suzhou Institute of Systems Medicine, Suzhou, Jiangsu, 215123, China. taijiao@ibms.pumc.edu.cn.|Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China. taijiao@ibms.pumc.edu.cn.
Taijiao
T
Jiang
Shanghai Center for Bioinformation Technology, 1278 Keyuan Road, Shanghai, 2012035, People's Republic of China. yyli@scbit.org.|Shanghai Industrial Technology Institute, 1278 Keyuan Road, Shanghai, 201203, People's Republic of China. yyli@scbit.org.|Shanghai Engineering Research Center of Pharmaceutical Translation, 1278 Keyuan Road, Shanghai, 201203, People's Republic of China. yyli@scbit.org.
Yuan-Yuan
YY
Li
D019295:Computational Biology / Q000379:methods*; D025941:Protein Interaction Mapping / Q000379:methods*; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D012997:Solvents / Q000737:chemistry
D011506:Proteins; D012997:Solvents