A novel index of protein-protein interface propensity improves interface residue recognition

作者：Dai, Wentao; Wu, Aiping; Ma, Liangxiao; Li, Yi-Xue; Jiang, Taijiao^*; Li, Yuan-Yuan

来源：BMC Systems Biology, 2016, 10(Suppl 4): 112.

DOI：10.1186/s12918-016-0351-7

• HOT-SPOTS
• GLOBULAR-PROTEINS
• SITE PREDICTION
• PRINCIPLES
• POTENTIALS
• COMPLEXES
• IDENTIFY
• DATABASE

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.
esults: 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 (Astral2.05-404506). 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.

• 出版日期2016-12-23
• 单位上海生物信息技术研究中心; 北京协和医学院; 中国医学科学院北京协和医院