標題: ATRIPPI: AN ATOM-RESIDUE PREFERENCE SCORING FUNCTION FOR PROTEIN-PROTEIN INTERACTIONS
作者: Liu, Kang-Ping
Hsu, Kai-Cheng
Huang, Jhang-Wei
Chang, Lu-Shian
Yang, Jinn-Moon
生物科技學系
生物資訊及系統生物研究所
Department of Biological Science and Technology
Institude of Bioinformatics and Systems Biology
關鍵字: Protein-protein interaction;atom-atom interacting preference;knowledge-based scoring matrix;residue-residue interaction preference
公開日期: 1-六月-2010
摘要: We present an ATRIPPI model for analyzing protein-protein interactions. This model is a 167-atom-type and residue-specific interaction preferences with distance bins derived from 641 co-crystallized protein-protein interfaces. The ATRIPPI model is able to yield physical meanings of hydrogen bonding, disulfide bonding, electrostatic interactions, van der Waals and aromatic-aromatic interactions. We applied this model to identify the native states and near-native complex structures on 17 bound and 17 unbound complexes from thousands of decoy structures. On average, 77.5% structures (155 structures) of top rank 200 structures are closed to the native structure. These results suggest that the ATRIPPI model is able to keep the advantages of both atom-atom and residue-residue interactions and is a potential knowledge-based scoring function for protein-protein docking methods. We believe that our model is robust and provides biological meanings to support protein-protein interactions.
URI: http://dx.doi.org/10.1142/S0218213010000169
http://hdl.handle.net/11536/9567
ISSN: 0218-2130
DOI: 10.1142/S0218213010000169
期刊: INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
Volume: 19
Issue: 3
起始頁: 251
結束頁: 266
顯示於類別:會議論文