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^*

来源：International Journal on Artificial Intelligence Tools, 2010, 19(3): 251-266.

DOI：10.1142/S0218213010000169

• Protein-protein interaction
• atom-atom interacting preference
• knowledge-based scoring matrix
• residue-residue interaction preference

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.

• 出版日期2010-6