Browsing by Author "Chang, Lu-Shian"

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ATRIPPI: AN ATOM-RESIDUE PREFERENCE SCORING FUNCTION FOR PROTEIN-PROTEIN INTERACTIONS
(2010-06-01)
Liu, Kang-Ping
;
Hsu, Kai-Cheng
;
Huang, Jhang-Wei
;
Chang, Lu-Shian
;
Yang, Jinn-Moon
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.
ATRIPPI: An atom-residue preference scoring function for protein-protein interactions
(2009)
Liu, Kang-Ping
;
Chang, Lu-Shian
;
Yang, Jinn-Moon
;
生物科技學系
;
Department of Biological Science and Technology
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 Wools 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.