TY - JOUR
T1 - ATRIPPI
T2 - An atom-residue preference scoring function for protein-protein interactions
AU - Liu, Kang Ping
AU - Hsu, Kai Cheng
AU - Huang, Jhang Wei
AU - Chang, Lu Shian
AU - Yang, Jinn Moon
N1 - Funding Information:
J.-M. Yang was supported by National Science Council and partial support of the ATU plan by MOE. Authors are grateful to both the hardware and software supports of the Structural Bioinformatics Core Facility at National Chiao Tung University.
Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2010/6
Y1 - 2010/6
N2 - 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 atomatom and residueresidue 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 proteinprotein interactions.
AB - 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 atomatom and residueresidue 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 proteinprotein interactions.
KW - Protein-protein interaction
KW - atomatom interacting preference
KW - knowledge-based scoring matrix
KW - residueresidue interaction preference
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U2 - 10.1142/S0218213010000169
DO - 10.1142/S0218213010000169
M3 - Article
AN - SCOPUS:77954329155
VL - 19
SP - 251
EP - 266
JO - International Journal on Artificial Intelligence Tools
JF - International Journal on Artificial Intelligence Tools
SN - 0218-2130
IS - 3
ER -