Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 251-266 |
| Number of pages | 16 |
| Journal | International Journal on Artificial Intelligence Tools |
| Volume | 19 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Jun 2010 |
| Externally published | Yes |
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Keywords
- atomatom interacting preference
- knowledge-based scoring matrix
- Protein-protein interaction
- residueresidue interaction preference
ASJC Scopus subject areas
- Artificial Intelligence
Cite this
ATRIPPI : An atom-residue preference scoring function for protein-protein interactions. / Liu, Kang Ping; Hsu, Kai Cheng; Huang, Jhang Wei; Chang, Lu Shian; Yang, Jinn Moon.
In: International Journal on Artificial Intelligence Tools, Vol. 19, No. 3, 06.2010, p. 251-266.Research output: Contribution to journal › Article
}
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
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 - atomatom interacting preference
KW - knowledge-based scoring matrix
KW - Protein-protein interaction
KW - residueresidue interaction preference
UR - http://www.scopus.com/inward/record.url?scp=77954329155&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77954329155&partnerID=8YFLogxK
U2 - 10.1142/S0218213010000169
DO - 10.1142/S0218213010000169
M3 - Article
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 -