Name
Title | ||
|---|---|---|
ATRIPPI: An Atom-residue Preference Scoring Function for Protein-protein Interactions |
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 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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/BIBE.2009.45 | International Journal on Artificial Intelligence Tools |
Keywords | Field | DocType |
protein protein interaction,score function | Electrostatics,Protein–protein interaction,Biological system,Computer science,Protein engineering,Docking (dog),Atom,van der Waals force,Molecular biophysics,Artificial intelligence,Hydrogen bond,Machine learning | Conference |
Volume | Issue | ISSN |
19 | 3 | 2471-7819 |
ISBN | Citations | PageRank |
978-0-7695-3656-9 | 0 | 0.34 |
References | Authors | |
3 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| KANG-PING LIU | 1 | 0 | 1.35 |
| Kai-Cheng Hsu | 2 | 82 | 8.27 |
| Jhang-Wei Huang | 3 | 36 | 2.54 |
| LU-SHIAN CHANG | 4 | 0 | 0.34 |
| Jinn-moon Yang | 5 | 364 | 35.89 |