Name
Title | ||
|---|---|---|
In silico screening of protein-protein interactions with all-to-all rigid docking and clustering: an application to pathway analysis. |
Abstract | ||
|---|---|---|
We propose a computational screening system of protein-protein interactions using tertiary structure data. Our system combines all-to-all protein docking and clustering to find interacting protein pairs. We tuned our prediction system by applying various parameters and clustering algorithms and succeeded in outperforming previous methods. This method was also applied to a biological pathway estimation problem to show its use in network level analysis. The structural data were collected from the Protein Data Bank, PDB. Then all-to-all docking among target protein structures was conducted using a conventional protein-protein docking software package, ZDOCK. The highest-ranked 2000 decoys were clustered based on structural similarity among the predicted docking forms. The features of generated clusters were analyzed to estimate the biological relevance of protein-protein interactions. Our system achieves a best F-measure value of 0.43 when applied to a subset of general protein-protein docking benchmark data. The same system was applied to protein data in a bacterial chemotaxis pathway, utilizing essentially the same parameter set as the benchmark data. We obtained 0.45 for the F-measure value. The proposed approach to computational PPI detection is a promising methodology for mediating between structural studies and systems biology by utilizing cumulative protein structure data for pathway analysis. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1142/S0219720009004461 | J. Bioinformatics and Computational Biology |
Keywords | Field | DocType |
protein docking,protein protein interaction,high performance computing | Protein–protein interaction prediction,Protein tertiary structure,Protein–protein interaction,Biology,Docking (dog),Protein–ligand docking,Macromolecular docking,Artificial intelligence,Bioinformatics,Cluster analysis,Protein Data Bank,Machine learning | Journal |
Volume | Issue | ISSN |
7 | 6 | 1757-6334 |
Citations | PageRank | References |
10 | 0.88 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y Matsuzaki | 1 | 172 | 41.30 |
| Yusuke Matsuzaki | 2 | 10 | 0.88 |
| Toshiyuki Sato | 3 | 10 | 0.88 |
| Yutaka Akiyama | 4 | 172 | 37.62 |