Name
Abstract | ||
|---|---|---|
The ability to identify protein-protein interaction sites and to detect specific amino acid residues that contribute to the specificity and affinity of protein interactions has important implications for problems ranging from rational drug design to analysis of metabolic and signal transduction networks.We have developed a two-stage method consisting of a support vector machine (SVM) and a Bayesian classifier for predicting surface residues of a protein that participate in protein-protein interactions. This approach exploits the fact that interface residues tend to form clusters in the primary amino acid sequence. Our results show that the proposed two-stage classifier outperforms previously published sequence-based methods for predicting interface residues. We also present results obtained using the two-stage classifier on an independent test set of seven CAPRI (Critical Assessment of PRedicted Interactions) targets. The success of the predictions is validated by examining the predictions in the context of the three-dimensional structures of protein complexes. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1093/bioinformatics/bth920 | ISMB/ECCB (Supplement of Bioinformatics) |
Keywords | Field | DocType |
primary amino acid sequence,proposed two-stage classifier,bayesian classifier,two-stage classifier,specific amino acid residue,protein interaction site,protein interaction,two-stage method,protein interface residue,protein complex,interface residue,amino acid sequence,rational drug design,protein protein interaction,amino acid,support vector machine | Protein–protein interaction,Computer science,Artificial intelligence,Classifier (linguistics),Peptide sequence,Binding site,Pattern recognition,Naive Bayes classifier,Drug design,Support vector machine,Bioinformatics,Machine learning,Test set | Conference |
Volume | Issue | ISSN |
20 Suppl 1 | 1 | 1367-4811 |
Citations | PageRank | References |
45 | 4.14 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Changhui Yan | 1 | 196 | 17.58 |
| Drena Dobbs | 2 | 423 | 35.43 |
| Vasant Honavar | 3 | 3353 | 468.10 |