Name
Abstract | ||
|---|---|---|
The identification of proteinprotein interaction sites is an essential intermediate step for mutant design and the prediction of protein networks. In recent years a significant number of methods have been developed to predict these interface residues and here we review the current status of the field. Progress in this area requires a clear view of the methodology applied, the data sets used for training and testing the systems, and the evaluation procedures. We have analysed the impact of a representative set of features and algorithms and highlighted the problems inherent in generating reliable protein data sets and in the posterior analysis of the results. Although it is clear that there have been some improvements in methods for predicting interacting sites, several major bottlenecks remain. Proteins in complexes are still under-represented in the structural databases and in particular many proteins involved in transient complexes are still to be crystallized. We provide suggestions for effective feature selection, and make it clear that community standards for testing, training and performance measures are necessary for progress in the field. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1093/bib/bbp021 | BRIEFINGS IN BIOINFORMATICS |
Keywords | Field | DocType |
protein-protein interaction,binding sites,protein complexes,prediction,machine learning | Data set,Protein–protein interaction,Feature selection,Biology,Bioinformatics | Journal |
Volume | Issue | ISSN |
10 | 3 | 1467-5463 |
Citations | PageRank | References |
48 | 1.73 | 22 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Iakes Ezkurdia | 1 | 67 | 3.40 |
| Lisa Bartoli | 2 | 76 | 3.49 |
| Piero Fariselli | 3 | 851 | 96.03 |
| Rita Casadio | 4 | 1032 | 108.10 |
| Alfonso Valencia | 5 | 2577 | 322.43 |
| Michael L Tress | 6 | 183 | 11.61 |