Predicting protein interface residues using easily accessible on-line resources

Abstract

It has beenmore than a decade since the completion of the Human Genome Project that provided us with a complete list of human proteins. The next obvious task is to figure out how various parts interact with each other. On that account, we re- view 10methods for protein interface prediction, which are freely available as web servers. In addition, we comparatively evaluate their performance on a common data set comprising different quality target structures. We find that using experi- mental structures and high-quality homology models, structure-basedmethods outperformthose using only protein se- quences, with global template-based approaches providing the best performance. Formoderate-qualitymodels, sequence- basedmethods often performbetter than those structure-based techniques that rely on fine atomic details. We note that post-processing protocols implemented in severalmethods quantitatively improve the results only for experimental struc- tures, suggesting that these procedures should be tuned up for computer-generatedmodels. Finally, we anticipate that advancedmeta-prediction protocols are likely to enhance interface residue prediction. Notwithstanding further improve- ments, easily accessible web servers already provide the scientific community with convenient resources for the identifica- tion of protein-protein interaction sites.