Evolutionary profiles improve protein-protein interaction prediction from sequence.

TitleEvolutionary profiles improve protein-protein interaction prediction from sequence.
Publication TypeJournal Article
Year of Publication2015
AuthorsHamp, T, Rost, B
Date Published2015 Feb 4

MOTIVATION: Many methods predict the physical interaction between two proteins (protein-protein interactions; PPIs) from sequence alone. Their performance drops substantially for proteins not used for training.

RESULTS: Here, we introduce a new approach to predict PPIs from sequence alone which is based on evolutionary profiles and profile-kernel support vector machines. It improved over the state-of-the-art, in particular for proteins that are sequence-dissimilar to proteins with known interaction partners. Filtering by gene expression data increased accuracy further for the few, most reliably predicted interactions (low recall). The overall improvement was so substantial that we compiled a list of the most reliably predicted PPIs in human. Our method makes a significant difference for biology because it improves most for the majority of proteins without experimental annotations.

AVAILABILITY AND IMPLEMENTATION: Implementation and most reliably predicted human PPIs available at https://rostlab.org/owiki/index.php/Profppikernel.

CONTACT: rost@in.tum.de Supplementary information: Supplementary data are available at Bioinformatics online.

Alternate JournalBioinformatics
PubMed ID25657331