title: Evolutionary profiles improve protein-protein interaction prediction from sequence.
abstract: BACKGROUND
  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.
  
  
  METHODS
  Implementation and most reliably predicted human PPIs available at https://rostlab.org/owiki/index.php/Profppikernel.
authors: Department of Informatics, Bioinformatics & Computational Biology, TUM (Technische Universität München)-I12, Boltzmannstr. 3, 85748 Garching/Munich, Germany.
authors: Tobias
authors: T
authors: Hamp
authors: Department of Informatics, Bioinformatics & Computational Biology, TUM (Technische Universität München)-I12, Boltzmannstr. 3, 85748 Garching/Munich, Germany.
authors: Burkhard
authors: B
authors: Rost
mesh_terms: D019295:Computational Biology / Q000379:methods*; D006801:Humans; D008958:Models, Molecular; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D060388:Support Vector Machine
chemical_list: D011506:Proteins