An integrative approach for predicting interactions of protein regions.
BACKGROUND
Protein-protein interactions are commonly mediated by the physical contact of distinct protein regions. Computational identification of interacting protein regions aids in the detailed understanding of protein networks and supports the prediction of novel protein interactions and the reconstruction of protein complexes.


RESULTS
We introduce an integrative approach for predicting protein region interactions using a probabilistic model fitted to an observed protein network. In particular, we consider globular domains, short linear motifs and coiled-coil regions as potential protein-binding regions. Possible cooperations between multiple regions within the same protein are taken into account. A.negrained confidence system allows for varying the impact of specific protein interactions and region annotations on the modeling process. We apply our prediction approach to a large training set using a maximum likelihood method, compare different scoring functions for region interactions and validate the predicted interactions against a collection of experimentally observed interactions. In addition, we analyze prediction performance with respect to the inclusion of different region types, the incorporation of confidence values for training data and the utilization of predicted protein interactions.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Schelhorn
Sven-Eric
SE
Max Planck Institute for Informatics, Saarbrücken, Germany.
Lengauer
Thomas
T
Albrecht
Mario
M
D000595:Amino Acid Sequence; D001665:Binding Sites; D003198:Computer Simulation; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000648:ultrastructure; D020539:Sequence Analysis, Protein / Q000379:methods*; D018511:Systems Integration
D011506:Proteins