3 Ergebnisse
Suchergebnisse
1 - 3 von 3
- KonferenzbeitragClassifying permanent and transient protein interactions(German Conference on Bioinformatics, 2006) Schroeder, MichaelCurrently much research is devoted to the characterization and classification of transient and permanent protein-protein interactions. From the literature, we take data sets consisting of 161 permanent (65 homodimers, 96 heterodimers) and 242 transient interactions. We collect over 300 interface attributes relating to size, physiochemical properties, interaction propensities, and secondary structure elements. Our major discovery is a surprisingly simple relationship not yet reported in the literature: interactions with the same molecular weight or very big interfaces are per- manent and otherwise transient. We train a support vector machine and achieve the following results: Molecular weight difference alone achieves 80% success rate. To- gether with the size of the buried surface the success rate improves to 89%. Adding water at the interface and the number of hydrophobic contacts we achieve a success rate of 97%.
- Editiertes Buch
- KonferenzbeitragA novel, comprehensive method to detect and predict protein-protein interactions applied to the study of vesicular trafficking(German Conference on Bioinformatics, 2006) Schroeder, MichaelMotivation. Computational methods to predict protein-protein interactions are of great need. They can help to formulate hypotheses, guide experimental research and serve as additional measures to assess the quality of data obtained in high-throughput interaction experiments. Here, we describe a fully automated threestep procedure to predict and confirm protein-protein interactions. By maximising the information from text mining of the biomedical literature, data from interaction databases, and from available protein structures, we aim at generating a comprehensive picture of known and novel potential interactions between a given set of proteins. Results. A recent proteomics assay to identify the protein machinery involved in vesicular trafficking between the biosynthetic and the endosomal compartments revealed 35 proteins that were found as part of membrane coats on liposomes. When applying our method to this data set, we are able to reconstruct most of the interactions known to the molecular biologist. In addition, we predict novel interactions, among these potential linkers of the AP-1 and the Arp2/3 complex to membrane-bound proteins as well as a potential GTPase-GTPase effector interaction. Conclusions. Our method allows for a comprehensive network reconstruction that can assist the molecular biologist. Predicted interactions are backed up by structural or experimental evidence and can be inferred at varying levels of confidence. Our method pinpoints existing key interactions and can facilitate the generation of hypotheses.