An integrative approach for predicting interactions of protein regions Export

@article{citeulike:3106931, abstract = {Motivation: Protein-protein interactions are commonly mediated by the physical contact of distinct protein regions. Computational identi.cation 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.tted 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 con.dence system allows for varying the impact of speci.c 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 con.dence values for training data and the utilization of predicted protein interactions. Contact: mario.albrecht@mpi-inf.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btn290}, author = {Schelhorn, Sven-Eric and Lengauer, Thomas and Albrecht, Mario}, citeulike-article-id = {3106931}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btn290}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/24/16/i35}, day = {15}, doi = {10.1093/bioinformatics/btn290}, journal = {Bioinformatics}, keywords = {domain, interaction, prediction, protein}, month = {August}, number = {16}, pages = {i35--41}, posted-at = {2008-08-11 04:13:26}, priority = {4}, title = {An integrative approach for predicting interactions of protein regions}, url = {http://dx.doi.org/10.1093/bioinformatics/btn290}, volume = {24}, year = {2008} }

TY - JOUR ID - citeulike:3106931 L3 - citeulike-article-id:3106931 N2 - Motivation: Protein-protein interactions are commonly mediated by the physical contact of distinct protein regions. Computational identi.cation 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.tted 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 con.dence system allows for varying the impact of speci.c 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 con.dence values for training data and the utilization of predicted protein interactions. Contact: mario.albrecht@mpi-inf.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btn290 IS - 16 JF - Bioinformatics EP - 41 TI - An integrative approach for predicting interactions of protein regions VL - 24 SP - i35 KW - domain KW - interaction KW - prediction KW - protein AU - Schelhorn, Sven-Eric AU - Lengauer, Thomas AU - Albrecht, Mario PY - 2008/08/15/ UR - http://dx.doi.org/10.1093/bioinformatics/btn290 ER -