Showing papers by "Walter R. Gilks published in 2010"
01 Jan 2010
TL;DR: It is shown how interactions in the test data set for which the training data are not informative can be automatically excluded from the prediction process, giving improved prediction success rates at the expense of restricted coverage of the testData.
Abstract: Experiments to determine the complete 3-dimensional structures of protein complexes are difficult to perform and only a limited range of such structures are available In contrast, large-scale screening experiments have identified thousands of pairwise interactions between proteins, but such experiments do not produce explicit structural information In addition, the data produced by these high through-put experiments contain large numbers of false positive results, and can be biased against detection of certain types of interaction Several methods exist that analyse such pairwise interaction data in terms of the constituent domains within proteins, scoring pairs of domain superfamilies according to their propensity to interact These scores can be used to predict the strongest domain-domain contact (the contact with the largest surface area) between interacting proteins for which the domain-level structures of the individual proteins are known We test this predictive approach on a set of pairwise protein interactions taken from the Protein Quaternary Structure (PQS) database for which the true domain-domain contacts are known ∗The authors would like to thank Emmanuel Levy of the MRC Laboratory of Molecular Biology for his help preparing this manuscript While the overall prediction success rate across the whole test data set is poor, we shown how interactions in the test data set for which the training data are not informative can be automatically excluded from the prediction process, giving improved prediction success rates at the expense of restricted coverage of the test data