Identifying protein complexes directly from high-throughput TAP data with Markov random fields

Wasinee Rungsarityotin¹^,3 , Roland Krause¹^,2 , Arno Schödl³ and Alexander Schliep¹

¹Max Planck Institute for Molecular Genetics, Department of Computational Molecular Biology, Ihnestr. 73, D-14195 Berlin, Germany

²Max Planck Institute for Infection Biology, Department of Cellular Microbiology, Charitéplatz 1, D-10117 Berlin, Germany

³Think-cell software, Invalidenstr. 43, D-10115 Berlin, Germany

author email corresponding author email

BMC Bioinformatics 2007, 8:482doi:10.1186/1471-2105-8-482


Published:	19 December 2007

Abstract

Background

Predicting protein complexes from experimental data remains a challenge due to limited resolution and stochastic errors of high-throughput methods. Current algorithms to reconstruct the complexes typically rely on a two-step process. First, they construct an interaction graph from the data, predominantly using heuristics, and subsequently cluster its vertices to identify protein complexes.

Results

We propose a model-based identification of protein complexes directly from the experimental observations. Our model of protein complexes based on Markov random fields explicitly incorporates false negative and false positive errors and exhibits a high robustness to noise. A model-based quality score for the resulting clusters allows us to identify reliable predictions in the complete data set. Comparisons with prior work on reference data sets shows favorable results, particularly for larger unfiltered data sets. Additional information on predictions, including the source code under the GNU Public License can be found at http://algorithmics.molgen.mpg.de/Static/Supplements/ProteinComplexes.

Conclusion

We can identify complexes in the data obtained from high-throughput experiments without prior elimination of proteins or weak interactions. The few parameters of our model, which does not rely on heuristics, can be estimated using maximum likelihood without a reference data set. This is particularly important for protein complex studies in organisms that do not have an established reference frame of known protein complexes.