The Stowers Institutes Proteomics Center has published a novel method of using normalized spectral counts derived from a series of affinity purifications analyzed by mass spectrometry (APMS) to generate a probabilistic measure of the preference of proteins to associate with one another.
The work which allows for the assignment of probabilities not only to the interactions within well-defined protein assemblies, but also to interactions between complexes was posted today to the Web site of the Proceedings of the National Academy of Sciences (PNAS).
Large-scale APMS studies have played important roles in the assembly and analysis of comprehensive protein interaction networks for lower eukaryotes, such as yeast. But the development of such networks for human proteins has been slowed by the high cost and significant technical challenges associated with systematic studies of protein interactions.
The Stowers Institutes Proteomics Center has addressed this challenge by developing a method for building local and focused protein networks. With this computational approach, the probability for two proteins to associate is calculated from the bait-to-prey relationship alone, an improvement over other methods requiring systematic reciprocal bait-prey interactions or co-purification of preys by a third bait.
Previous protein interaction networks built using protein mass spectrometry data were largely based on binary yes/no data, where a protein is present in a sample or it is not, explains Michael Washburn, Ph.D., Director of Proteomics and senior author on the paper. We were interested in quantitative proteomics approaches. We were able to develop a method to generate more information-rich networks, where the preference of two proteins to associate within a defined complex or within a larger network assembly can be estimated using Baysian probabilities. The new approach adds more information to the analysis of protein complexes
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| Contact: Marie Jennings mfj@stowers-institute.org 816-926-4015 Stowers Institute for Medical Research Source:Eurekalert |
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