Publication Date:
2012-06-12
Description:
Background: Affinity-Purification Mass-Spectrometry (AP-MS) provides a powerful means of identifyingprotein complexes and interactions. Several important challenges exist in interpreting theresults of AP-MS experiments. First, the reproducibility of AP-MS experimental replicatescan be low, due both to technical variability and the dynamic nature of protein interactions inthe cell. Second, the identification of true protein-protein interactions in AP-MS experimentsis subject to inaccuracy due to high false negative and false positive rates. Severalexperimental approaches can be used to mitigate these drawbacks, including the use ofreplicated and control experiments and relative quantification to sensitively distinguish trueinteracting proteins from false ones. Results: To address the issues of reproducibility and accuracy of protein-protein interactions, weintroduce a two-step method, called ROCS, which makes use of Indicator Proteins to selectreproducible AP-MS experiments, and of Confidence Scores to select specific protein-proteininteractions. The Indicator Proteins account for measures of protein identification as well asprotein reproducibility, effectively allowing removal of outlier experiments that contributenoise and affect downstream inferences. The filtered set of experiments is then used in theProtein-Protein Interaction (PPI) scoring step. Prey protein scoring is done by computing aConfidence Score, which accounts for the probability of occurrence of prey proteins in thebait experiments relative to the control experiment, where the significance cutoff parameter isestimated by simultaneously controlling false positives and false negatives against metrics offalse discovery rate and biological coherence respectively. In summary, the ROCS methodrelies on automatic objective criterions for parameter estimation and error-controlledprocedures. We illustrate the performance of our method by applying it to five previously published AP-MS experiments, each containing well characterized protein interactions,allowing for systematic benchmarking of ROCS. We show that our method may be used onits own to make accurate identification of specific, biologically relevant protein-proteininteractions or in combination with other AP-MS scoring methods to significantly improveinferences. Conclusions: Our method addresses important issues encountered in AP-MS datasets, making ROCS a verypromising tool for this purpose, either on its own or especially in conjunction with other Methods: We anticipate that our methodology may be used more generally in proteomicsstudies and databases, where experimental reproducibility issues arise. The method isimplemented in the R language, and is available as an R package called "ROCS", freelyavailable from the CRAN repository http://cran.r-project.org/.
Electronic ISSN:
1471-2105
Topics:
Biology
,
Computer Science
Permalink