The development of selected reaction monitoring methods for targeted proteomics via empirical refinement

Abstract

Software advancements in the last several years have had a significant impact on proteomics from method development to data analysis. Herein, we detail a method, which uses our in-house developed software tool termed Skyline, for empirical refinement of candidate peptides from targeted proteins. The method consists of four main steps from generation of a testable hypothesis, method development, peptide refinement, to peptide validation. The ultimate goal is to identify the best performing peptide in terms of ionization efficiency, reproducibility, specificity, and chromatographic characteristics to monitor as a proxy for protein abundance. It is important to emphasize that this method allows the user to perform this refinement procedure in the sample matrix and organism of interest with the instrumentation available. Finally, the method is demonstrated in a case study to determine the best peptide to monitor the abundance of surfactant protein B in lung aspirates.

Figure 1

A) A schematic of a triple quadrupole operating in selected reaction monitoring mode. B) A typical spectrum created by monitoring a single peptide and all of its y-transitions

Figure 2

The workflow used for the development of peptide SRM assays. Four mains steps are outlined including development of a hypothesis, method development, validation and refinement.

Figure 3

A case study involving the development of an SRM assay to measure the abundance of SP-B in aspirate derived from neonates with respiratory distress syndrome. A) Fasta sequence of SP-B is pasted into Skyline and an in-silico digestion is performed using trypsin (user defined). All y-transitions are monitored and the transitions are exported directly into an instrument method (user defined). B) Data is imported into skyline and the abundances of the various peptides are evaluated. High responding peptides are kept while low ones are deleted. Validation is performed using dot products C) RT prediction and D) manual inspection of elution profiles. For peptide QPE no spectrum was found in the library and therefore no dot product is listed for this peptide. The dot product for DPL is significantly low. Both of these peptides also lie off the regression line shown in C (blue squares). Combined with elution profiles (data not shown) these data indicate a low probability of a positive match. For this example, these peptides would be immediately discarded due to their low response - but this is a nice example of how the combination of RT prediction, dot products, and elution profiles can be used to validate peptides.