Accurate samples for testing mass spectrometry based peptide quantification algorithms

Quantitative proteomic experiments use algorithms to estimate peptide abundances from spectra. The efficacy of these algorithms is usually tested against a contrived mixture of proteins. However, the numerous error sources in mass spectrometry based proteomics experiments must be accounted for to evaluate novel algorithms in an unbiased manner. We set out to examine how to best utilize a set of calibration data for this purpose. We demonstrated that calibration data will have substantial noise whose magnitude depends on whether comparisons are made within or across experiments. We then propose a novel method of testing algorithms that uses the natural isotopic envelope of peptides to minimize measurement noise. We show that the variability of isotopic peptide ratios is an order of magnitude lower with this approach than with typical standard protein mixtures. We conclude by demonstrating the usefulness of this new technique in the analysis of typical peak picking algorithms.