Surface analysis of peptide mass spectra to improve time and mass localization

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.

Wavelet-based method for noise characterization and rejection in high-performance liquid chromatography coupled to mass spectrometry

We present a new method for rejecting noise from HPLC-MS data sets. The algorithm reveals peptides at low concentrations by minimizing both the chemical and the random noise. The goal is reached through a systematic approach to characterize and remove the background. The data are represented as two-dimensional maps, in order to optimally exploit the complementary dimensions of separation of the peptides offered by the LC-MS technique. The virtual chromatograms, reconstructed from the spectrographic data, have proved to be more suitable to characterize the noise than the raw mass spectra. By means of wavelet analysis, it was possible to access both the chemical and the random noise, at different scales of the decomposition. The novel approach has proved to efficiently distinguish signal from noise and to selectively reject the background while preserving low-abundance peptides.