Search

The aim of the work was to explore usefulness of artificial neural network (ANN) analysis for the evaluation of proteomics data. The analysis was applied to the data generated by the widely used protein identification program Sequest, completed with several structural parameters readily calculated from peptide molecular formulas. Proteins from yeast cells were identified based on the MS/ MS spectra of peptides. The constructed ANN was demonstrated to classify automatically as either 'good' or 'bad' the peptide MS/MS spectra otherwise classified manually. An appropriately trained ANN proves to be a high-throughput tool facilitating examination of Sequest's results. ANNs are recommended as a means of automatic processing of large amounts of MS/MS data, which normally must be considered in the analysis of complex mixtures of proteins in proteomics.

A new denoising and peak picking algorithm (MEND, matched filtration with experimental noise determination) for analysis of LC--MS data is described. The algorithm minimizes both random and chemical noise in order to determine MS peaks corresponding to sample components. Noise characteristics in the data set are experimentally determined and used for efficient denoising. MEND is shown to enable low-intensity peaks to be detected, thus providing additional useful information for sample analysis. The process of denoising, performed in the chromatographic time domain, does not distort peak shapes in the m/z domain, allowing accurate determination of MS peak centroids, including low-intensity peaks. MEND has been applied to denoising of LC-MALDI-TOF-MS and LC-ESI-TOF-MS data for tryptic digests of protein mixtures. MEND is shown to suppress chemical and random noise and baseline fluctuations, as well as filter out false peaks originating from the matrix (MALDI) or mobile phase (ESI). In addition, MEND is shown to be effective for protein expression analysis by allowing selection of a large number of differentially expressed ICAT pairs, due to increased signal-to-noise ratio and mass accuracy.

This work explores the use of 20-[micro]m-i.d. polymeric polystyrene-divinylbenzene monolithic nanocapillary columns for the LC-ESI-MS analysis of tryptic digest peptide mixtures. In contrast to the packing of microparticles, capillary columns were prepared, without the need of high pressure, in fused-silica capillaries, by thermally induced in situ copolymerization of styrene and divinylbenzene. The polymerization conditions and mobile-phase composition were optimized for chromatographic performance leading to efficiencies over 100 000 plates/m for peptide separations. High mass sensitivity (~10 amol of peptides) in the MS and MS/MS modes using an ion trap MS was found, a factor of up to 20-fold improvement over 75-[micro]m-i.d. nanocolumns. A wide linear dynamic range (~4 orders of magnitude) was achieved, and good run-to-run and column-to-column reproducibility of isocratic and gradient elution separations were found. As samples, both model proteins and tissue extracts were employed. Gradient nano-LC-MS analysis of a proteolytic digest of a tissue extract, equivalent to a sample size of ~1000 cells injected, is presented.

Electrically driven separations which contain nanoparticles offer detection and separation advantages but are often difficult to reproduce. To address possible sources of separation inconsistencies, anionic functionalized gold nanoparticles are thoroughly characterized and subsequently included in continuous full filling capillary electrophoresis separations of varying concentrations of three small molecules. Citrate stabilized gold nanospheres are functionalized with 11-mercaptoundecanoic acid, 6-mercaptohexanoic acid, or thioctic acid self-assembled monolayers (SAMs) and characterized using dynamic light scattering, extinction spectroscopy, zeta potential, and X-ray photoelectron spectroscopy prior to use in capillary electrophoresis. Several important trends are noted. First, the stability of these anionic nanoparticles in the capillary improves with increased ligand packing density as indicated by a ratio of absorbance collected at 520 to 600 nm. Second, increasing nanoparticle concentration from 0 to 2 nM (0-0.0025%, w/w) minimally impacts analyte migration times; however, when higher nanoparticle concentrations are included within the capillary, nanoparticle aggregation occurs which induces separation inconsistencies. Third, analyte peak areas are most significantly impacted as their concentration decreases. These trends are attributed to both sample enrichment and electrostatic interactions between the anionic carboxylic acid functionalized gold nanoparticles and sample. These important findings suggest that sample concentration-induced conductivity differences between the sample matrix and separation buffer as well as SAM packing density are important parameters to both characterize and consider when nanoparticles are used during continuous full filling separations and their subsequent use to enhance spectroscopic signals to improve in-capillary analyte detection limits. [ABSTRACT FROM AUTHOR]

This work explores the use of 20-μm-i.d. polymeric polystyrene-divinylbenzene monolithic nanocapillary columns for the LC-ESI-MS analysis of tryptic digest peptide mixtures. In contrast to the packing of microparticles, capillary columns were prepared, without the need of high pressure, in fused-silica capillaries, by thermally induced in situ copolymerization of styrene and divinylbenzene. The polymerization conditions and mobile-phase composition were optimized for chromatographic performance leading to efficiencies over 100000 plates/m for peptide separations. High mass sensitivity (∼10 amol of peptides) in the MS and MS/MS modes using an ion trap MS was found, a factor of up to 20-fold improvement over 75-μmi.d. nanocolumns. A wide linear dynamic range (∼4 orders of magnitude) was achieved, and good run-to-run and column-to-column reproducibility of isocratic and gradient elution separations were found. As samples, both model proteins and tissue extracts were employed. Gradient nano-LC-MS analysis of a proteolytic digest of a tissue extract, equivalent to a sample size of ∼1000 cells injected, is presented. [ABSTRACT FROM AUTHOR]

The aim of the work was to explore usefulness of artificial neural network (ANN) analysis for the evaluation of proteomics data. The analysis was applied to the data generated by the widely used protein identification pro- gram Sequest, completed with several structural parameters readily calculated from peptide molecular formulas. Proteins from yeast cells were identified based on the MS/MS spectra of peptides. The constructed ANN was demonstrated to classify automatically as either good or "bad" the peptide MS/MS spectra otherwise classified manually. An appropriately trained ANN proves to be a high-throughput tool facilitating examination of Sequest's results. ANNs are recommended as a means of automatic processing of large amounts of MS/MS data, which normally must be considered in the analysis of complex mixtures of proteins in proteomics. [ABSTRACT FROM AUTHOR]