Biomedical subjects

Sharon S Chen

Publications and source records attributed to Sharon S Chen.

5 recordsLinked to original sources

PubMed2006

Computational prediction of proteotypic peptides for quantitative proteomics.

Mass spectrometry-based quantitative proteomics has become an important component of biological and clinical research. Although such analyses typically assume that a protein's peptide fragments are observed with equal likelihood, only a few so-called 'proteotypic' peptides are repeatedly and consistently identified for any given protein present in a mixture. Using >600,000 peptide identifications generated by four proteomic platforms, we empirically identified >16,000 proteotypic peptides for 4,030 distinct yeast proteins. Characteristic physicochemical properties of these peptides were used to develop a computational tool that can predict proteotypic peptides for any protein from any organism, for a given platform, with >85% cumulative accuracy. Possible applications of proteotypic peptides include validation of protein identifications, absolute quantification of proteins, annotation of coding sequences in genomes, and characterization of the physical principles governing key elements of mass spectrometric workflows (e.g., digestion, chromatography, ionization and fragmentation).

Algorithms↗

PubMed2005

LC-MS solvent composition monitoring and chromatography alignment using mobile phase tracer molecules.

In the field of proteomics, reproducible liquid chromatographic description of analytes is often a key element for the differentiation or identification of proteins or peptides for clinical or biological research projects. However, analyte identification by retention time can be problematic in proteomics where lack of standardization can result in significantly different chromatography for the same analytes analyzed on different machines. Here we present a novel method of monitoring the mobile phase gradient of LC-MS/MS analyses by monitoring the ion current signal intensities of tracer molecules dissolved in the mobile phase solvents. The tracers' ion current signal intensities chronicled gradient fluctuations, did not adversely affect the number or quality of CID-based sequence identifications, and had lower run-to-run variance when compared to retention time.

Chromatography, Liquid↗

PubMed2005

Mass spectrometry-based quantitative proteomic profiling.

Quantitative proteomics involves the identification and quantitation of protein components in various biological systems. Stable isotope labelling technology, by both metabolic and chemical methods, has been the most commonly used approach for global proteome-wide profiling. Recently, its capability has been extended from labelled pairs to multiple labels, allowing for the simultaneous quantification of multiplex samples. The ion intensity-based quantitative approach has progressively gained more popularity as mass spectrometry performance has improved significantly. Although some success has been reported, it remains difficult comprehensively to characterise the global proteome, due to its enormous complexity and dynamic range. The use of sub-proteome fractionation techniques permits a simplification of the proteome and provides a practical step towards the ultimate dissection of the entire proteome. Further development of the technology for targeting sub-proteomes on a functional basis - such as selecting proteins with differential expression profiles from mass spectrometric analyses, for further mass spectrometric sequencing in an intelligent manner--is expected in the near future.

Biotin↗

PubMed2003

Abundance ratio-dependent proteomic analysis by mass spectrometry.

The goal of quantitative proteomics is to determine the identity and relative quantity of each protein present in two or more complex protein samples. Here we describe a novel approach to quantitative proteomics. It is based on a highly accurate algorithm for the automated quantification of chromatographically fractionated, isotope-coded affinity-tagged peptides and MALDI quadrupole time-of-flight tandem mass spectrometry for their identification. The method is capable of detecting and selectively identifying those proteins within a complex mixture that show a difference in relative abundance. We demonstrate the effectiveness and the versatility of this approach in the analysis of a standard protein mixture, protein expression profiling in a human prostate cancer cell line model, and identification of the specific components of the multiprotein transcriptional machinery in S. cerevisiae.

Humans↗

PubMedDate not supplied

Improving mass and liquid chromatography based identification of proteins using bayesian scoring.

We present a method for peptide and protein identification based on LC-MS profiling. The method identified peptides at high-throughput without expending the sequencing time necessary for CID spectra based identification. The measurable peptide properties of mass and liquid chromatographic elution conditions are used to characterize and differentiate peptide features, and these peptide features are matched to a reference database from previously acquired and archived LC-MS/MS experiments to generate sequence assignments. The matches are scored according to the probability of an overlap between the peptide feature and the database peptides resulting in a ranked list of possible peptide sequences for each peptide submitted. This method resulted in 6 times more peptide sequence identifications from a single LC-MS analysis of yeast than from shotgun peptide sequencing using LC-MS/MS.

Algorithms↗