Results 1 - 10 of 944

Large-scale analysis of the yeast proteome by multidimensional protein identification technology

by Michael P Washburn , Dirk Wolters , † , John R Yates Iii - Nat. Biotechnol , 2001
"... We describe a largely unbiased method for rapid and large-scale proteome analysis by multidimensional liquid chromatography, tandem mass spectrometry, and database searching by the SEQUEST algorithm, named multidimensional protein identification technology (MudPIT). MudPIT was applied to the proteo ..."
Abstract - Cited by 276 (5 self) - Add to MetaCart
We describe a largely unbiased method for rapid and large-scale proteome analysis by multidimensional liquid chromatography, tandem mass spectrometry, and database searching by the SEQUEST algorithm, named multidimensional protein identification technology (MudPIT). MudPIT was applied to the proteome of the Saccharomyces cerevisiae strain BJ5460 grown to mid-log phase and yielded the largest proteome analysis to date. A total of 1,484 proteins were detected and identified. Categorization of these hits demonstrated the ability of this technology to detect and identify proteins rarely seen in proteome analysis, including lowabundance proteins like transcription factors and protein kinases. Furthermore, we identified 131 proteins with three or more predicted transmembrane domains, which allowed us to map the soluble domains of many of the integral membrane proteins. MudPIT is useful for proteome analysis and may be specifically applied to integral membrane proteins to obtain detailed biochemical information on this unwieldy class of proteins.

Correlation between protein and mRNA abundance in yeast

by Steven P. Gygi, Yvan Rochon, B. Robert Franza, Ruedi Aebersold, W. Zhou, J. Vogelstein, M. A. Basrai, D. E. Bassett, P. Hieter, B. Vogelstein, K. W. Kinzler - Mol Cell Biol , 1999
"... We have determined the relationship between mRNA and protein expression levels for selected genes expressed in the yeast Saccharomyces cerevisiae growing at mid-log phase. The proteins contained in total yeast cell lysate were separated by high-resolution two-dimensional (2D) gel electrophoresis. Ov ..."
Abstract - Cited by 205 (2 self) - Add to MetaCart
We have determined the relationship between mRNA and protein expression levels for selected genes expressed in the yeast Saccharomyces cerevisiae growing at mid-log phase. The proteins contained in total yeast cell lysate were separated by high-resolution two-dimensional (2D) gel electrophoresis. Over 150 protein spots were excised and identified by capillary liquid chromatography-tandem mass spectrometry (LC-MS/MS). Protein spots were quantified by metabolic labeling and scintillation counting. Corresponding mRNA levels were calculated from serial analysis of gene expression (SAGE) frequency tables (V. E. Velculescu, L. Zhang,
(Show Context)

A dynamic programming approach to de novo peptide sequencing via tandem mass spectrometry, in: Symposium on Discrete Algorithms

by Ting Chen , Ming-Yang Kao , Matthew Tepel , John Rush , George M Church , 2000
"... Abstract Tandem mass spectrometry fragments a large number of molecules of the same peptide sequence into charged molecules of prefix and suffix peptide subsequences, and then measures mass/charge ratios of these ions. The de novo peptide sequencing problem is to reconstruct the peptide sequence fr ..."
Abstract - Cited by 90 (5 self) - Add to MetaCart
Abstract Tandem mass spectrometry fragments a large number of molecules of the same peptide sequence into charged molecules of prefix and suffix peptide subsequences, and then measures mass/charge ratios of these ions. The de novo peptide sequencing problem is to reconstruct the peptide sequence from a given tandem mass spectral data of k ions. By implicitly transforming the spectral data into an NC-spectrum graph G = (V, E) where |V | = 2k + 2, we can solve this problem in O(|V ||E|) time and O(|V | 2 ) space using dynamic programming. For an ideal noise-free spectrum with only b-and y-ions, we improve the algorithm to O(|V | + |E|) time and O(|V |) space. Our approach can be further used to discover a modified amino acid in O(|V ||E|) time. The algorithms have been implemented and tested on experimental data.
(Show Context)

Interpretation of Shotgun Proteomic Data: The Protein Inference Problem

by Alexey I. Nesvizhskii, Ruedi Aebersold - Mol. Cell. Proteomics , 2005
"... The shotgun proteomic strategy based on digesting proteins into peptides and sequencing them using tandem mass spectrometry and automated database searching has become the method of choice for identifying proteins in most large scale studies. However, the peptide-centric nature of shotgun proteomics ..."
Abstract - Cited by 85 (8 self) - Add to MetaCart
The shotgun proteomic strategy based on digesting proteins into peptides and sequencing them using tandem mass spectrometry and automated database searching has become the method of choice for identifying proteins in most large scale studies. However, the peptide-centric nature of shotgun proteomics complicates the analysis and biological interpretation of the data especially in the case of higher eukaryote organisms. The same peptide sequence can be present in multiple different proteins or protein isoforms. Such shared peptides therefore can lead to ambiguities in determining the identities of sample proteins. In this article we illustrate the difficulties of interpreting shotgun proteomic data and discuss the need for common nomenclature and transparent informatic approaches. We also discuss related issues such as the
(Show Context)

SCOPE: a probabilistic model for scoring tandem mass spectra against a peptide database

by Vineet Bafna, Nathan Edwards - Bioinformatics , 2001
"... Proteomics, or the direct analysis of the expressed protein components of a cell, is critical to our understanding of cellular biological processes in normal and diseased tissue. A key requirement for its success is the ability to identify proteins in complex mixtures. Recent technological advances ..."
Abstract - Cited by 79 (7 self) - Add to MetaCart
Proteomics, or the direct analysis of the expressed protein components of a cell, is critical to our understanding of cellular biological processes in normal and diseased tissue. A key requirement for its success is the ability to identify proteins in complex mixtures. Recent technological advances in tandem mass spectrometry has made it the method of choice for high-throughput identification of proteins. Unfortunately, the software for unambiguously identifying peptide sequences has not kept pace with the recent hardware improvements in mass spectrometry instruments. Critical for reliable high-throughput protein identification, scoring functions evaluate the quality of a match between experimental spectra and a database peptide. Current scoring function technology relies heavily on ad-hoc parameterization and manual curation by experienced mass spectrometrists. In this work, we propose a two-stage stochastic model for the observed MS/MS spectrum, given a peptide. Our model explicitly incorporates fragment ion probabilities, noisy spectra, and instrument measurement error. We describe how to compute this probability based score efficiently, using a dynamic programming technique. A prototype implementation demonstrates the effectiveness of the model. Contact:

Assigning significance to peptides identified by tandem mass spectrometry using decoy databases

by Lukas Käll, John D. Storey, Michael J. Maccoss, William Stafford Noble - J. Proteome Res , 2008
"... Automated methods for assigning peptides to observed tandem mass spectra typically return a list of peptide-spectrum matches, ranked according to an arbitrary score. In this article, we describe methods for converting these arbitrary scores into more useful statistical significance measures. These m ..."
Abstract - Cited by 65 (13 self) - Add to MetaCart
Automated methods for assigning peptides to observed tandem mass spectra typically return a list of peptide-spectrum matches, ranked according to an arbitrary score. In this article, we describe methods for converting these arbitrary scores into more useful statistical significance measures. These methods employ a decoy sequence database as a model of the null hypothesis, and use false discovery rate (FDR) analysis to correct for multiple testing. We first describe a simple FDR inference method and then describe how estimating and taking into account the percentage of incorrectly identified spectra in the entire data set can lead to increased statistical power.

The Paragon Algorithm, a Next Generation Search Engine That Uses Sequence Temperature Values and Feature Probabilities to Identify Peptides from Tandem Mass Spectra * □S

by Ignat V. Shilov, Sean L. Seymour, Alpesh A. Patel, Alex Loboda, Wilfred H. Tang, Sean P. Keating, Christie L. Hunter, Lydia M. Nuwaysir, Daniel A. Schaeffer
"... The Paragon TM Algorithm, a novel database search engine for the identification of peptides from tandem mass spectrometry data, is presented. Sequence Temperature Values are computed using a sequence tag algorithm, allowing the degree of implication by an MS/MS spectrum of each region of a database ..."
Abstract - Cited by 62 (0 self) - Add to MetaCart
The Paragon TM Algorithm, a novel database search engine for the identification of peptides from tandem mass spectrometry data, is presented. Sequence Temperature Values are computed using a sequence tag algorithm, allowing the degree of implication by an MS/MS spectrum of each region of a database to be determined on a continuum. Counter to conventional approaches, features such as modifications, substitutions, and cleavage events are modeled with probabilities rather than by discrete user-controlled settings to consider or not consider a feature. The use of feature probabilities in conjunction with Sequence Temperature Values allows for a very large increase in the effective search space with only a very small increase in the actual number of hypotheses that must be scored. The algorithm has a new kind of user
(Show Context)

Toward a human blood serum proteome: analysis by multidimensional separation coupled with mass spectrometry

by Joshua N. Adkins, Susan M. Varnum, Kenneth J. Auberry, Ronald J. Moore, Nicolas H. Angell, Richard D. Smith, David L. Springer, Joel G. Pounds - Mol. Cell. Proteomics , 2002
"... Blood serum is a complex body fluid that contains various proteins ranging in concentration over at least 9 orders of magnitude. Using a combination of mass spectrometry technologies with improvements in sample preparation, we have performed a proteomic analysis with submilliliter quantities of seru ..."
Abstract - Cited by 59 (4 self) - Add to MetaCart
Blood serum is a complex body fluid that contains various proteins ranging in concentration over at least 9 orders of magnitude. Using a combination of mass spectrometry technologies with improvements in sample preparation, we have performed a proteomic analysis with submilliliter quantities of serum and increased the measurable concentration range for proteins in blood serum beyond previous reports. We have detected 490 proteins in serum by on-line reversed-phase microcapillary liquid chromatography coupled with ion trap mass spectrometry. To perform this analysis, immunoglobulins were removed from serum using protein A/G, and the remaining proteins were digested with trypsin. Resulting peptides were separated by strong cation exchange chromatography into distinct fractions prior to analysis. This separation resulted in a
(Show Context)

Detecting differential and correlated protein expression in label-free shotgun proteomics

by Bing Zhang , Nathan C Verberkmoes , Michael A Langston , | Edward Uberbacher , Robert L Hettich , Nagiza F Samatova - J Proteome Res , 2006
"... Recent studies have revealed a relationship between protein abundance and sampling statistics, such as sequence coverage, peptide count, and spectral count, in label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) shotgun proteomics. The use of sampling statistics offers a promising ..."
Abstract - Cited by 57 (3 self) - Add to MetaCart
Recent studies have revealed a relationship between protein abundance and sampling statistics, such as sequence coverage, peptide count, and spectral count, in label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) shotgun proteomics. The use of sampling statistics offers a promising method of measuring relative protein abundance and detecting differentially expressed or coexpressed proteins. We performed a systematic analysis of various approaches to quantifying differential protein expression in eukaryotic Saccharomyces cerevisiae and prokaryotic Rhodopseudomonas palustris labelfree LC-MS/MS data. First, we showed that, among three sampling statistics, the spectral count has the highest technical reproducibility, followed by the less-reproducible peptide count and relatively nonreproducible sequence coverage. Second, we used spectral count statistics to measure differential protein expression in pairwise experiments using five statistical tests: Fisher's exact test, G-test, AC test, t-test, and LPE test. Given the S. cerevisiae data set with spiked proteins as a benchmark and the false positive rate as a metric, our evaluation suggested that the Fisher's exact test, G-test, and AC test can be used when the number of replications is limited (one or two), whereas the t-test is useful with three or more replicates available. Third, we generalized the G-test to increase the sensitivity of detecting differential protein expression under multiple experimental conditions. Out of 1622 identified R. palustris proteins in the LC-MS/MS experiment, the generalized G-test detected 1119 differentially expressed proteins under six growth conditions. Finally, we studied correlated expression of these 1119 proteins by analyzing pairwise expression correlations and by delineating protein clusters according to expression patterns. Through pairwise expression correlation analysis, we demonstrated that proteins co-located in the same operon were much more strongly coexpressed than those from different operons. Combining cluster analysis with existing protein functional annotations, we identified six protein clusters with known biological significance. In summary, the proposed generalized G-test using spectral count sampling statistics is a viable methodology for robust quantification of relative protein abundance and for sensitive detection of biologically significant differential protein expression under multiple experimental conditions in label-free shotgun proteomics.

Characterization of the low molecular weight human serum proteome

by Radhakrishna S. Tirumalai, King C. Chan, Darue A. Prieto, Haleem J. Issaq, Thomas P, Timothy D. Veenstra - Mol. Cell. Proteomics , 2003
"... chromatography; ESI, electrospray ionization; MS/MS, tandem mass spectrometry; IT- ..."
Abstract - Cited by 52 (4 self) - Add to MetaCart
chromatography; ESI, electrospray ionization; MS/MS, tandem mass spectrometry; IT-
(Show Context)