UniPep - a database for human N-linked glycosites: a resource for biomarker discovery

There has been considerable recent interest in proteomic analyses of plasma for the purpose of discovering biomarkers. Profiling N-linked glycopeptides is a particularly promising method because the population of N-linked glycosites represents the proteomes of plasma, the cell surface, and secreted proteins at very low redundancy and provides a compelling link between the tissue and plasma proteomes. Here, we describe UniPep http://www.unipep.org - a database of human N-linked glycosites - as a resource for biomarker discovery.

[1]  Jing Zhang,et al.  Quantitative proteomic analysis of age-related changes in human cerebrospinal fluid , 2005, Neurobiology of Aging.

[2]  R. Aebersold,et al.  The Application of New Software Tools to Quantitative Protein Profiling Via Isotope-coded Affinity Tag (ICAT) and Tandem Mass Spectrometry , 2003, Molecular & Cellular Proteomics.

[3]  Eugene A. Kapp,et al.  Overview of the HUPO Plasma Proteome Project: Results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly‐available database , 2005, Proteomics.

[4]  Ronald J. Moore,et al.  Toward a Human Blood Serum Proteome , 2002, Molecular & Cellular Proteomics.

[5]  Tatiana A. Tatusova,et al.  Entrez Gene: gene-centered information at NCBI , 2004, Nucleic Acids Res..

[6]  Chris F. Taylor,et al.  A common open representation of mass spectrometry data and its application to proteomics research , 2004, Nature Biotechnology.

[7]  J. Roth Protein N-glycosylation along the secretory pathway: relationship to organelle topography and function, protein quality control, and cell interactions. , 2002, Chemical reviews.

[8]  Ruedi Aebersold,et al.  The Application of New Software Tools to Quantitative Protein Profiling Via Isotope-coded Affinity Tag (ICAT) and Tandem Mass Spectrometry , 2003, Molecular & Cellular Proteomics.

[9]  Alexey I Nesvizhskii,et al.  Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. , 2002, Analytical chemistry.

[10]  T. Veenstra,et al.  Characterization of the Low Molecular Weight Human Serum Proteome*S , 2003, Molecular & Cellular Proteomics.

[11]  Ronald J Moore,et al.  Quantitative Proteome Analysis of Human Plasma following in Vivo Lipopolysaccharide Administration Using 16O/18O Labeling and the Accurate Mass and Time Tag Approach*S , 2005, Molecular & Cellular Proteomics.

[12]  Richard D. Smith,et al.  Proteome analyses using accurate mass and elution time peptide tags with capillary LC time-of-flight mass spectrometry , 2003, Journal of the American Society for Mass Spectrometry.

[13]  S. Bryant,et al.  Open mass spectrometry search algorithm. , 2004, Journal of proteome research.

[14]  J. Yates,et al.  A model for random sampling and estimation of relative protein abundance in shotgun proteomics. , 2004, Analytical chemistry.

[15]  N. Anderson,et al.  The Human Plasma Proteome , 2002, Molecular & Cellular Proteomics.

[16]  E Bause,et al.  Structural requirements of N-glycosylation of proteins. Studies with proline peptides as conformational probes. , 1983, The Biochemical journal.

[17]  R. Aebersold,et al.  ProbID: A probabilistic algorithm to identify peptides through sequence database searching using tandem mass spectral data , 2002, Proteomics.

[18]  R. Aebersold,et al.  Quantitative profiling of differentiation-induced microsomal proteins using isotope-coded affinity tags and mass spectrometry , 2001, Nature Biotechnology.

[19]  Ruedi Aebersold,et al.  Identification and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry , 2003, Nature Biotechnology.

[20]  Ruedi Aebersold,et al.  High Throughput Quantitative Analysis of Serum Proteins Using Glycopeptide Capture and Liquid Chromatography Mass Spectrometry *S , 2005, Molecular & Cellular Proteomics.

[21]  Raymond A Dwek,et al.  Statistical analysis of the protein environment of N-glycosylation sites: implications for occupancy, structure, and folding. , 2003, Glycobiology.

[22]  R. Nelson,et al.  Investigating diversity in human plasma proteins. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Ronald J Moore,et al.  Human plasma N-glycoproteome analysis by immunoaffinity subtraction, hydrazide chemistry, and mass spectrometry. , 2005, Journal of proteome research.

[24]  Søren Brunak,et al.  A Neural Network Method for Identification of Prokaryotic and Eukaryotic Signal Peptides and Prediction of their Cleavage Sites , 1997, Int. J. Neural Syst..

[25]  Nichole L. King,et al.  Human Plasma PeptideAtlas , 2005, Proteomics.

[26]  D. N. Perkins,et al.  Probability‐based protein identification by searching sequence databases using mass spectrometry data , 1999, Electrophoresis.

[27]  D. L. Diamond,et al.  Analysis of prostate cancer by proteomics using tissue specimens. , 2005, The Journal of urology.

[28]  A. Krogh,et al.  Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. , 2001, Journal of molecular biology.

[29]  J. Yates,et al.  An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database , 1994, Journal of the American Society for Mass Spectrometry.

[30]  Eric W. Deutsch,et al.  The PeptideAtlas project , 2005, Nucleic Acids Res..

[31]  Nichole L. King,et al.  Integration with the human genome of peptide sequences obtained by high-throughput mass spectrometry , 2004, Genome Biology.

[32]  R. Aebersold,et al.  Scoring proteomes with proteotypic peptide probes , 2005, Nature Reviews Molecular Cell Biology.

[33]  Jing Zhang,et al.  Quantitative proteomics of cerebrospinal fluid from patients with Alzheimer disease. , 2005, Journal of Alzheimer's disease : JAD.

[34]  Ronald J Moore,et al.  Ultra-high-efficiency strong cation exchange LC/RPLC/MS/MS for high dynamic range characterization of the human plasma proteome. , 2004, Analytical chemistry.

[35]  E. Petricoin,et al.  Use of proteomic patterns in serum to identify ovarian cancer , 2002, The Lancet.

[36]  J. Lorente,et al.  Effect of antibiotic treatment on serum PSA and percent free PSA levels in patients with biochemical criteria for prostate biopsy and previous lower urinary tract infections. , 2002, The International journal of biological markers.

[37]  Ruedi Aebersold,et al.  Chemical probes and tandem mass spectrometry: a strategy for the quantitative analysis of proteomes and subproteomes. , 2004, Current opinion in chemical biology.

[38]  J. Weinstein,et al.  Biomarkers in Cancer Staging, Prognosis and Treatment Selection , 2005, Nature Reviews Cancer.

[39]  R. Aebersold,et al.  A uniform proteomics MS/MS analysis platform utilizing open XML file formats , 2005, Molecular systems biology.

[40]  E. Diamandis Mass Spectrometry as a Diagnostic and a Cancer Biomarker Discovery Tool , 2004, Molecular & Cellular Proteomics.

[41]  N. Anderson,et al.  Multi‐component immunoaffinity subtraction chromatography: An innovative step towards a comprehensive survey of the human plasma proteome , 2003, Proteomics.

[42]  J. Ku,et al.  Influence of age, anthropometry, and hepatic and renal function on serum prostate-specific antigen levels in healthy middle-age men. , 2003, Urology.

[43]  M. Boguski,et al.  dbEST — database for “expressed sequence tags” , 1993, Nature Genetics.

[44]  R. Beavis,et al.  A method for assessing the statistical significance of mass spectrometry-based protein identifications using general scoring schemes. , 2003, Analytical chemistry.

[45]  Ning Zhang,et al.  High Throughput Proteome Screening for Biomarker Detection* , 2005, Molecular & Cellular Proteomics.

[46]  Fang Wang,et al.  The human serum proteome: Display of nearly 3700 chromatographically separated protein spots on two‐dimensional electrophoresis gels and identification of 325 distinct proteins , 2003, Proteomics.