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JournalISSN: 1535-3893

Journal of Proteome Research 

American Chemical Society
About: Journal of Proteome Research is an academic journal published by American Chemical Society. The journal publishes majorly in the area(s): Proteome & Proteomics. It has an ISSN identifier of 1535-3893. Over the lifetime, 8781 publications have been published receiving 397626 citations. The journal is also known as: J. Proteome Res. & J Proteome Res.


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Journal ArticleDOI
TL;DR: A novel peptide search engine using a probabilistic scoring model that can handle data with arbitrarily high fragment mass accuracy, is able to assign and score complex patterns of post-translational modifications, and accommodates extremely large databases.
Abstract: A key step in mass spectrometry (MS)-based proteomics is the identification of peptides in sequence databases by their fragmentation spectra. Here we describe Andromeda, a novel peptide search engine using a probabilistic scoring model. On proteome data, Andromeda performs as well as Mascot, a widely used commercial search engine, as judged by sensitivity and specificity analysis based on target decoy searches. Furthermore, it can handle data with arbitrarily high fragment mass accuracy, is able to assign and score complex patterns of post-translational modifications, such as highly phosphorylated peptides, and accommodates extremely large databases. The algorithms of Andromeda are provided. Andromeda can function independently or as an integrated search engine of the widely used MaxQuant computational proteomics platform and both are freely available at www.maxquant.org. The combination enables analysis of large data sets in a simple analysis workflow on a desktop computer. For searching individual spect...

4,689 citations

Journal ArticleDOI
TL;DR: The combination of strong cation exchange (SCX) and reversed-phase (RP) chromatography to achieve two-dimensional separation prior to MS/MS and 1,504 yeast proteins were unambiguously identified in this single analysis.
Abstract: Highly complex protein mixtures can be directly analyzed after proteolysis by liquid chromatography coupled with tandem mass spectrometry (LC−MS/MS). In this paper, we have utilized the combination of strong cation exchange (SCX) and reversed-phase (RP) chromatography to achieve two-dimensional separation prior to MS/MS. One milligram of whole yeast protein was proteolyzed and separated by SCX chromatography (2.1 mm i.d.) with fraction collection every minute during an 80-min elution. Eighty fractions were reduced in volume and then re-injected via an autosampler in an automated fashion using a vented-column (100 μm i.d.) approach for RP-LC−MS/MS analysis. More than 162 000 MS/MS spectra were collected with 26 815 matched to yeast peptides (7537 unique peptides). A total of 1504 yeast proteins were unambiguously identified in this single analysis. We present a comparison of this experiment with a previously published yeast proteome analysis by Yates and colleagues (Washburn, M. P.; Wolters, D.; Yates, J. ...

1,654 citations

Journal ArticleDOI
TL;DR: Silver nanoparticles (nano-Ag) are potent and broad-spectrum antimicrobial agents and appear to be an efficient physicochemical system conferring antimicrobial silver activities.
Abstract: Silver nanoparticles (nano-Ag) are potent and broad-spectrum antimicrobial agents. In this study, spherical nano-Ag (average diameter = 9.3 nm) particles were synthesized using a borohydride reduction method and the mode of their antibacterial action against E. coli was investigated by proteomic approaches (2-DE and MS identification), conducted in parallel to analyses involving solutions of Ag+ ions. The proteomic data revealed that a short exposure of E. coli cells to antibacterial concentrations of nano-Ag resulted in an accumulation of envelope protein precursors, indicative of the dissipation of proton motive force. Consistent with these proteomic findings, nano-Ag were shown to destabilize the outer membrane, collapse the plasma membrane potential and deplete the levels of intracellular ATP. The mode of action of nano-Ag was also found to be similar to that of Ag+ ions (e.g., Dibrov, P. et al, Antimicrob. Agents Chemother. 2002, 46, 2668−2670); however, the effective concentrations of nano-Ag and Ag...

1,418 citations

Journal ArticleDOI
TL;DR: DTASelect, a new software package, assembles SEQUEST identifications and highlights the most significant matches, and the accompanying Contrast tool compares DTASelect results from multiple experiments to improve the speed and precision of proteomic data analysis.
Abstract: The components of complex peptide mixtures can be separated by liquid chromatography, fragmented by tandem mass spectrometry, and identified by the SEQUEST algorithm. Inferring a mixture's source proteins requires that the identified peptides be reassociated. This process becomes more challenging as the number of peptides increases. DTASelect, a new software package, assembles SEQUEST identifications and highlights the most significant matches. The accompanying Contrast tool compares DTASelect results from multiple experiments. The two programs improve the speed and precision of proteomic data analysis.

1,383 citations

Journal ArticleDOI
TL;DR: The Open Mass Spectrometry Search Algorithm (OMSSA), designed to be faster than published algorithms in searching large MS/MS datasets, matches more spectra from a standard protein cocktail than a comparable algorithm.
Abstract: Large numbers of MS/MS peptide spectra generated in proteomics experiments require efficient, sensitive and specific algorithms for peptide identification. In the Open Mass Spectrometry Search Algorithm (OMSSA), specificity is calculated by a classic probability score using an explicit model for matching experimental spectra to sequences. At default thresholds, OMSSA matches more spectra from a standard protein cocktail than a comparable algorithm. OMSSA is designed to be faster than published algorithms in searching large MS/MS datasets. Keywords: protein identification • algorithm • bioinformatics • mass spectrometry • proteomics • significance testing

1,304 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
2023203
2022297
2021506
2020432
2019350
2018452