Literature DB >> 21780838

Examining troughs in the mass distribution of all theoretically possible tryptic peptides.

Alexey V Nefedov1, Indranil Mitra, Allan R Brasier, Rovshan G Sadygov.   

Abstract

This work describes the mass distribution of all theoretically possibly tryptic peptides made of 20 amino acids, up to the mass of 3 kDa, with resolution of 0.001 Da. We characterize regions between the peaks of the distribution, including gaps (forbidden zones) and low-populated areas (quiet zones). We show how the gaps shrink over the mass range and when they completely disappear. We demonstrate that peptide compositions in quiet zones are less diverse than those in the peaks of the distribution and that by eliminating certain types of unrealistic compositions the gaps in the distribution may be increased. The mass distribution is generated using a parallel implementation of a recursive procedure that enumerates all amino acid compositions. It allows us to enumerate all compositions of tryptic peptides below 3 kDa in 48 min using a computer cluster with 12 Intel Xeon X5650 CPUs (72 cores). The results of this work can be used to facilitate protein identification and mass defect labeling in mass spectrometry-based proteomics experiments.

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Year:  2011        PMID: 21780838      PMCID: PMC3184890          DOI: 10.1021/pr2003177

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  16 in total

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3.  Theoretical and experimental prospects for protein identification based solely on accurate mass measurement.

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4.  De novo peptide sequencing using exhaustive enumeration of peptide composition.

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Authors:  Hilda Hernandez; Sarah Niehauser; Stacey A Boltz; Vijay Gawandi; Robert S Phillips; I Jonathan Amster
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6.  Accurate mass as a bioinformatic parameter in data-to-knowledge conversion: Fourier transform ion cyclotron resonance mass spectrometry for peptide de novo sequencing.

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Journal:  Eur J Mass Spectrom (Chichester)       Date:  2007       Impact factor: 1.067

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8.  Probing combinatorial library diversity by mass spectrometry.

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Authors:  Annette Michalski; Juergen Cox; Matthias Mann
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10.  Averagine-scaling analysis and fragment ion mass defect labeling in peptide mass spectrometry.

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Journal:  Anal Chem       Date:  2008-09-09       Impact factor: 6.986

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  11 in total

1.  Improved mass defect model for theoretical tryptic peptides.

Authors:  Indranil Mitra; Alexey V Nefedov; Allan R Brasier; Rovshan G Sadygov
Journal:  Anal Chem       Date:  2012-03-07       Impact factor: 6.986

2.  Acquiring and Analyzing Data Independent Acquisition Proteomics Experiments without Spectrum Libraries.

Authors:  Lindsay K Pino; Seth C Just; Michael J MacCoss; Brian C Searle
Journal:  Mol Cell Proteomics       Date:  2020-04-20       Impact factor: 5.911

3.  Advanced Multidimensional Separations in Mass Spectrometry: Navigating the Big Data Deluge.

Authors:  Jody C May; John A McLean
Journal:  Annu Rev Anal Chem (Palo Alto Calif)       Date:  2016-03-30       Impact factor: 10.745

4.  A deeper look into Comet--implementation and features.

Authors:  Jimmy K Eng; Michael R Hoopmann; Tahmina A Jahan; Jarrett D Egertson; William S Noble; Michael J MacCoss
Journal:  J Am Soc Mass Spectrom       Date:  2015-06-27       Impact factor: 3.109

5.  Simplifying MS1 and MS2 spectra to achieve lower mass error, more dynamic range, and higher peptide identification confidence on the Bruker timsTOF Pro.

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Journal:  PLoS One       Date:  2022-07-07       Impact factor: 3.752

6.  De novo correction of mass measurement error in low resolution tandem MS spectra for shotgun proteomics.

Authors:  Jarrett D Egertson; Jimmy K Eng; Michael S Bereman; Edward J Hsieh; Gennifer E Merrihew; Michael J MacCoss
Journal:  J Am Soc Mass Spectrom       Date:  2012-09-25       Impact factor: 3.109

7.  Using SEQUEST with theoretically complete sequence databases.

Authors:  Rovshan G Sadygov
Journal:  J Am Soc Mass Spectrom       Date:  2015-08-04       Impact factor: 3.109

8.  Use of theoretical peptide distributions in phosphoproteome analysis.

Authors:  Mridul Kalita; Takhar Kasumov; Allan R Brasier; Rovshan G Sadygov
Journal:  J Proteome Res       Date:  2013-06-19       Impact factor: 4.466

9.  Use of singular value decomposition analysis to differentiate phosphorylated precursors in strong cation exchange fractions.

Authors:  Rovshan G Sadygov
Journal:  Electrophoresis       Date:  2014-07-24       Impact factor: 3.535

10.  A parallel method for enumerating amino acid compositions and masses of all theoretical peptides.

Authors:  Alexey V Nefedov; Rovshan G Sadygov
Journal:  BMC Bioinformatics       Date:  2011-11-07       Impact factor: 3.307

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