Observation of sequence-specific peptide fragmentation using extended tandem mass spectrometry experiments

AbstractGranulocyte-macrophage colony-stimulating factor (GM-CSF) is a cytokine and a white blood cell growth factor that has found usage as a therapeutic protein. During analysis of different fermentation batches of GM-CSF recombinantly expressed in E. coli, a covalent modification was identified on the protein by intact mass spectrometry. The modification gave a mass shift of + 70 Da and peptide mapping analysis demonstrated that it located to the protein N-terminus and lysine side chains. The chemical composition of C4H6O was found to be the best candidate by peptide fragmentation using tandem mass spectrometry. The modification likely contains a carbonyl group, since the mass of the modification increased by 2 Da by reduction with borane pyridine complex and it reacted with 2,4-dinitrophenylhydrazine. On the basis of chemical and tandem mass spectrometry fragmentation behavior, the modification could be attributed to crotonaldehyde, a reactive compound formed during lipid peroxidation. A low recorded oxygen pressure in the reactor during protein expression could be linked to the formation of this compound. This study shows the importance of maintaining full control over all reaction parameters during recombinant protein production.

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Data Mining in Protein Identification by Tandem Mass Spectrometry

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch074 ◽

2011 ◽

pp. 472-478

Author(s):

Haipeng Wang

Keyword(s):

Mass Spectrometry ◽

Data Mining ◽

Tandem Mass Spectrometry ◽

Large Scale ◽

Protein Identification ◽

Peptide Fragmentation ◽

Tandem Mass ◽

Fragmentation Pattern ◽

Post Translational Modification ◽

Challenges And Opportunities

Protein identification (sequencing) by tandem mass spectrometry is a fundamental technique for proteomics which studies structures and functions of proteins in large scale and acts as a complement to genomics. Analysis and interpretation of vast amounts of spectral data generated in proteomics experiments present unprecedented challenges and opportunities for data mining in areas such as data preprocessing, peptide-spectrum matching, results validation, peptide fragmentation pattern discovery and modeling, and post-translational modification (PTM) analysis. This article introduces the basic concepts and terms of protein identification and briefly reviews the state-of-the-art relevant data mining applications. It also outlines challenges and future potential hot spots in this field.

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Mining a Tandem Mass Spectrometry Database To Determine the Trends and Global Factors Influencing Peptide Fragmentation

Analytical Chemistry ◽

10.1021/ac034616t ◽

2003 ◽

Vol 75 (22) ◽

pp. 6251-6264 ◽

Cited By ~ 178

Author(s):

Eugene A. Kapp ◽

Frédéric Schütz ◽

Gavin E. Reid ◽

James S. Eddes ◽

Robert L. Moritz ◽

...

Keyword(s):

Mass Spectrometry ◽

Tandem Mass Spectrometry ◽

Peptide Fragmentation ◽

Tandem Mass ◽

Factors Influencing

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AN ITERATIVE ALGORITHM TO QUANTIFY FACTORS INFLUENCING PEPTIDE FRAGMENTATION DURING TANDEM MASS SPECTROMETRY

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720007002643 ◽

2007 ◽

Vol 05 (02a) ◽

pp. 297-311 ◽

Cited By ~ 4

Author(s):

CHUNGONG YU ◽

YU LIN ◽

SHIWEI SUN ◽

JINJIN CAI ◽

JINGFEN ZHANG ◽

...

Keyword(s):

Mass Spectrometry ◽

Tandem Mass Spectrometry ◽

Iterative Algorithm ◽

Protein Identification ◽

Peptide Fragmentation ◽

Peptide Sequence ◽

Experimental Result ◽

Theoretical Spectrum ◽

Database Searching ◽

Tandem Mass

In protein identification by tandem mass spectrometry, it is critical to accurately predict the theoretical spectrum for a peptide sequence. To date, the widely-used database searching methods adopted simple statistical models for predicting. For some peptide, these models usually yield a theoretical spectrum with a significant deviation from the experimental one. In this paper, in order to derive an improved predicting model, we utilized a non-linear programming model to quantify the factors impacting peptide fragmentation. Then, an iterative algorithm was proposed to solve this optimization problem. Upon a training set of 1803 spectra, the experimental result showed a good agreement with some known principles about peptide fragmentation, such as the tendency to cleave at the middle of peptide, and Pro's preference of the N-terminal cleavage. Moreover, upon a testing set of 941 spectra, comparison of the predicted spectra against the experimental ones showed that this method can generate reasonable predictions. The results in this paper can offer help to both database searching and de novo methods.

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The development of organometallic OBOC peptide libraries and sequencing of N-terminal rhenium(I) tricarbonyl-containing peptides utilizing MALDI tandem mass spectrometry

Canadian Journal of Chemistry ◽

10.1139/cjc-2014-0259 ◽

2015 ◽

Vol 93 (2) ◽

pp. 234-243 ◽

Cited By ~ 5

Author(s):

Dana R. Cruickshank ◽

Leonard G. Luyt

Keyword(s):

Mass Spectrometry ◽

Tandem Mass Spectrometry ◽

Systematic Investigation ◽

Peptide Fragmentation ◽

Peptide Libraries ◽

Imaging Agents ◽

Tandem Mass ◽

Binding Properties ◽

Fragmentation Patterns ◽

Large Peptide

The development of peptide-based imaging agents through screening of large peptide libraries is hindered by the additional requirement of a radionuclide−chelator complex that can negatively affect the binding properties of the peptide. Herein, we report N-terminal rhenium(I)tricarbonyl OBOC (one-bead, one-compound) peptide libraries for use in the direct screening of potential imaging agents. The rhenium(I) tricarbonyl is incorporated directly in the library as an imaging entity surrogate to account for the presence of a technetium-99m radionuclide chelate. The identification of unknown organometallic peptides on single beads is successfully accomplished through MALDI tandem mass spectrometry, preceded by a systematic investigation of the effects of a variety of N-terminal rhenium(I) tricarbonyl chelates on peptide fragmentation patterns.

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