The ease of peptide detection by matrix-assisted laser desorption/ionization mass spectrometry: the effect of secondary structure on signal intensity

Wenschuh, Holger; Halada, Petr; Lamer, Stephanie; Jungblut, Peter R.; Krause, Eberhard

doi:10.1002/(sici)1097-0231(19980214)12:3<115::aid-rcm124>3.0.co;2-5

Cited by 35 publications

(40 citation statements)

References 18 publications

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“…The hypothesis is supported by the fact that the unmatched peptides have a higher ␣-helix structures index whereas the matched peptides tend to have amino acid compositions more consistent with ␤-sheet conformations. There are some observations in literature consistent with this point of view [9]. While secondary structure assignment is rather inaccurate, our results still could reflect differential solvation and/or secondary structure formation.…”

Section: Discussionsupporting

confidence: 69%

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Algorithm for accurate similarity measurements of peptide mass fingerprints and its application

Monigatti

Berndt

2005

J. Am. Soc. Mass Spectrom.

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We present a simple algorithm which allows accurate estimates of the similarity between peptide fingerprint mass spectra from matrix assisted laser desorption/ionization (MALDI) spectrometers. The algorithm, which is a combination of mass correlation and intensity rank correlation, was used to cluster similar spectra and to generate consensus spectra from a data store of more than 100,000 spectra. The resulting first spectra library of 1248 unambiguously identified different protein digests was used to search for missed cleavage patterns that have not been reported so far and to shed light on some peptide ionization characteristics. The findings of this study could be directly implemented in peptide mass fingerprint search algorithms to decrease the false positive error rate to Ͻ0.25%. Furthermore, the results contribute to the understanding of the peptide ionization process in MALDI experiments. (J Am Soc Mass Spectrom 2005, 16, 13-21)

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Section: Discussionsupporting

confidence: 69%

“…Krause et al [8] presented the dominance of arginine-containing peptides in MALDI-MS peptide fingerprints. The effects of secondary structures on signal intensities have been investigated by Wenschuh et al [9]. The results of these studies are all derived from a few experimentally validated spectra.…”

mentioning

confidence: 99%

Algorithm for accurate similarity measurements of peptide mass fingerprints and its application

Monigatti

Berndt

2005

J. Am. Soc. Mass Spectrom.

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“…Conversely, parameters for coil exert a positive influence, suggesting that less regulated peptide structure may be optimal for LC-ESI. It has been demonstrated previously that peptides with ␣-helical structure have reduced ionization efficiency in MALDI (40), however to our knowledge this feature has not previously been demonstrated to influence efficiency of ionization by electrospray. Interestingly, Russell and colleagues (41) have evidence for a singly protonated model peptide suggesting that whereas computer simulations predict helical conformation to be the lowest energy structure, random coil and turn conformers are more prevalent.…”

Section: Discussionmentioning

confidence: 67%

CONSeQuence: Prediction of Reference Peptides for Absolute Quantitative Proteomics Using Consensus Machine Learning Approaches

Eyers

Lawless

Wedge

et al. 2011

Molecular & Cellular Proteomics

123

169

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Mass spectrometric based methods for absolute quantification of proteins, such as QconCAT, rely on internal standards of stable-isotope labeled reference peptides, or "Q-peptides," to act as surrogates. Key to the success of this and related methods for absolute protein quantification (such as AQUA) is selection of the Q-peptide. Here we describe a novel method, CONSeQuence (consensus predictor for Q-peptide sequence), based on four different machine learning approaches for Q-peptide selection. CONSeQuence demonstrates improved performance over existing methods for optimal Q-peptide selection in the absence of prior experimental information, as validated using two independent test sets derived from yeast. Furthermore, we examine the physicochemical parameters associated with good peptide surrogates, and demonstrate that in addition to charge and hydrophobicity, peptide secondary structure plays a significant role in determining peptide "detectability" in liquid chromatography-electrospray ionization experiments. We relate peptide properties to protein tertiary structure, demonstrating a counterintuitive preference for buried status for frequently detected peptides. Finally, we demonstrate the improved efficacy of the general approach by applying a predictor trained on yeast data to sets of proteotypic peptides from two additional species taken from an existing peptide identification repository.Molecular & Cellular Proteomics 10: 10.1074/mcp.M110.003384, 1-12, 2011.The study of cellular systems via identification of their protein components is becoming almost commonplace with the continuing advances in mass spectrometric hardware and associated analytical software. The current drive is now to assign accurate quantitative information to these protein components, such that the data can be used in systems modeling studies. For these models to be effective and the dynamics of these biochemical systems simulated during activation or perturbation, absolute rather than relative quantitative information must be provided (1). Even in the absence of sophisticated modeling studies, evaluating both the qualitative and quantitative aspects of biological networks can permit understanding of the complex interplay of system components, as well as the identification of disease biomarkers (2-4).Given the dependence of mass spectrometric signal intensity on the nature of the analyte, methods for absolute protein quantification primarily rely on standardization of signal intensity with known quantities of isotope-labeled references that are identical in primary structure to the analyte (5-7). In a typical such proteomics experiment, quantification is performed at the peptide level, often by virtue of selected reaction monitoring experiments, using defined amounts of pure isotope-labeled tryptic peptide as reference for unknown quantities of the tryptic hydrolysate of the protein of interest. Protein amount is subsequently inferred. This can also be combined with label-free approaches to infer absolute peptide quantifications using a...

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“…It has been reported that several factors, such as suppression effects in peptide mixtures, 10 size, 11 hydrophobicity, 11 the presence of a charged side chain 12 and aromatic amino acids, 12 as well as secondary structure, 3 affect the desorption/ionization . These data suggest that the pI of these peptides might not be related to the desorption/ionization efficiency of peptides in MALDI-MS.…”

Section: Maldi-ms Measurement Of Peptide Sets With or Without Disulfimentioning

confidence: 99%

“…2 On the other hand, in 1998, Wenschuh et al had reported that structurally well-characterized model peptides, displaying stable α-helical or β-sheet structures, show lower signal intensities in MALDI-MS than peptides with disturbed secondary structures. 3 The relationship between desorption/ ionization efficiency of peptides and peptide conformation still remains an enigma.…”

Section: Introductionmentioning

confidence: 99%

Desorption/Ionization Efficiency of Peptides Containing Disulfide Bonds in Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry

et al. 2012

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Experimental Peptides synthesis and determination of their concentrationPeptides were synthesized by Fmoc peptide chemistry using a Shimadzu PSSM-8 automated peptide synthesizer (Shimadzu, Kyoto, Japan), and purified by reverse-phase HPLC on a C18 column. The identity and purity of the peptides were confirmed by matrix-assisted laser desorption/ionization (MALDI)-TOF MS using a MicroflexAI mass spectrometer (Bruker Daltonics, Yokohama, Japan) and α-cyano-4-hydroxycinnamic acid as a matrix. Peptides with one disulfide bond were prepared by air oxidation of reduced peptides without disulfide bonds (SH type). For air oxidation, 10 -7 M peptides were suspended in 50 mM ammonium acetate buffer (pH 8.2), stirred and left at room temperature for one or two days. After oxidation, 0.4% 2012 © The Japan Society for Analytical Chemistry † To whom correspondence should be addressed. In order to elucidate the role of desorption/ionization efficiency of peptides in MALDI-MS, we focused on peptides with disulfide bonds, which form a rigid tertiary structure. We synthesized seven sets of peptides with one disulfide bond (oxytocin, somatostatin, [Arg 8 ]-vasopressin, [Arg 8 ]-vasotocin, cortistatin, melanin-concentrating hormone, urotensin II-related peptide) and five sets of peptides with two disulfide bonds (tertiapin, α-conotoxin GI, α-conotoxin ImI, α-conotoxin MI and α-conotoxin SI). Each peptide set consisted of three peptides: the oxidized form (S-S type), the reduced form (SH type), and an internal standard peptide in which all cysteine residues were substituted with alanine residues. In the case of urotensin II-related peptide, tertiapin, α-conotoxin ImI and α-conotoxin MI, the reduced form showed higher desorption/ionization efficiency than the oxidized form. In contrast, the other peptides revealed higher desorption/ionization efficiency in the oxidized form relative to the reduced form. These results imply that a rigid structure of peptides formed by disulfide bonds does not correlate with desorption/ionization efficiency in MALDI-MS.

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The ease of peptide detection by matrix-assisted laser desorption/ionization mass spectrometry: the effect of secondary structure on signal intensity

Cited by 35 publications

References 18 publications

Algorithm for accurate similarity measurements of peptide mass fingerprints and its application

Algorithm for accurate similarity measurements of peptide mass fingerprints and its application

CONSeQuence: Prediction of Reference Peptides for Absolute Quantitative Proteomics Using Consensus Machine Learning Approaches

Desorption/Ionization Efficiency of Peptides Containing Disulfide Bonds in Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry

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