PEAKS DB: De Novo Sequencing Assisted Database Search for Sensitive and Accurate Peptide Identification

Zhang, Jing; Lei, Xin; Shan, Baozhen; Chen, Weiwu; Xie, Mingjie; Yuen, Denis; Zhang, Weimin; Zhang, Zefeng; Lajoie, Gilles; Ma, Bin

doi:10.1074/mcp.m111.010587

Cited by 948 publications

(853 citation statements)

References 24 publications

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“…Spectra were first selected by quality, and fragmentation spectra with the same mass (less than 5 ppm difference) and retention time were merged. The PEAKS DB, PTM, and Spider function [20] were used to assign the MS/MS spectra to peptide sequences by matching the experimental spectra to theoretical spectra generated from the automatically annotated G. m. morsitans protein database available on VectorBase [21], supplemented with self-predicted annotations. The following search parameters were used: a precursor mass tolerance of 8 ppm using monoisotopic mass and a fragment mass tolerance of 15 mmu.…”

Section: Resultsmentioning

confidence: 99%

Peptidomics of Neuropeptidergic Tissues of the Tsetse FlyGlossina morsitans morsitans

Caers

Boonen

Abbeele³

et al. 2015

J. Am. Soc. Mass Spectrom.

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Abstract. Neuropeptides and peptide hormones are essential signaling molecules that regulate nearly all physiological processes. The recent release of the tsetse fly genome allowed the construction of a detailed in silico neuropeptide database (International Glossina Genome Consortium, Science 344, 380-386 (2014)), as well as an in-depth mass spectrometric analysis of the most important neuropeptidergic tissues of this medically and economically important insect species. Mass spectrometric confirmation of predicted peptides is a vital step in the functional characterization of neuropeptides, as in vivo peptides can be modified, cleaved, or even mispredicted. Using a nanoscale reversed phase liquid chromatography coupled to a Q Exactive Orbitrap mass spectrometer, we detected 51 putative bioactive neuropeptides encoded by 19 precursors: adipokinetic hormone (AKH) I and II, allatostatin A and B, capability/ pyrokinin (capa/PK), corazonin, calcitonin-like diuretic hormone (CT/DH), FMRFamide, hugin, leucokinin, myosuppressin, natalisin, neuropeptide-like precursor (NPLP) 1, orcokinin, pigment dispersing factor (PDF), RYamide, SIFamide, short neuropeptide F (sNPF) and tachykinin. In addition, propeptides, truncated and spacer peptides derived from seven additional precursors were found, and include the precursors of allatostatin C, crustacean cardioactive peptide, corticotropin releasing factor-like diuretic hormone (CRF/DH), ecdysis triggering hormone (ETH), ion transport peptide (ITP), neuropeptide F, and proctolin, respectively. The majority of the identified neuropeptides are present in the central nervous system, with only a limited number of peptides in the corpora cardiaca-corpora allata and midgut. Owing to the large number of identified peptides, this study can be used as a reference for comparative studies in other insects.

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Section: Resultsmentioning

confidence: 99%

Peptidomics of Neuropeptidergic Tissues of the Tsetse FlyGlossina morsitans morsitans

Caers

Boonen

Abbeele³

et al. 2015

J. Am. Soc. Mass Spectrom.

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“…The desalted peptide mixture of each protein spot was analyzed using Agilent 1260 Infinity Nanoflow pump LC system coupled to Agilent 6550 iFunnel QTOF mass spectrometer (Agilent technologies) as described previously [3]. Mass spectrometry data obtained was exported as .mgf format and analyzed using MASCOT search engine and PEAKS denovo sequencing software [11]. Parameter adopted by these tools for protein identification from Swiss-Prot database using trypsin as enzyme specificity; number of missed cleavages as one; mass accuracy of 10 ppm for peptide tolerance and 0.05 Da for fragment tolerance; fixed modification as carbamidomethylation of cysteine residues and oxidation of methionine as variable modification.…”

Section: Mass Spectrometric Analysis Of Proteins From 2d Gelsmentioning

confidence: 99%

Analysis of Dynamics of Proteome in Resistant Cultivar of Pearl Millet Seedlings during Sclerospora graminicola Infection

Anup¹,

Kini

2016

J App Biol Biotech

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The current study was performed to analyze the dynamics of proteome in resistant cultivar of pearl millet seedlings during early hours of downy mildew infection. This was achieved using two-dimensional gel electrophoresis (2DE)-nano LCMS/MS based approach. Total protein extract from two-day-old pearl millet seedlings harvested 6 h post inoculation with pathogen, was fractionated by 2DE on 5-8 pI range IPG strips. Among the 210 protein spots detected by the PD-Quest software, 21 proteins were found to be up-regulated and 14 proteins down-regulated. Differential regulation of these proteins during downy mildew infection was found to be significant at p ≤ 0.05. Majority of these proteins belonged to the following functional categories: energy and metabolism (32%) category as well as stress and defense (32%). Unlike the previous proteomic study in pearl millet, the present study showed differential accumulation of early expressing defense-related genes such as those involved in reactive oxygen species mediated pathways, pathogenesis-related proteins and other interconnecting pathways responsible for coordinating the resistance mechanism of the plant. The current findings shed light on the vital differentially regulated proteins of pearl millet during early phase of pathogen infection that can be targeted as future disease management strategy.

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“…For each PSM identified, SEQUEST provides two scores X corr and ΔCn. According to the PEAKS DB paper, 10 the linear combination of the two scores X corr + 5·ΔCn provides the optimal identification performance for SEQUEST. We further supplemented this score by adding 5 times the Pearson CC between the spectrum and the predicted spectrum from the peptide sequence.…”

Section: Improving Sequest Database Search With Predicted Spectrummentioning

confidence: 99%

“…De novo sequencing can be used to both confirm the database search results and to derive sequences that are not in a database. 10,11 Among the de novo sequencing tools developed, the more popular ones are PEAKS 12,13 and PepNovo. 14−16 In theory, de novo sequencing can be regarded as a special database search that searches in all the amino acid combinations (instead of any given protein database).…”

Section: ■ Introductionmentioning

confidence: 99%

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MS-Simulator: Predicting Y-Ion Intensities for Peptides with Two Charges Based on the Intensity Ratio of Neighboring Ions

Sun

Yang

et al. 2012

J. Proteome Res.

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For the identification of peptides with tandem mass spectrometry (MS/MS), many software tools rely on the comparison between an experimental spectrum and a theoretically predicted spectrum. Consequently, the accurate prediction of the theoretical spectrum from a peptide sequence can potentially improve the peptide identification performance and is an important problem for mass spectrometry based proteomics. In this study a new approach, called MS-Simulator, is presented for predicting the y-ion intensities in the spectrum of a given peptide. The new approach focuses on the accurate prediction of the relative intensity ratio between every two adjacent y-ions. The theoretical spectrum can then be derived from these ratios. The prediction of a ratio is a closed-form equation that involves up to five consecutive amino acids nearby the two y-ions and the two peptide termini. Compared with another existing spectrum prediction tool MassAnalyzer, the new approach not only simplifies the computation, but also improves the prediction accuracy.

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PEAKS DB: De Novo Sequencing Assisted Database Search for Sensitive and Accurate Peptide Identification

Cited by 948 publications

References 24 publications

Peptidomics of Neuropeptidergic Tissues of the Tsetse FlyGlossina morsitans morsitans

Peptidomics of Neuropeptidergic Tissues of the Tsetse FlyGlossina morsitans morsitans

Analysis of Dynamics of Proteome in Resistant Cultivar of Pearl Millet Seedlings during Sclerospora graminicola Infection

MS-Simulator: Predicting Y-Ion Intensities for Peptides with Two Charges Based on the Intensity Ratio of Neighboring Ions

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