Scientific workflow optimization for improved peptide and protein identification

Holl, Sonja; Mohammed, Yassene; Zimmermann, Olav; Palmblad, Magnus

doi:10.1186/s12859-015-0714-x

Cited by 12 publications

(9 citation statements)

References 36 publications

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“…Thanks to the combination of advances in instrumentation, fragmentation methods, and analysis strategies, MS has become an indispensable tool for the study of protein expression, protein interactions, and modifications. [1][2][3][4][5][6][7][8][9][10] A typical strategy for obtaining proteomics information from a biological sample consists of the combination of enzymatic digestion (usually by trypsin) followed by (nano)-liquid chromatography electrospray ionization tandem MS of the resulting peptides. One of the fragmentation approaches is the data-dependent MS/MS analysis (DDA),…”

Section: Introductionmentioning

confidence: 99%

Selection of Collision Energies in Proteomics Mass Spectrometry Experiments for Best Peptide Identification: Study of Mascot Score Energy Dependence Reveals Double Optimum

et al. 2018

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Collision energy is a key parameter determining the information content of beam-type collision induced dissociation tandem mass spectrometry (MS/MS) spectra, and its optimal choice largely affects successful peptide and protein identification in MS-based proteomics. For an MS/MS spectrum, quality of peptide match based on sequence database search, often characterized in terms of a single score, is a complex function of spectrum characteristics, and its collision energy dependence has remained largely unexplored. We carried out electrospray ionization-quadrupole-time of flight (ESI-Q-TOF)-MS/MS measurements on 2807 peptides from tryptic digests of HeLa and E. coli at 21 different collision energies. Agglomerative clustering of the resulting Mascot score versus energy curves revealed that only few of them display a single, well-defined maximum; rather, they feature either a broad plateau or two clear peaks. Nonlinear least-squares fitting of one or two Gaussian functions allowed the characteristic energies to be determined. We found that the double peaks and the plateaus in Mascot score can be associated with the different energy dependence of b- and y-type fragment ion intensities. We determined that the energies for optimum Mascot scores follow separate linear trends for the unimodal and bimodal cases with rather large residual variance even after differences in proton mobility are taken into account. This leaves room for experiment optimization and points to the possible influence of further factors beyond m/ z.

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Section: Introductionmentioning

confidence: 99%

Selection of Collision Energies in Proteomics Mass Spectrometry Experiments for Best Peptide Identification: Study of Mascot Score Energy Dependence Reveals Double Optimum

et al. 2018

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“…SSRCalc was implemented for online access at http://hs2.proteome.ca/SSR-Calc/SSRCalcX.html with several optional versions optimized for a variety of most popular RP-HPLC separation conditions. Recently, the SSRCalc model has been upgraded by taking The SSRCalc algorithm was also integrated into a number of proteomic pipelines used for peptide identification filtering 29,45 and scheduling of the parent-fragment transitions in MRM/SRM-based quantitative analysis (Skyline, 46 ATAQS, 47 SRMCollider, 48 and MaRiMba 49 ).…”

Section: Retention Time Prediction Based On the Amino Acid Compositionmentioning

confidence: 99%

Predictive chromatography of peptides and proteins as a complementary tool for proteomics

Tarasova

Masselon

Gorshkov

et al. 2016

Analyst

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In the last couple of decades, considerable effort has been focused on developing methods for quantitative and qualitative proteome characterization. The method of choice in this characterization is mass spectrometry used in combination with sample separation. One of the most widely used separation techniques at the front end of a mass spectrometer is high performance liquid chromatography (HPLC). A unique feature of HPLC is its specificity to the amino acid sequence of separated peptides and proteins. This specificity may provide additional information about the peptides or proteins under study which is complementary to the mass spectrometry data. The value of this information for proteomics has been recognized in the past few decades, which has stimulated significant effort in the development and implementation of computational and theoretical models for the prediction of peptide retention time for a given sequence. Here we review the advances in this area and the utility of predicted retention times for proteomic applications.

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“…6). This is likely due to another peptide within the isolation window that accounts for (most of) the MS2 singals 18 .…”

Section: Resultsmentioning

confidence: 99%

ionbot: a novel, innovative and sensitive machine learning approach to LC-MS/MS peptide identification

Degroeve

Gabriels

Velghe

et al. 2021

Preprint

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Mass spectrometry-based proteomics generates vast amounts of signal data that require computational interpretation to obtain peptide identifications. Dozens of algorithms for this task exist, but all exploit only part of the acquired data to judge a peptide-to-spectrum match (PSM), ignoring important information such as the observed retention time and fragment ion peak intensity pattern. Moreover, only few identification algorithms allow open modification searches that can substantially increase peptide identifications. We here therefore introduce ionbot, a novel open modification search engine that is the first to fully merge machine learning with peptide identification. This core innovation brings the ability to include a much larger range of experimental data into PSM scoring, and even to adapt this scoring to the specifics of the data itself. As a result, ionbot substantially increases PSM confidence for open searches, and even enables a further increase in peptide identification rate of up to 30% by also considering highly plausible, lower-ranked, co-eluting matches for a fragmentation spectrum. Moreover, the exclusive use of machine learning for scoring also means that any future improvements to predictive models for peptide behavior will also result in more sensitive and accurate peptide identification.

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Scientific workflow optimization for improved peptide and protein identification

Cited by 12 publications

References 36 publications

Selection of Collision Energies in Proteomics Mass Spectrometry Experiments for Best Peptide Identification: Study of Mascot Score Energy Dependence Reveals Double Optimum

Selection of Collision Energies in Proteomics Mass Spectrometry Experiments for Best Peptide Identification: Study of Mascot Score Energy Dependence Reveals Double Optimum

Predictive chromatography of peptides and proteins as a complementary tool for proteomics

ionbot: a novel, innovative and sensitive machine learning approach to LC-MS/MS peptide identification

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