Dataset Information

Improved Monoisotopic Mass Estimation for Deeper Proteome Coverage

ABSTRACT: Accurate assignment of monoisotopic peaks is essential for the identification of peptides in bottom-up proteomics. Misassignment or inaccurate attribution of peptidic ions leads to lower sensitivity and fewer total peptide identifications. In the present work we present a performant, open-source, cross-platform algorithm, Monocle, for the rapid reassignment of instrument assigned precursor peaks to monoisotopic peptide assignments. We demonstrate that the present algorithm can be integrated into many common proteomics pipelines and provides rapid conversion from multiple data source types. Finally, we show that our monoisotopic peak assignment results in up to a two-fold increase in total peptide identifications compared to analyses lacking monoisotopic correction and a 44% improvement over previous monoisotopic peak correction algorithms.

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (human) Saccharomyces Cerevisiae (baker's Yeast)

SUBMITTER: Jiaming Li

LAB HEAD: Steven Gygi

PROVIDER: PXD019311 | Pride | 2020-11-17

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	g06592_hela_TMT10_FAIMS.raw	Raw
	g06592_hela_TMT10_FAIMS_MonocleCLI.mzIdentML	Mzid
	g06592_hela_TMT10_FAIMS_MonocleCLI.mzXML	Mzxml
	g06592_hela_TMT10_FAIMS_RawConverter.mzIdentML	Mzid
	g06592_hela_TMT10_FAIMS_RawConverter.mzXML	Mzxml

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Publications

Improved Monoisotopic Mass Estimation for Deeper Proteome Coverage.

Rad Ramin R Li Jiaming J Mintseris Julian J O'Connell Jeremy J Gygi Steven P SP Schweppe Devin K DK

Journal of proteome research 20201116 1

Accurate assignment of monoisotopic peaks is essential for the identification of peptides in bottom-up proteomics. Misassignment or inaccurate attribution of peptidic ions leads to lower sensitivity and fewer total peptide identifications. In the present work, we present a performant, open-source, cross-platform algorithm, Monocle, for the rapid reassignment of instrument-assigned precursor peaks to monoisotopic peptide assignments. We demonstrate that the present algorithm can be integrated int ...[more]

PMID: 33190505

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Project description:Datasets contain protein identification and raw data from MALDI protein identifications. Identified proteins resulted from a comparison of GLC1 and GLC1 SCLC cell lines using 2D DIGE. MALDI-TOF-MS analyses were performed on an UltraFlexTM II (Bruker Daltonics) instrument according to the instructions of the manufacturer. The instrument was equipped with a scoutTM MTP MALDI target. The spectra were acquired in the positive ion mode according to the settings given by the manufacturer. For external calibration, a peptide standard (m/z 757.399, 1296.684, 1619.822, 2093.086 and 3147.471) was used. The MALDI-PMF spectra were processed using the FlexAnalysis™ 2.4 software (Bruker Daltonics) and converted in the .xml format. For peak detection, the spectra were subjected to an internal recalibration using 13 different monoisotopic masses from autolysis products of trypsin and fragments of keratins ranging from m/z 842.509 – 2825.406. Following parameters were applied: snap peak detection algorithm, signal to noise threshold of 6, maximal number of peaks 100, quality factor threshold 50 and baseline subtraction TopHat. The generated mass lists were subsequently sent to ProteinScapeTM 1.3 (Bruker Daltonics, Bremen, Germany), triggering database searches using ProFound (Version 2002.03.01, Proteometrics LLC) and MASCOT (Version 2.3.02, Matrix Science, London, UK). The following search parameters were selected: fixed cysteine modification with propionamide, variable modification due to methionine oxidation, one maximal missed cleavage sites in case of incomplete trypsin hydrolysis and no details about 2-DE derived protein mass and pI. Using the Score booster function of ProteinScapeTM the mass lists were recalibrated and background masses removed using a list containing 44 masses occurring in a minimum of 10% of generated peak lists. The database searches were run with a mass tolerance of 40 ppm using UniProt’s human complete proteome set (downloaded on 26.10.2012) containing 68.109 protein entries. The used database is a composite database consisting of the UniProtKB entries and a duplicate of the same database, in which the amino acid sequence of each protein entry was randomly shuffled. Proteins reaching Profound score > 1.5 or Mascot score > 64 were considered as identified. Using these criteria one decoy database entry was found by the search engines indicating high confidence of protein identifications. If several database entries of homologues proteins matched these criteria only the entry with the highest score was reported.