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Article: rPTMDetermine: A Fully Automated Methodology for Endogenous Tyrosine Nitration Validation, Site-Localization, and Beyond

TitlerPTMDetermine: A Fully Automated Methodology for Endogenous Tyrosine Nitration Validation, Site-Localization, and Beyond
Authors
KeywordsPeptide identification
Peptides and proteins
Organic reactions
Biological databases
Ions
Issue Date2020
PublisherAmerican Chemical Society. The Journal's web site is located at http://pubs.acs.org/ac
Citation
Analytical Chemistry, 2020, v. 92 n. 15, p. 10768–10776 How to Cite?
AbstractWe present herein rPTMDetermine, an adaptive and fully automated methodology for validation of the identification of rarely occurring post-translational modifications (PTMs), using a semisupervised approach with a linear discriminant analysis (LDA) algorithm. With this strategy, verification is enhanced through similarity scoring of tandem mass spectrometry (MS/MS) comparisons between modified peptides and their unmodified analogues. We applied rPTMDetermine to (1) perform fully automated validation steps for modified peptides identified from an in silico database and (2) retrieve potential yet-to-be-identified modified peptides from raw data (that had been missed through conventional database searches). In part (1), 99 of 125 3-nitrotyrosyl-containing (nitrated) peptides obtained from a ProteinPilot search were validated and localized. Twenty nitrated peptides were falsely assigned because of incorrect monoisotopic peak assignments, leading to erroneous identification of deamidation and nitration. Five additional nitrated peptides were, however, validated after performing nonmonoisotopic peak correction. In part (2), an additional 236 unique nitrated peptides were retrieved and localized, containing 113 previously unreported nitration sites; 25 endogenous nitrated peptides with novel sites were selected and verified by comparison with synthetic analogues. In summary, we identified and confidently validated 296 unique nitrated peptides—collectively representing the largest number of endogenously identified 3-nitrotyrosyl-containing peptides from the cerebral cortex proteome of a Macaca fascicularis model of stroke. Furthermore, we harnessed the rPTMDetermine strategy to complement conventional database searching and enhance the confidence of assigning rarely occurring PTMs, while recovering many missed peptides. In a final demonstration, we successfully extended the application of rPTMDetermine to peptides featuring tryptophan oxidation.
Persistent Identifierhttp://hdl.handle.net/10722/284019
ISSN
2019 Impact Factor: 6.785
2015 SCImago Journal Rankings: 2.369

 

DC FieldValueLanguage
dc.contributor.authorDong, N-
dc.contributor.authorSpencer, DM-
dc.contributor.authorQuan, Q-
dc.contributor.authorLe Blanc, JCY-
dc.contributor.authorFeng, J-
dc.contributor.authorLI, M-
dc.contributor.authorSiu, KWM-
dc.contributor.authorChu, IK-
dc.date.accessioned2020-07-20T05:55:22Z-
dc.date.available2020-07-20T05:55:22Z-
dc.date.issued2020-
dc.identifier.citationAnalytical Chemistry, 2020, v. 92 n. 15, p. 10768–10776-
dc.identifier.issn0003-2700-
dc.identifier.urihttp://hdl.handle.net/10722/284019-
dc.description.abstractWe present herein rPTMDetermine, an adaptive and fully automated methodology for validation of the identification of rarely occurring post-translational modifications (PTMs), using a semisupervised approach with a linear discriminant analysis (LDA) algorithm. With this strategy, verification is enhanced through similarity scoring of tandem mass spectrometry (MS/MS) comparisons between modified peptides and their unmodified analogues. We applied rPTMDetermine to (1) perform fully automated validation steps for modified peptides identified from an in silico database and (2) retrieve potential yet-to-be-identified modified peptides from raw data (that had been missed through conventional database searches). In part (1), 99 of 125 3-nitrotyrosyl-containing (nitrated) peptides obtained from a ProteinPilot search were validated and localized. Twenty nitrated peptides were falsely assigned because of incorrect monoisotopic peak assignments, leading to erroneous identification of deamidation and nitration. Five additional nitrated peptides were, however, validated after performing nonmonoisotopic peak correction. In part (2), an additional 236 unique nitrated peptides were retrieved and localized, containing 113 previously unreported nitration sites; 25 endogenous nitrated peptides with novel sites were selected and verified by comparison with synthetic analogues. In summary, we identified and confidently validated 296 unique nitrated peptides—collectively representing the largest number of endogenously identified 3-nitrotyrosyl-containing peptides from the cerebral cortex proteome of a Macaca fascicularis model of stroke. Furthermore, we harnessed the rPTMDetermine strategy to complement conventional database searching and enhance the confidence of assigning rarely occurring PTMs, while recovering many missed peptides. In a final demonstration, we successfully extended the application of rPTMDetermine to peptides featuring tryptophan oxidation.-
dc.languageeng-
dc.publisherAmerican Chemical Society. The Journal's web site is located at http://pubs.acs.org/ac-
dc.relation.ispartofAnalytical Chemistry-
dc.subjectPeptide identification-
dc.subjectPeptides and proteins-
dc.subjectOrganic reactions-
dc.subjectBiological databases-
dc.subjectIons-
dc.titlerPTMDetermine: A Fully Automated Methodology for Endogenous Tyrosine Nitration Validation, Site-Localization, and Beyond-
dc.typeArticle-
dc.identifier.emailDong, N: npdong@HKUCC-COM.hku.hk-
dc.identifier.emailSpencer, DM: dms305@hku.hk-
dc.identifier.emailChu, IK: ivankchu@hkucc.hku.hk-
dc.identifier.authorityChu, IK=rp00683-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1021/acs.analchem.0c02148-
dc.identifier.pmid32628467-
dc.identifier.hkuros311061-
dc.identifier.hkuros311063-
dc.identifier.volume92-
dc.publisher.placeUnited States-

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