nach oben

Erschienen in:

2023 | OriginalPaper | Buchkapitel

NeoMS: Identification of Novel MHC-I Peptides with Tandem Mass Spectrometry

verfasst von : Shaokai Wang, Ming Zhu, Bin Ma

Erschienen in: Bioinformatics Research and Applications

Verlag: Springer Nature Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The study of immunopeptidomics requires the identification of both regular and mutated MHC-I peptides from mass spectrometry data. For the efficient identification of MHC-I peptides with either one or no mutation from a sequence database, we propose a novel workflow: NeoMS. It employs three main modules: generating an expanded sequence database with a tagging algorithm, a machine learning-based scoring function to maximize the search sensitivity, and a careful target-decoy implementation to control the false discovery rates (FDR) of both the regular and mutated peptides. Experimental results demonstrate that NeoMS both improved the identification rate of the regular peptides over other database search software and identified hundreds of mutated peptides that have not been identified by any current methods. Further study shows the validity of these new novel peptides.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Phylogenetic Information as Soft Constraints in RNA Secondary Structure Prediction

Nächstes Kapitel On Sorting by Flanked Transpositions

He, Q., Jiang, X., Zhou, X., Weng, J.: Targeting cancers through tcr-peptide/mhc interactions. J. Hematol. Oncol. 12(1), 1–17 (2019)CrossRef

Peng, M., et al.: Neoantigen vaccine: an emerging tumor immunotherapy. Mol. Cancer 18(1), 1–14 (2019)CrossRefPubMedPubMedCentral

Karasaki, T., et al.: Prediction and prioritization of neoantigens: integration of RNA sequencing data with whole-exome sequencing. Cancer Sci. 108(2), 170–177 (2017)CrossRefPubMedPubMedCentral

Bassani-Sternberg, M., et al.: Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry. Nat. Commun. 7(1), 13404 (2016)CrossRefPubMedPubMedCentral

Zhang, J., et al. Peaks db: de novo sequencing assisted database search for sensitive and accurate peptide identification. Molecular Cellular Proteomics 11(4) (2012)

Eng, J.K., Jahan, T.A., Hoopmann, M.R.: Comet: an open-source ms/ms sequence database search tool. Proteomics 13(1), 22–24 (2013)CrossRefPubMed

Cox, J., Mann, M.: Maxquant enables high peptide identification rates, individualized ppb-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26(12), 1367–1372 (2008)CrossRefPubMed

The, M., MacCoss, M.J., Noble, W.S., Käll, L.: Fast and accurate protein false discovery rates on large-scale proteomics data sets with percolator 3.0. J. Am. Soc. Mass Spectrometry 27, 1719–1727 (2016)

Bichmann, L., et al.: Mhcquant: automated and reproducible data analysis for immunopeptidomics. J. Proteome Res. 18(11), 3876–3884 (2019)CrossRefPubMed

10.

Li, K., Jain, A., Malovannaya, A., Wen, B., Zhang, B.: Deeprescore: leveraging deep learning to improve peptide identification in immunopeptidomics. Proteomics 20(21–22), 1900334 (2020)CrossRef

11.

Wilhelm, M., et al.: Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics. Nature Commun. 12(1), 3346 (2021)CrossRef

12.

Declercq, A.: Ms2rescore: data-driven rescoring dramatically boosts immunopeptide identification rates. Molecular Cell. Proteomics 21(8) (2022)

13.

Zeng, W.-F.: Alphapeptdeep: a modular deep learning framework to predict peptide properties for proteomics. Nat. Commun. 13(1), 7238 (2022)CrossRefPubMedPubMedCentral

14.

Tran, N.H., Qiao, R., Xin, L., Chen, X., Shan, B., Li, M.: Personalized deep learning of individual immunopeptidomes to identify neoantigens for cancer vaccines. Nat. Mach. Intell. 2(12), 764–771 (2020)CrossRef

15.

Qiao, R., et al.: Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices. Nat. Mach. Intell. 3(5), 420–425 (2021)CrossRef

16.

Kong, A.T., Leprevost, F.V., Avtonomov, D.M., Mellacheruvu, D., Nesvizhskii, A.I.: Msfragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat. Methods 14(5), 513–520 (2017)CrossRefPubMedPubMedCentral

17.

Chi, H., et al.: Comprehensive identification of peptides in tandem mass spectra using an efficient open search engine. Nat. Biotechnol. 36(11), 1059–1061 (2018)CrossRef

18.

Devabhaktuni, A., et al.: Taggraph reveals vast protein modification landscapes from large tandem mass spectrometry datasets. Nat. Biotechnol. 37(4), 469–479 (2019)CrossRefPubMedPubMedCentral

19.

Kacen, A., et al.: Post-translational modifications reshape the antigenic landscape of the mhc i immunopeptidome in tumors. Nat. Biotechnol. 41(2), 239–251 (2023)CrossRefPubMed

20.

Bassani-Sternberg, M., Pletscher-Frankild, S., Jensen, L.J., Mann, M.: Mass spectrometry of human leukocyte antigen class i peptidomes reveals strong effects of protein abundance and turnover on antigen presentation*[s]. Mole. Cell. Proteomics 14(3), 658–673 (2015)CrossRef

21.

Adusumilli, R., Mallick, P.: Data conversion with proteowizard msconvert. Proteomics: methods and protocols, pp. 339–368 (2017)

22.

Ma, B.: Novor: real-time peptide de novo sequencing software. J. Am. Soc. Mass Spectrom. 26(11), 1885–1894 (2015)CrossRefPubMedPubMedCentral

23.

Di Tommaso, P., Chatzou, M., Floden, E.W., Barja, P.P., Palumbo, E., Notredame, C.: Nextflow enables reproducible computational workflows. Nat. Biotechnol. 35(4), 316–319 (2017)CrossRefPubMed

24.

UniProt Consortium: Uniprot: a hub for protein information. Nucleic Acids Res. 43(D1), D204–D212 (2015)CrossRef

25.

Wen, B., Li, K., Zhang, Y., Zhang, B.: Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis. Nat. Commun. 11(1), 1759 (2020)CrossRefPubMedPubMedCentral

26.

Zeng, W.-F., Zhou, X.-X., Zhou, W.-J., Chi, H., Zhan, J., He, S.-M.: Ms/ms spectrum prediction for modified peptides using pdeep2 trained by transfer learning. Anal. Chem. 91(15), 9724–9731 (2019)CrossRefPubMed

27.

Liu, K., Li, S., Wang, L., Ye, Y., Tang, H.: Full-spectrum prediction of peptides tandem mass spectra using deep neural network. Anal. Chem. 92(6), 4275–4283 (2020)CrossRefPubMedPubMedCentral

28.

Elias, J.E., Gygi, S.P.: Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4(3), 207–214 (2007)CrossRefPubMed

29.

O’Donnell, T.J., Rubinsteyn, A., Laserson, U.: Mhcflurry 2.0: improved pan-allele prediction of mhc class i-presented peptides by incorporating antigen processing. Cell Syst. 11(1), 42–48 (2020)

Titel: NeoMS: Identification of Novel MHC-I Peptides with Tandem Mass Spectrometry
verfasst von: Shaokai Wang
Ming Zhu
Bin Ma
Verlag: Springer Nature Singapore
Buch: Bioinformatics Research and Applications
Print ISBN: 978-981-9970-73-5

Electronic ISBN: 978-981-9970-74-2

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-981-99-7074-2_22

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner