Mass spectrometry data clustering of neuropeptides

Dnr:

SNIC 2017/7-60

Type:

SNAC Small

Principal Investigator:

Matthew The

Affiliation:

Kungliga Tekniska högskolan

Start Date:

2017-04-10

End Date:

2018-05-01

Primary Classification:

10203: Bioinformatik (beräkningsbiologi) (tillämpningar under 10610)

Webpage:

Allocation

Abstract

Neuropeptides play an important role in signalling between neurons and are known to be involved in processes such as addiction and depression [1]. Applying tandem mass spectrometry, these neuropeptides can be identified and quantified, and related to the process of interest [2],[3]. Opposed to regular proteins, these peptides undergo several transformations from their so-called precursor protein to their functional neuropeptides [4], and can therefore not be readily predicted from their corresponding genes. This poses a problem for conventional database search algorithms (e.g. MSGF+ [5] or Tide [6]), as the number of hypotheses grows exponentially. We have previously developed a clustering algorithm, MaRaCluster [7], which could help in reducing the number of spectra that need to be searched, as well as discover frequently occurring unidentified spectra. The former helps in reducing the number of hypothesis directly, while the latter opens up the possibility of using search algorithms that employ experimental spectra (e.g. SpectraST [8] or BiblioSpec [9]), instead of theoretical spectra. [1] Hökfelt, Tomas, et al. "Neuropeptides—an overview." Neuropharmacology 39.8 (2000): 1337-1356. [2] Bourdenx, Mathieu, et al. "Abnormal structure-specific peptide transmission and processing in a primate model of Parkinson's disease and l-DOPA-induced dyskinesia." Neurobiology of disease 62 (2014): 307-312. [3] Svensson, Marcus, et al. "Peptidomics-based discovery of novel neuropeptides." Journal of proteome research 2.2 (2003): 213-219. [4] Lee, Ji Eun. "Neuropeptidomics: Mass Spectrometry-Based Identification and Quantitation of Neuropeptides." Genomics & informatics 14.1 (2016): 12-19. [5] Kim, Sangtae, Nitin Gupta, and Pavel A. Pevzner. "Spectral probabilities and generating functions of tandem mass spectra: a strike against decoy databases." Journal of proteome research 7.8 (2008): 3354-3363. [6] McIlwain, Sean, et al. "Crux: rapid open source protein tandem mass spectrometry analysis." Journal of proteome research 13.10 (2014): 4488-4491. [7] The, Matthew, and Lukas Käll. "MaRaCluster: A Fragment Rarity Metric for Clustering Fragment Spectra in Shotgun Proteomics." Journal of proteome research 15.3 (2016): 713-720. [8] Lam, H., Deutsch, E. W., Eddes, J. S., Eng, J. K., King, N., Stein, S. E. and Aebersold, R. (2007), Development and validation of a spectral library searching method for peptide identification from MS/MS. Proteomics, 7: 655–667. doi: 10.1002/pmic.200600625 [9] Frewen, Barbara E., et al. "Analysis of peptide MS/MS spectra from large-scale proteomics experiments using spectrum libraries." Analytical chemistry 78.16 (2006): 5678-5684.