Prediction of protein complexes in Trypanosoma brucei by protein correlation profiling mass spectrometry and machine learning

Thomas W. M. Crozier, Michele Tinti, Mark Larance, Angus I. Lamond (Lead / Corresponding author), Michael A. J. Ferguson (Lead / Corresponding author)

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Abstract

A disproportionate number of predicted proteins from the genome sequence of the protozoan parasite Trypanosoma brucei, an important human and animal pathogen, are hypothetical proteins of unknown function. This paper describes a protein correlation profiling mass spectrometry approach, using two size exclusion and one ion exchange chromatography systems, to derive sets of predicted protein complexes in this organism by hierarchical clustering and machine learning methods. These hypothesis-generating proteomic data are provided in an open access online data visualisation environment (http://134.36.66.166:8083/complex_explorer). The data can be searched conveniently via a user friendly, custom graphical interface. We provide examples of both potential new subunits of known protein complexes and of novel trypanosome complexes of suggested function, contributing to improving the functional annotation of the trypanosome proteome. Data are available via ProteomeXchange with identifier PXD005968.

Original languageEnglish
Pages (from-to)2254-2267
Number of pages14
JournalMolecular & Cellular Proteomics
Volume16
Issue number12
Early online date17 Oct 2017
DOIs
Publication statusPublished - 1 Dec 2017

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Keywords

  • Trypanosoma
  • procyclic
  • protein correlation profiling mass spectrometry
  • machine learning
  • protein complexes

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