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Mapping Proteoforms and Protein Complexes From King Cobra Venom Using Both Denaturing and Native Top-down Proteomics.

Rafael Donadélli MelaniOwen S SkinnerLuca FornelliGilberto B DomontPhilip D ComptonNeil L Kelleher
Published in: Molecular & cellular proteomics : MCP (2016)
Characterizing whole proteins by top-down proteomics avoids a step of inference encountered in the dominant bottom-up methodology when peptides are assembled computationally into proteins for identification. The direct interrogation of whole proteins and protein complexes from the venom of Ophiophagus hannah (king cobra) provides a sharply clarified view of toxin sequence variation, transit peptide cleavage sites and post-translational modifications (PTMs) likely critical for venom lethality. A tube-gel format for electrophoresis (called GELFrEE) and solution isoelectric focusing were used for protein fractionation prior to LC-MS/MS analysis resulting in 131 protein identifications (18 more than bottom-up) and a total of 184 proteoforms characterized from 14 protein toxin families. Operating both GELFrEE and mass spectrometry to preserve non-covalent interactions generated detailed information about two of the largest venom glycoprotein complexes: the homodimeric l-amino acid oxidase (∼130 kDa) and the multichain toxin cobra venom factor (∼147 kDa). The l-amino acid oxidase complex exhibited two clusters of multiproteoform complexes corresponding to the presence of 5 or 6 N-glycans moieties, each consistent with a distribution of N-acetyl hexosamines. Employing top-down proteomics in both native and denaturing modes provides unprecedented characterization of venom proteoforms and their complexes. A precise molecular inventory of venom proteins will propel the study of snake toxin variation and the targeted development of new antivenoms or other biotherapeutics.
Keyphrases
  • amino acid
  • mass spectrometry
  • escherichia coli
  • protein protein
  • high resolution
  • binding protein
  • small molecule
  • liquid chromatography
  • wound healing
  • hyaluronic acid
  • data analysis