Complete Characterization of Cardiac Myosin Heavy Chain (223 kDa) Enabled by Size-Exclusion Chromatography and Middle-Down Mass Spectrometry.
Ontology highlight
ABSTRACT: Myosin heavy chain (MHC), the major component of the myosin motor molecule, plays an essential role in force production during muscle contraction. However, a comprehensive analysis of MHC proteoforms arising from sequence variations and post-translational modifications (PTMs) remains challenging due to the difficulties in purifying MHC (?223 kDa) and achieving complete sequence coverage. Herein, we have established a strategy to effectively purify and comprehensively characterize MHC from heart tissue by combining size-exclusion chromatography (SEC) and middle-down mass spectrometry (MS). First, we have developed a MS-compatible SEC method for purifying MHC from heart tissue with high efficiency. Next, we have optimized the Glu-C, Asp-N, and trypsin limited digestion conditions for middle-down MS. Subsequently, we have applied this strategy with optimized conditions to comprehensively characterize human MHC and identified ?-MHC as the predominant isoform in human left ventricular tissue. Full sequence coverage based on highly accurate mass measurements has been achieved using middle-down MS combining 1 Glu-C, 1 Asp-N, and 1 trypsin digestion. Three different PTMs: acetylation, methylation, and trimethylation were identified in human ?-MHC and the corresponding sites were localized to the N-terminal Gly, Lys34, and Lys129, respectively, by electron capture dissociation (ECD). Taken together, we have demonstrated this strategy is highly efficient for purification and characterization of MHC, which can be further applied to studies of the role of MHC proteoforms in muscle-related diseases. We also envision that this integrated SEC/middle-down MS strategy can be extended for the characterization of other large proteins over 200 kDa.
SUBMITTER: Jin Y
PROVIDER: S-EPMC5526197 | biostudies-literature | 2017 May
REPOSITORIES: biostudies-literature
ACCESS DATA