Many proteoforms—arising from alternative splicing, post-translational modifications (PTM), or paralogous genes—have distinct biological functions, such as histone PTM proteoforms. However, their quantification by existing bottom-up mass-spectrometry (MS) methods is undermined by peptide-specific biases. To avoid these biases, we developed and implemented a first-principles model (HIquant) for quantifying proteoform stoichiometries. We characterized when MS data allow inferring proteoform stoichiometries by HIquant and derived an algorithm for optimal inference. We applied this algorithm to infer proteoform stoichiometries in two experimental systems that supported rigorous bench-marking: alkylated proteoforms spiked-in at known ratios and endogenous histone 3 PTM proteoforms quantified relative to internal heavy standards. When compared with the benchmarks, the proteoform stoichiometries interfered by HIquant without using external standards had relative error of 5–15% for simple proteoforms and 20–30% for complex proteoforms. A HIquant server is implemented at: https://web.northeastern.edu/slavov/2014_HIquant/
slavovLabThe French must have had very inebriated time all the time in the 1920s, with 184 liters of wine / person per year. That is probably 1-2 bottles of wine per adult per day ... every day, unless children actively helped with that alcohol feast!
@MaxCRoser, that's hard to imagine. t.co/WWsXN2cau0