| Literature DB >> 30282776 |
Dmitry Malioutov1, Tianchi Chen2, Edoardo Airoldi3, Jacob Jaffe4, Bogdan Budnik5, Nikolai Slavov6.
Abstract
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/2014HIquant/.Keywords: Algorithms; Bioinformatics; Bioinformatics Software; Mass Spectrometry; Mathematical Modeling
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Year: 2018 PMID: 30282776 PMCID: PMC6317479 DOI: 10.1074/mcp.TIR118.000947
Source DB: PubMed Journal: Mol Cell Proteomics ISSN: 1535-9476 Impact factor: 5.911