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• W2317998431 abstract "Mitochondria are heterogeneous organelles involved in energy production, metabolism, and cellular signaling that oftentimes are isolated from cells for chemical characterization (e.g., proteomic analysis). The chemical composition of the mitochondrial outer membrane is one of the factors defining the mitochondrial isoelectric point (pI), which is a property useful for the analysis and characterization of isolated mitochondria. We previously used capillary isoelectric focusing (cIEF) with laser-induced-fluorescence detection to determine the experimental pI of individual mitochondria after their isolation under depolarizing conditions. This technique revealed that, when kept nonfunctional, mitochondrial pI is heterogeneous as displayed by the observed distributions of pI. To model the effect of surface composition on pI heterogeneity of these mitochondria, we devised a method to predict mitochondrial pI values using simulated surface compositions. The method was initially validated by predicting the pI values of known mitochondrial outer membrane proteins and was then extended to isolated mitochondria, in which both ionizable amino acids and phospholipids contribute to mitochondrial pI. After using a Monte Carlo method to generate a library of over 2 million possible mitochondrial surface compositions, sufficient compositions to match the frequency of occurrence of experimental mitochondrial pI values were randomly selected. This comparison allows for association of a given individual mitochondrial pI with thousands of randomly chosen compositions. The method predicts significant changes in the percentages of some amino acids and phospholipids for observed pI differences between individual mitochondria, which is an important advancement toward explaining the observed heterogeneity of mitochondrial pI." @default.
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• W2317998431 date "2013-02-08" @default.
• W2317998431 modified "2023-10-11" @default.
• W2317998431 title "Predicting Isoelectric Points of Nonfunctional Mitochondria from Monte Carlo Simulations of Surface Compositions" @default.
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• W2317998431 doi "https://doi.org/10.1021/la304129y" @default.
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