Dataset Information

A Systematic Protein Turnover Map for Decoding Protein Degradation.

ABSTRACT: Protein degradation is mediated by an expansive and complex network of protein modification and degradation enzymes. Matching degradation enzymes with their targets and determining globally which proteins are degraded by the proteasome or lysosome/vacuole have been a major challenge. Furthermore, an integrated view of protein degradation for cellular pathways has been lacking. Here, we present an analytical platform that combines systematic gene deletions with quantitative measures of protein turnover to deconvolve protein degradation pathways for Saccharomyces cerevisiae. The resulting turnover map (T-MAP) reveals target candidates of nearly all E2 and E3 ubiquitin ligases and identifies the primary degradation routes for most proteins. We further mined this T-MAP to identify new substrates of ER-associated degradation (ERAD) involved in sterol biosynthesis and to uncover regulatory nodes for sphingolipid biosynthesis. The T-MAP approach should be broadly applicable to the study of other cellular processes, including mammalian systems.

SUBMITTER: Christiano R

PROVIDER: S-EPMC7745259 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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A Systematic Protein Turnover Map for Decoding Protein Degradation.

Christiano Romain R Arlt Henning H Kabatnik Sonja S Mejhert Niklas N Lai Zon Weng ZW Farese Robert V RV Walther Tobias C TC

Cell reports 20201101 6

Protein degradation is mediated by an expansive and complex network of protein modification and degradation enzymes. Matching degradation enzymes with their targets and determining globally which proteins are degraded by the proteasome or lysosome/vacuole have been a major challenge. Furthermore, an integrated view of protein degradation for cellular pathways has been lacking. Here, we present an analytical platform that combines systematic gene deletions with quantitative measures of protein tu ...[more]

PMID: 33176155

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Project description:In higher eukaryotes many genes encode protein isoforms whose properties and biological roles are often poorly characterized. Here we describe systematic approaches for detection of either distinct isoforms, or separate pools of the same isoform, with differential biological properties. Using information from ion intensities we have estimated protein abundance levels and using rates of change in stable isotope labeling with amino acids in cell culture isotope ratios we measured turnover rates and subcellular distribution for the HeLa cell proteome. Protein isoforms were detected using three data analysis strategies that evaluate differences between stable isotope labeling with amino acids in cell culture isotope ratios for specific groups of peptides within the total set of peptides assigned to a protein. The candidate approach compares stable isotope labeling with amino acids in cell culture isotope ratios for predicted isoform-specific peptides, with ratio values for peptides shared by all the isoforms. The rule of thirds approach compares the mean isotope ratio values for all peptides in each of three equal segments along the linear length of the protein, assessing differences between segment values. The three in a row approach compares mean isotope ratio values for each sequential group of three adjacent peptides, assessing differences with the mean value for all peptides assigned to the protein. Protein isoforms were also detected and their properties evaluated by fractionating cell extracts on one-dimensional SDS-PAGE prior to trypsin digestion and MS analysis and independently evaluating isotope ratio values for the same peptides isolated from different gel slices. The effect of protein phosphorylation on turnover rates was analyzed by comparing mean turnover values calculated for all peptides assigned to a protein, either including, or excluding, values for cognate phosphopeptides. Collectively, these experimental and analytical approaches provide a framework for expanding the functional annotation of the genome.

Dataset Information

A Systematic Protein Turnover Map for Decoding Protein Degradation.

Publications

A Systematic Protein Turnover Map for Decoding Protein Degradation.

Similar Datasets

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