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      Cell-wide analysis of protein thermal unfolding reveals determinants of thermostability.

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          Abstract

          Temperature-induced cell death is thought to be due to protein denaturation, but the determinants of thermal sensitivity of proteomes remain largely uncharacterized. We developed a structural proteomic strategy to measure protein thermostability on a proteome-wide scale and with domain-level resolution. We applied it to Escherichia coli, Saccharomyces cerevisiae, Thermus thermophilus, and human cells, yielding thermostability data for more than 8000 proteins. Our results (i) indicate that temperature-induced cellular collapse is due to the loss of a subset of proteins with key functions, (ii) shed light on the evolutionary conservation of protein and domain stability, and (iii) suggest that natively disordered proteins in a cell are less prevalent than predicted and (iv) that highly expressed proteins are stable because they are designed to tolerate translational errors that would lead to the accumulation of toxic misfolded species.

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          Most cited references51

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          Mistranslation-induced protein misfolding as a dominant constraint on coding-sequence evolution.

          Strikingly consistent correlations between rates of coding-sequence evolution and gene expression levels are apparent across taxa, but the biological causes behind the selective pressures on coding-sequence evolution remain controversial. Here, we demonstrate conserved patterns of simple covariation between sequence evolution, codon usage, and mRNA level in E. coli, yeast, worm, fly, mouse, and human that suggest that all observed trends stem largely from a unified underlying selective pressure. In metazoans, these trends are strongest in tissues composed of neurons, whose structure and lifetime confer extreme sensitivity to protein misfolding. We propose, and demonstrate using a molecular-level evolutionary simulation, that selection against toxicity of misfolded proteins generated by ribosome errors suffices to create all of the observed covariation. The mechanistic model of molecular evolution that emerges yields testable biochemical predictions, calls into question the use of nonsynonymous-to-synonymous substitution ratios (Ka/Ks) to detect functional selection, and suggests how mistranslation may contribute to neurodegenerative disease.
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            Tracking cancer drugs in living cells by thermal profiling of the proteome.

            The thermal stability of proteins can be used to assess ligand binding in living cells. We have generalized this concept by determining the thermal profiles of more than 7000 proteins in human cells by means of mass spectrometry. Monitoring the effects of small-molecule ligands on the profiles delineated more than 50 targets for the kinase inhibitor staurosporine. We identified the heme biosynthesis enzyme ferrochelatase as a target of kinase inhibitors and suggest that its inhibition causes the phototoxicity observed with vemurafenib and alectinib. Thermal shifts were also observed for downstream effectors of drug treatment. In live cells, dasatinib induced shifts in BCR-ABL pathway proteins, including CRK/CRKL. Thermal proteome profiling provides an unbiased measure of drug-target engagement and facilitates identification of markers for drug efficacy and toxicity. Copyright © 2014, American Association for the Advancement of Science.
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              Is Open Access

              eggNOG: automated construction and annotation of orthologous groups of genes

              The identification of orthologous genes forms the basis for most comparative genomics studies. Existing approaches either lack functional annotation of the identified orthologous groups, hampering the interpretation of subsequent results, or are manually annotated and thus lag behind the rapid sequencing of new genomes. Here we present the eggNOG database (‘evolutionary genealogy of genes: Non-supervised Orthologous Groups’), which contains orthologous groups constructed from Smith–Waterman alignments through identification of reciprocal best matches and triangular linkage clustering. Applying this procedure to 312 bacterial, 26 archaeal and 35 eukaryotic genomes yielded 43 582 course-grained orthologous groups of which 9724 are extended versions of those from the original COG/KOG database. We also constructed more fine-grained groups for selected subsets of organisms, such as the 19 914 mammalian orthologous groups. We automatically annotated our non-supervised orthologous groups with functional descriptions, which were derived by identifying common denominators for the genes based on their individual textual descriptions, annotated functional categories, and predicted protein domains. The orthologous groups in eggNOG contain 1 241 751 genes and provide at least a broad functional description for 77% of them. Users can query the resource for individual genes via a web interface or download the complete set of orthologous groups at http://eggnog.embl.de.
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                Author and article information

                Journal
                Science
                Science (New York, N.Y.)
                American Association for the Advancement of Science (AAAS)
                1095-9203
                0036-8075
                Feb 24 2017
                : 355
                : 6327
                Affiliations
                [1 ] Institute of Biochemistry, Department of Biology, ETH Zurich (ETHZ), CH-8093 Zurich, Switzerland.
                [2 ] Systems Biology Graduate School PhD Program, ETHZ and University of Zurich, CH-8093 Zurich, Switzerland.
                [3 ] Institute of Molecular Systems Biology, Department of Biology, ETHZ, CH-8093 Zurich, Switzerland.
                [4 ] Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, CH-8057 Zurich, Switzerland.
                [5 ] Institute of Biochemistry, Department of Biology, ETH Zurich (ETHZ), CH-8093 Zurich, Switzerland. paola.picotti@bc.biol.ethz.ch.
                Article
                355/6327/eaai7825
                10.1126/science.aai7825
                28232526
                3352bf19-337a-44be-a367-58ed1e0e8d96
                History

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