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      Quantitative reactivity profiling predicts functional cysteines in proteomes

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          Abstract

          Cysteine is the most intrinsically nucleophilic amino acid in proteins, where its reactivity is tuned to perform diverse biochemical functions. The absence of a consensus sequence that defines functional cysteines in proteins has hindered their discovery and characterization. Here, we describe a proteomics method to quantitatively profile the intrinsic reactivity of cysteine residues en masse directly in native biological systems. Hyperreactivity was a rare feature among cysteines and found to specify a wide range of activities, including nucleophilic and reductive catalysis and sites of oxidative modification. Hyperreactive cysteines were identified in several proteins of uncharacterized function, including a residue conserved across eukaryotic phylogeny that we show is required for yeast viability and involved in iron-sulfur protein biogenesis. Finally, we demonstrate that quantitative reactivity profiling can also form the basis for screening and functional assignment of cysteines in computationally designed proteins, where it discriminated catalytically active from inactive cysteine hydrolase designs.

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          Most cited references48

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          Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae.

          Defining protein complexes is critical to virtually all aspects of cell biology. Two recent affinity purification/mass spectrometry studies in Saccharomyces cerevisiae have vastly increased the available protein interaction data. The practical utility of such high throughput interaction sets, however, is substantially decreased by the presence of false positives. Here we created a novel probabilistic metric that takes advantage of the high density of these data, including both the presence and absence of individual associations, to provide a measure of the relative confidence of each potential protein-protein interaction. This analysis largely overcomes the noise inherent in high throughput immunoprecipitation experiments. For example, of the 12,122 binary interactions in the general repository of interaction data (BioGRID) derived from these two studies, we marked 7504 as being of substantially lower confidence. Additionally, applying our metric and a stringent cutoff we identified a set of 9074 interactions (including 4456 that were not among the 12,122 interactions) with accuracy comparable to that of conventional small scale methodologies. Finally we organized proteins into coherent multisubunit complexes using hierarchical clustering. This work thus provides a highly accurate physical interaction map of yeast in a format that is readily accessible to the biological community.
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            De novo computational design of retro-aldol enzymes.

            The creation of enzymes capable of catalyzing any desired chemical reaction is a grand challenge for computational protein design. Using new algorithms that rely on hashing techniques to construct active sites for multistep reactions, we designed retro-aldolases that use four different catalytic motifs to catalyze the breaking of a carbon-carbon bond in a nonnatural substrate. Of the 72 designs that were experimentally characterized, 32, spanning a range of protein folds, had detectable retro-aldolase activity. Designs that used an explicit water molecule to mediate proton shuffling were significantly more successful, with rate accelerations of up to four orders of magnitude and multiple turnovers, than those involving charged side-chain networks. The atomic accuracy of the design process was confirmed by the x-ray crystal structure of active designs embedded in two protein scaffolds, both of which were nearly superimposable on the design model.
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              DTASelect and Contrast: tools for assembling and comparing protein identifications from shotgun proteomics.

              The components of complex peptide mixtures can be separated by liquid chromatography, fragmented by tandem mass spectrometry, and identified by the SEQUEST algorithm. Inferring a mixture's source proteins requires that the identified peptides be reassociated. This process becomes more challenging as the number of peptides increases. DTASelect, a new software package, assembles SEQUEST identifications and highlights the most significant matches. The accompanying Contrast tool compares DTASelect results from multiple experiments. The two programs improve the speed and precision of proteomic data analysis.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                0028-0836
                1476-4687
                14 September 2010
                17 November 2010
                9 December 2010
                9 June 2011
                : 468
                : 7325
                : 790-795
                Affiliations
                [1 ]The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, California 92037, USA.
                [2 ]Department of Chemical Physiology, The Scripps Research Institute, La Jolla, California 92037, USA.
                [3 ]Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA.
                [4 ]Interdisciplinary Program in Biomolecular Structure and Design, University of Washington, Seattle, Washington 98195, USA.
                [5 ]Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, USA.
                Author notes
                Correspondence and requests for materials should be addressed to B.F.C. cravatt@ 123456scripps.edu
                [*]

                These authors contributed equally to this work.

                Article
                nihpa234109
                10.1038/nature09472
                3058684
                21085121
                95ed3ca0-f732-4ecc-8e7c-093296cfbafc

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                History
                Funding
                Funded by: National Institute of Mental Health : NIMH
                Funded by: National Cancer Institute : NCI
                Funded by: National Institute of General Medical Sciences : NIGMS
                Award ID: U54 MH084512-030004 ||MH
                Funded by: National Institute of Mental Health : NIMH
                Funded by: National Cancer Institute : NCI
                Funded by: National Institute of General Medical Sciences : NIGMS
                Award ID: R37 CA087660-10 ||CA
                Funded by: National Institute of Mental Health : NIMH
                Funded by: National Cancer Institute : NCI
                Funded by: National Institute of General Medical Sciences : NIGMS
                Award ID: R01 GM090294-02 ||GM
                Funded by: National Institute of Mental Health : NIMH
                Funded by: National Cancer Institute : NCI
                Funded by: National Institute of General Medical Sciences : NIGMS
                Award ID: R01 CA087660-09 ||CA
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