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      Phosphoribosyl Diphosphate (PRPP): Biosynthesis, Enzymology, Utilization, and Metabolic Significance

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          Abstract

          SUMMARY

          Phosphoribosyl diphosphate (PRPP) is an important intermediate in cellular metabolism. PRPP is synthesized by PRPP synthase, as follows: ribose 5-phosphate + ATP → PRPP + AMP. PRPP is ubiquitously found in living organisms and is used in substitution reactions with the formation of glycosidic bonds. PRPP is utilized in the biosynthesis of purine and pyrimidine nucleotides, the amino acids histidine and tryptophan, the cofactors NAD and tetrahydromethanopterin, arabinosyl monophosphodecaprenol, and certain aminoglycoside antibiotics. The participation of PRPP in each of these metabolic pathways is reviewed. Central to the metabolism of PRPP is PRPP synthase, which has been studied from all kingdoms of life by classical mechanistic procedures. The results of these analyses are unified with recent progress in molecular enzymology and the elucidation of the three-dimensional structures of PRPP synthases from eubacteria, archaea, and humans. The structures and mechanisms of catalysis of the five diphosphoryltransferases are compared, as are those of selected enzymes of diphosphoryl transfer, phosphoryl transfer, and nucleotidyl transfer reactions. PRPP is used as a substrate by a large number phosphoribosyltransferases. The protein structures and reaction mechanisms of these phosphoribosyltransferases vary and demonstrate the versatility of PRPP as an intermediate in cellular physiology. PRPP synthases appear to have originated from a phosphoribosyltransferase during evolution, as demonstrated by phylogenetic analysis. PRPP, furthermore, is an effector molecule of purine and pyrimidine nucleotide biosynthesis, either by binding to PurR or PyrR regulatory proteins or as an allosteric activator of carbamoylphosphate synthetase. Genetic analyses have disclosed a number of mutants altered in the PRPP synthase-specifying genes in humans as well as bacterial species.

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          A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae.

          Two large-scale yeast two-hybrid screens were undertaken to identify protein-protein interactions between full-length open reading frames predicted from the Saccharomyces cerevisiae genome sequence. In one approach, we constructed a protein array of about 6,000 yeast transformants, with each transformant expressing one of the open reading frames as a fusion to an activation domain. This array was screened by a simple and automated procedure for 192 yeast proteins, with positive responses identified by their positions in the array. In a second approach, we pooled cells expressing one of about 6,000 activation domain fusions to generate a library. We used a high-throughput screening procedure to screen nearly all of the 6,000 predicted yeast proteins, expressed as Gal4 DNA-binding domain fusion proteins, against the library, and characterized positives by sequence analysis. These approaches resulted in the detection of 957 putative interactions involving 1,004 S. cerevisiae proteins. These data reveal interactions that place functionally unclassified proteins in a biological context, interactions between proteins involved in the same biological function, and interactions that link biological functions together into larger cellular processes. The results of these screens are shown here.
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            A comprehensive two-hybrid analysis to explore the yeast protein interactome.

            Protein-protein interactions play crucial roles in the execution of various biological functions. Accordingly, their comprehensive description would contribute considerably to the functional interpretation of fully sequenced genomes, which are flooded with novel genes of unpredictable functions. We previously developed a system to examine two-hybrid interactions in all possible combinations between the approximately 6,000 proteins of the budding yeast Saccharomyces cerevisiae. Here we have completed the comprehensive analysis using this system to identify 4,549 two-hybrid interactions among 3,278 proteins. Unexpectedly, these data do not largely overlap with those obtained by the other project [Uetz, P., et al. (2000) Nature (London) 403, 623-627] and hence have substantially expanded our knowledge on the protein interaction space or interactome of the yeast. Cumulative connection of these binary interactions generates a single huge network linking the vast majority of the proteins. Bioinformatics-aided selection of biologically relevant interactions highlights various intriguing subnetworks. They include, for instance, the one that had successfully foreseen the involvement of a novel protein in spindle pole body function as well as the one that may uncover a hitherto unidentified multiprotein complex potentially participating in the process of vesicular transport. Our data would thus significantly expand and improve the protein interaction map for the exploration of genome functions that eventually leads to thorough understanding of the cell as a molecular system.
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              Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry.

              The recent abundance of genome sequence data has brought an urgent need for systematic proteomics to decipher the encoded protein networks that dictate cellular function. To date, generation of large-scale protein-protein interaction maps has relied on the yeast two-hybrid system, which detects binary interactions through activation of reporter gene expression. With the advent of ultrasensitive mass spectrometric protein identification methods, it is feasible to identify directly protein complexes on a proteome-wide scale. Here we report, using the budding yeast Saccharomyces cerevisiae as a test case, an example of this approach, which we term high-throughput mass spectrometric protein complex identification (HMS-PCI). Beginning with 10% of predicted yeast proteins as baits, we detected 3,617 associated proteins covering 25% of the yeast proteome. Numerous protein complexes were identified, including many new interactions in various signalling pathways and in the DNA damage response. Comparison of the HMS-PCI data set with interactions reported in the literature revealed an average threefold higher success rate in detection of known complexes compared with large-scale two-hybrid studies. Given the high degree of connectivity observed in this study, even partial HMS-PCI coverage of complex proteomes, including that of humans, should allow comprehensive identification of cellular networks.
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                Author and article information

                Journal
                Microbiology and Molecular Biology Reviews
                Microbiol. Mol. Biol. Rev.
                American Society for Microbiology
                1092-2172
                1098-5557
                December 28 2016
                March 2017
                December 28 2016
                March 2017
                : 81
                : 1
                Article
                10.1128/MMBR.00040-16
                5312242
                28031352
                21f1b7d4-3b29-493c-86df-5d0a1c536563
                © 2017
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