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      Exploring Bacterial Organelle Interactomes: A Model of the Protein-Protein Interaction Network in the Pdu Microcompartment

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          Abstract

          Bacterial microcompartments (MCPs) are protein-bound organelles that carry out diverse metabolic pathways in a wide range of bacteria. These supramolecular assemblies consist of a thin outer protein shell, reminiscent of a viral capsid, which encapsulates sequentially acting enzymes. The most complex MCP elucidated so far is the propanediol utilizing (Pdu) microcompartment. It contains the reactions for degrading 1,2-propanediol. While several experimental studies on the Pdu system have provided hints about its organization, a clear picture of how all the individual components interact has not emerged yet. Here we use co-evolution-based methods, involving pairwise comparisons of protein phylogenetic trees, to predict the protein-protein interaction (PPI) network governing the assembly of the Pdu MCP. We propose a model of the Pdu interactome, from which selected PPIs are further inspected via computational docking simulations. We find that shell protein PduA is able to serve as a “universal hub” for targeting an array of enzymes presenting special N-terminal extensions, namely PduC, D, E, L and P. The varied N-terminal peptides are predicted to bind in the same cleft on the presumptive luminal face of the PduA hexamer. We also propose that PduV, a protein of unknown function with remote homology to the Ras-like GTPase superfamily, is likely to localize outside the MCP, interacting with the protruding β-barrel of the hexameric PduU shell protein. Preliminary experiments involving a bacterial two-hybrid assay are presented that corroborate the existence of a PduU-PduV interaction. This first systematic computational study aimed at characterizing the interactome of a bacterial microcompartment provides fresh insight into the organization of the Pdu MCP.

          Author Summary

          Many bacteria produce giant proteinaceous structures within their cells, which they use to carry out special metabolic reactions in their interior. Much has been learned recently about the individual components—shell proteins and encapsulated enzymes—that assemble together, thousands of subunits in all, to make these bacterial microcompartments or MCPs. However, in order to carry out their biological functions, these systems must be highly organized through specific protein-protein interactions, and such a higher level understanding of organization in MCP systems is lacking. In this study, we use genomic data and phylogenetic analysis to predict the network of interactions between the approximately 20 different kinds of proteins and enzymes present in the Pdu MCP. Then, we use computational docking to examine a subset of those that are predicted to involve enzymes bound to the interior surface of the shell proteins, and show that the results are consistent with recent experimental data. We further provide new experimental evidence for one of the predicted protein-protein interactions. This study expands our understanding of a complex system of proteins serving as a metabolic organelle in bacterial cells, and provides a foundation for further experimental investigations.

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          Dating of the human-ape splitting by a molecular clock of mitochondrial DNA.

          A new statistical method for estimating divergence dates of species from DNA sequence data by a molecular clock approach is developed. This method takes into account effectively the information contained in a set of DNA sequence data. The molecular clock of mitochondrial DNA (mtDNA) was calibrated by setting the date of divergence between primates and ungulates at the Cretaceous-Tertiary boundary (65 million years ago), when the extinction of dinosaurs occurred. A generalized least-squares method was applied in fitting a model to mtDNA sequence data, and the clock gave dates of 92.3 +/- 11.7, 13.3 +/- 1.5, 10.9 +/- 1.2, 3.7 +/- 0.6, and 2.7 +/- 0.6 million years ago (where the second of each pair of numbers is the standard deviation) for the separation of mouse, gibbon, orangutan, gorilla, and chimpanzee, respectively, from the line leading to humans. Although there is some uncertainty in the clock, this dating may pose a problem for the widely believed hypothesis that the pipedal creature Australopithecus afarensis, which lived some 3.7 million years ago at Laetoli in Tanzania and at Hadar in Ethiopia, was ancestral to man and evolved after the human-ape splitting. Another likelier possibility is that mtDNA was transferred through hybridization between a proto-human and a proto-chimpanzee after the former had developed bipedalism.
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            Assigning protein functions by comparative genome analysis: protein phylogenetic profiles.

            Determining protein functions from genomic sequences is a central goal of bioinformatics. We present a method based on the assumption that proteins that function together in a pathway or structural complex are likely to evolve in a correlated fashion. During evolution, all such functionally linked proteins tend to be either preserved or eliminated in a new species. We describe this property of correlated evolution by characterizing each protein by its phylogenetic profile, a string that encodes the presence or absence of a protein in every known genome. We show that proteins having matching or similar profiles strongly tend to be functionally linked. This method of phylogenetic profiling allows us to predict the function of uncharacterized proteins.
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              The use of gene clusters to infer functional coupling.

              Previously, we presented evidence that it is possible to predict functional coupling between genes based on conservation of gene clusters between genomes. With the rapid increase in the availability of prokaryotic sequence data, it has become possible to verify and apply the technique. In this paper, we extend our characterization of the parameters that determine the utility of the approach, and we generalize the approach in a way that supports detection of common classes of functionally coupled genes (e.g., transport and signal transduction clusters). Now that the analysis includes over 30 complete or nearly complete genomes, it has become clear that this approach will play a significant role in supporting efforts to assign functionality to the remaining uncharacterized genes in sequenced genomes.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                February 2015
                3 February 2015
                : 11
                : 2
                : e1004067
                Affiliations
                [1 ]UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, California, United States of America
                [2 ]Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa, United States of America
                [3 ]Molecular Biology Institute, University of California, Los Angeles, Los Angeles, California, United States of America
                [4 ]Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, United States of America
                Boston University, United States of America
                Boston University, UNITED STATES
                Author notes

                The authors declare that no competing interests exist.

                Conceived and designed the experiments: JJ TAB. Performed the experiments: JJ YL. Analyzed the data: JJ YL TAB TOY. Wrote the paper: JJ TOY YL TAB.

                Article
                PCOMPBIOL-D-14-01300
                10.1371/journal.pcbi.1004067
                4315436
                25646976
                976ca8a3-caa3-4afe-a753-289b8877c8f7
                Copyright @ 2015

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 15 July 2014
                : 1 December 2014
                Page count
                Figures: 6, Tables: 1, Pages: 23
                Funding
                This work was funded by grant AI081146 from the National Institutes of Health. TOY is supported by the BER program of the U.S. Department of Energy Office of Science. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

                Quantitative & Systems biology
                Quantitative & Systems biology

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