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1.  Exploring Bacterial Organelle Interactomes: A Model of the Protein-Protein Interaction Network in the Pdu Microcompartment 
PLoS Computational Biology  2015;11(2):e1004067.
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.
PMCID: PMC4315436  PMID: 25646976
2.  CrowdPhase: crowdsourcing the phase problem 
The idea of attacking the phase problem by crowdsourcing is introduced. Using an interactive, multi-player, web-based system, participants work simultaneously to select phase sets that correspond to better electron-density maps in order to solve low-resolution phasing problems.
The human mind innately excels at some complex tasks that are difficult to solve using computers alone. For complex problems amenable to parallelization, strategies can be developed to exploit human intelligence in a collective form: such approaches are sometimes referred to as ‘crowdsourcing’. Here, a first attempt at a crowdsourced approach for low-resolution ab initio phasing in macromolecular crystallography is proposed. A collaborative online game named CrowdPhase was designed, which relies on a human-powered genetic algorithm, where players control the selection mechanism during the evolutionary process. The algorithm starts from a population of ‘individuals’, each with a random genetic makeup, in this case a map prepared from a random set of phases, and tries to cause the population to evolve towards individuals with better phases based on Darwinian survival of the fittest. Players apply their pattern-recognition capabilities to evaluate the electron-density maps generated from these sets of phases and to select the fittest individuals. A user-friendly interface, a training stage and a competitive scoring system foster a network of well trained players who can guide the genetic algorithm towards better solutions from generation to generation via gameplay. CrowdPhase was applied to two synthetic low-resolution phasing puzzles and it was shown that players could successfully obtain phase sets in the 30° phase error range and corresponding molecular envelopes showing agreement with the low-resolution models. The successful preliminary studies suggest that with further development the crowdsourcing approach could fill a gap in current crystallographic methods by making it possible to extract meaningful information in cases where limited resolution might otherwise prevent initial phasing.
PMCID: PMC4051500  PMID: 24914965
CrowdPhase; crowdsourcing; phase problem
3.  Protein tandem repeats: the more perfect the less structured 
The Febs Journal  2010;277(12):2673-2682.
We analysed structural properties of protein regions containing arrays of perfect and nearly perfect tandem repeats. Naturally occurring proteins with perfect repeats are practically absent among the proteins with known 3D structures. The great majority of such regions in the Protein DataBank (PDB) are found in the de novo designed proteins. The abundance of natural structured proteins with tandem repeats is inversely correlated with the repeat perfection: the chance to find natural structured proteins in PDB increases with a decrease in the level of repeat perfection. Prediction of intrinsic disorder within the tandem repeats in the SwissProt proteins supports the conclusion that the level of repeat perfection correlates with their tendency to be unstructured. This correlation is valid across the various species and subcellular localizations, although the level of disordered tandem repeats varies significantly between these datasets. On average, in prokaryotes, tandem repeats of cytoplasmic proteins were predicted to be the most structured, whereas in eukaryotes, the most structured portion of the repeats was found in the membrane proteins. Our study supports the hypothesis that in general, the repeat perfection is a sign of recent evolutionary events rather than of exceptional structural and (or) functional importance of the repeat residues.
PMCID: PMC2928880  PMID: 20553501
bioinformatics; disordered conformation; evolution; sequence analysis; protein structure
4.  Widespread Disulfide Bonding in Proteins from Thermophilic Archaea 
Archaea  2011;2011:409156.
Disulfide bonds are generally not used to stabilize proteins in the cytosolic compartments of bacteria or eukaryotic cells, owing to the chemically reducing nature of those environments. In contrast, certain thermophilic archaea use disulfide bonding as a major mechanism for protein stabilization. Here, we provide a current survey of completely sequenced genomes, applying computational methods to estimate the use of disulfide bonding across the Archaea. Microbes belonging to the Crenarchaeal branch, which are essentially all hyperthermophilic, are universally rich in disulfide bonding while lesser degrees of disulfide bonding are found among the thermophilic Euryarchaea, excluding those that are methanogenic. The results help clarify which parts of the archaeal lineage are likely to yield more examples and additional specific data on protein disulfide bonding, as increasing genomic sequencing efforts are brought to bear.
PMCID: PMC3177088  PMID: 21941460

Results 1-4 (4)