scholarly journals Multiple Binding Configurations of Fis Protein Pairs on DNA: Facilitated Dissociation versus Cooperative Dissociation

2019 ◽  
Author(s):  
Min-Yeh Tsai ◽  
Weihua Zheng ◽  
Mingchen Chen ◽  
Peter G. Wolynes
Keyword(s):  
Protein Interactions ◽  
Specific Binding ◽  
Dissociation Rate ◽  
Solution Phase ◽  
Protein Protein Interactions ◽  
E Coli ◽  
Dna Binding Sites ◽  
Multiple Binding ◽  
Broadcasting System ◽  
Complex Structural

AbstractAs a master transcription regulator, Fis protein influences over two hundred genes of E-coli. Fis protein’s non-specific binding to DNA is widely acknowledged, and its kinetics of dissociation from DNA is strongly influenced by its surroundings: the dissociation rate increases as the concentration of Fis protein in the solution-phase increases. In this study, we use computational methods to explore the global binding energy landscape of the Fis1:Fis2:DNA ternary complex. The complex contains a binary-Fis molecular dyad whose formation relies on complex structural rearrangements. The simulations allow us to distinguish several different pathways for the dissociation of the protein from DNA with different functional outcomes, and involving different protein stoichiometries: 1. Simple exchange of proteins and 2. Cooperative unbinding of two Fis proteins to yield bare DNA. In the case of exchange, the protein on the DNA is replaced by solution-phase protein through competition for DNA binding sites. This process seen in fluorescence imaging experiments has been called facilitated dissociation. In the latter case of cooperative unbinding of pairs, two neighboring Fis proteins on DNA form a unique binary-Fis configuration via protein-protein interactions, which in turn leads to the co-dissociation of both molecules simultaneously, a process akin to the “molecular stripping” seen in the NFκB/IκB genetic broadcasting system. This simulation shows that the existence of multiple binding configurations of transcription factors can have a significant impact on the kinetics and outcome of transcription factor dissociation from DNA, with important implications for the systems biology of gene regulation by Fis.

scholarly journals Driving the expression of theSalmonella entericasv Typhimurium flagellum usingflhDCfromEscherichia coliresults in key regulatory and cellular differences

2018 ◽  
Author(s):  
Ayman Albanna ◽  
Martin Sim ◽  
Paul A Hoskisson ◽  
Colin Gillespie ◽  
Christopher V. Rao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Flagellar Gene ◽  
Protein Protein Interactions ◽  
Chromosomal Locus ◽  
Genetic Changes ◽  
E Coli ◽  
Dna Binding Sites ◽  
Regulatory Feedback Loop ◽  
The Impact

ABSTRACTThe flagellar systems ofEscherichia coliandSalmonella entericaexhibit a significant level of genetic and functional synteny. Both systems are controlled by the flagellar specific master regulator FlhD4C2. Since the early days of genetic analyses of flagellar systems it has been known thatE. coli flhDCcan complement a ΔflhDCmutant inS. enterica. The genomic revolution has identified how genetic changes to transcription factors and / or DNA binding sites can impact the phenotypic outcome across related species. We were therefore interested in asking: using modern tools to interrogate flagellar gene expression and assembly, what would the impact be of replacing theflhDCcoding sequences inS. entericafor theE. coligenes at theflhDC S. enterciachromosomal locus? We show that even though all strains created are motile, flagellar gene expression is measurably lower whenflhDCECare present. These changes can be attributed to the impact of FlhD4C2DNA recognition and the protein-protein interactions required to generate a stable FlhD4C2complex. Furthermore, our data suggests that inE. colithe internal flagellar FliT regulatory feedback loop has a marked difference with respect to output of the flagellar systems. We argue due diligence is required in making assumptions based on heterologous expression of regulators and that even systems showing significant synteny may not behave in exactly the same manner.

scholarly journals E. coli primase and DNA polymerase III holoenzyme are able to bind concurrently to a primed template during DNA replication

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Andrea Bogutzki ◽  
Natalie Naue ◽  
Lidia Litz ◽  
Andreas Pich ◽  
Ute Curth
Keyword(s):  
Dna Replication ◽  
Dna Polymerase ◽  
Protein Interactions ◽  
Dna Polymerase Iii ◽  
Protein Protein Interactions ◽  
Single Stranded Dna ◽  
E Coli ◽  
Polymerase Iii ◽  
C Terminus ◽  
Pol Iii

Abstract During DNA replication in E. coli, a switch between DnaG primase and DNA polymerase III holoenzyme (pol III) activities has to occur every time when the synthesis of a new Okazaki fragment starts. As both primase and the χ subunit of pol III interact with the highly conserved C-terminus of single-stranded DNA-binding protein (SSB), it had been proposed that the binding of both proteins to SSB is mutually exclusive. Using a replication system containing the origin of replication of the single-stranded DNA phage G4 (G4ori) saturated with SSB, we tested whether DnaG and pol III can bind concurrently to the primed template. We found that the addition of pol III does not lead to a displacement of primase, but to the formation of higher complexes. Even pol III-mediated primer elongation by one or several DNA nucleotides does not result in the dissociation of DnaG. About 10 nucleotides have to be added in order to displace one of the two primase molecules bound to SSB-saturated G4ori. The concurrent binding of primase and pol III is highly plausible, since even the SSB tetramer situated directly next to the 3′-terminus of the primer provides four C-termini for protein-protein interactions.

scholarly journals The Protein Interactome of Glycolysis in Escherichia coli

Proteomes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 16
Author(s):  
Shomeek Chowdhury ◽  
Stephen Hepper ◽  
Mudassir K. Lodi ◽  
Milton H. Saier ◽  
Peter Uetz
Keyword(s):  
Escherichia Coli ◽  
Protein Interactions ◽  
Allosteric Regulation ◽  
Glycolytic Enzymes ◽  
Protein Protein Interactions ◽  
Post Translational Modification ◽  
Interacting Protein ◽  
E Coli ◽  
Protein Interactome ◽  
The Impact

Glycolysis is regulated by numerous mechanisms including allosteric regulation, post-translational modification or protein-protein interactions (PPI). While glycolytic enzymes have been found to interact with hundreds of proteins, the impact of only some of these PPIs on glycolysis is well understood. Here we investigate which of these interactions may affect glycolysis in E. coli and possibly across numerous other bacteria, based on the stoichiometry of interacting protein pairs (from proteomic studies) and their conservation across bacteria. We present a list of 339 protein-protein interactions involving glycolytic enzymes but predict that ~70% of glycolytic interactors are not present in adequate amounts to have a significant impact on glycolysis. Finally, we identify a conserved but uncharacterized subset of interactions that are likely to affect glycolysis and deserve further study.

scholarly journals Artificial Septal Targeting of Bacillus subtilis Cell Division Proteins in Escherichia coli: an Interspecies Approach to the Study of Protein-Protein Interactions in Multiprotein Complexes

2008 ◽  
Vol 190 (18) ◽  
pp. 6048-6059 ◽  
Author(s):  
Carine Robichon ◽  
Glenn F. King ◽  
Nathan W. Goehring ◽  
Jon Beckwith
Keyword(s):  
Escherichia Coli ◽  
Bacillus Subtilis ◽  
Cell Division ◽  
Protein Interactions ◽  
Fluorescent Protein ◽  
Bacterial Cell Division ◽  
Protein Protein Interactions ◽  
Positive Interaction ◽  
E Coli ◽  
Multiprotein Complexes

ABSTRACT Bacterial cell division is mediated by a set of proteins that assemble to form a large multiprotein complex called the divisome. Recent studies in Bacillus subtilis and Escherichia coli indicate that cell division proteins are involved in multiple cooperative binding interactions, thus presenting a technical challenge to the analysis of these interactions. We report here the use of an E. coli artificial septal targeting system for examining the interactions between the B. subtilis cell division proteins DivIB, FtsL, DivIC, and PBP 2B. This technique involves the fusion of one of the proteins (the “bait”) to ZapA, an E. coli protein targeted to mid-cell, and the fusion of a second potentially interacting partner (the “prey”) to green fluorescent protein (GFP). A positive interaction between two test proteins in E. coli leads to septal localization of the GFP fusion construct, which can be detected by fluorescence microscopy. Using this system, we present evidence for two sets of strong protein-protein interactions between B. subtilis divisomal proteins in E. coli, namely, DivIC with FtsL and DivIB with PBP 2B, that are independent of other B. subtilis cell division proteins and that do not disturb the cytokinesis process in the host cell. Our studies based on the coexpression of three or four of these B. subtilis cell division proteins suggest that interactions among these four proteins are not strong enough to allow the formation of a stable four-protein complex in E. coli in contrast to previous suggestions. Finally, our results demonstrate that E. coli artificial septal targeting is an efficient and alternative approach for detecting and characterizing stable protein-protein interactions within multiprotein complexes from other microorganisms. A salient feature of our approach is that it probably only detects the strongest interactions, thus giving an indication of whether some interactions suggested by other techniques may either be considerably weaker or due to false positives.

scholarly journals Surface plasmon resonance: a useful technique for cell biologists to characterize biomolecular interactions

2013 ◽  
Vol 24 (7) ◽  
pp. 883-886 ◽  
Author(s):  
Robert V. Stahelin
Keyword(s):  
Surface Plasmon Resonance ◽  
Surface Plasmon ◽  
Protein Interactions ◽  
Plasmon Resonance ◽  
Dissociation Rate ◽  
Biomolecular Interactions ◽  
Protein Protein Interactions ◽  
Interaction Kinetics ◽  
Lipid Protein Interactions ◽  
Dissociation Rate Constants

Surface plasmon resonance (SPR) is a powerful technique for monitoring the affinity and selectivity of biomolecular interactions. SPR allows for analysis of association and dissociation rate constants and modeling of biomolecular interaction kinetics, as well as for equilibrium binding analysis and ligand specificity studies. SPR has received much use and improved precision in classifying protein–protein interactions, as well as in studying small-molecule ligand binding to receptors; however, lipid–protein interactions have been underserved in this regard. With the field of lipids perhaps the next frontier in cellular research, SPR is a highly advantageous technique for cell biologists, as newly identified proteins that associate with cellular membranes can be screened rapidly and robustly for lipid specificity and membrane affinity. This technical perspective discusses the conditions needed to achieve success with lipid–protein interactions and highlights the unique lipid–protein interaction mechanisms that have been elucidated using SPR. It is intended to provide the reader a framework for quantitative and confident conclusions from SPR analysis of lipid–protein interactions.

Site-specific recombination by φC31 integrase and other large serine recombinases

2010 ◽  
Vol 38 (2) ◽  
pp. 388-394 ◽  
Author(s):  
Margaret C.M. Smith ◽  
William R.A. Brown ◽  
Andrew R. McEwan ◽  
Paul A. Rowley
Keyword(s):  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Serine Recombinases ◽  
Tyrosine Recombinases ◽  
Dna Binding Sites ◽  
Attachment Sites ◽  
Φc31 Integrase ◽  
Related Proteins ◽  
Control Protein

Most temperate phages encode an integrase for integration and excision of the prophage. Integrases belong either to the λ Int family of tyrosine recombinases or to a subgroup of the serine recombinases, the large serine recombinases. Integration by purified serine integrases occurs efficiently in vitro in the presence of their cognate (~50 bp) phage and host attachment sites, attP and attB respectively. Serine integrases require an accessory protein, Xis, to promote excision, a reaction in which the products of the integration reaction, attL and attR, recombine to regenerate attP and attB. Unlike other directional recombinases, serine integrases are not controlled by proteins occupying accessory DNA-binding sites. Instead, it is thought that different integrase conformations, induced by binding to the DNA substrates, control protein–protein interactions, which in turn determine whether recombination proceeds. The present review brings together the evidence for this model derived from the studies on φC31 integrase, Bxb1 integrase and other related proteins.

scholarly journals FimW Is a Negative Regulator Affecting Type 1 Fimbrial Expression in Salmonella enterica Serovar Typhimurium

2001 ◽  
Vol 183 (2) ◽  
pp. 435-442 ◽  
Author(s):  
Juliette K. Tinker ◽  
Lisa S. Hancox ◽  
Steven Clegg
Keyword(s):  
Dna Binding ◽  
Protein Interactions ◽  
Parental Strain ◽  
Salmonella Enterica ◽  
Specific Binding ◽  
Negative Regulator ◽  
E Coli ◽  
Serovar Typhimurium

ABSTRACT Type 1 fimbriae are proteinaceous surface appendages that carry adhesins specific for mannosylated glycoproteins. These fimbriae are found on most members of the family Enterobacteriaceae and are known to facilitate binding to a variety of eukaryotic cells, including those found on the mucosal surfaces of the alimentary tract. We have shown that the regulation of type 1 fimbrial expression in Salmonella enterica serovar Typhimurium is controlled, in part, by the products of four genes found within the fimgene cluster: fimZ, fimY, fimW, andfimU. To better understand the specific role of FimW in fimbrial expression, a mutation was constructed in this gene by the insertion of a kanamycin resistance DNA cassette into the chromosome. The resulting fimW mutation was characterized by mannose-sensitive hemagglutination and agglutination with fimbria-specific antiserum. Assays suggested that this mutant was more strongly fimbriate than the parental strain, exhibiting a four- to eightfold increase in fimbrial production. The fimWmutation was introduced into a second strain of Salmonella enterica serovar Typhimurium, and this mutant was also found to be strongly fimbriate compared to the parental strain. Consistent with the role of this protein as a negative regulator, fimA-lacZexpression in serovar Typhimurium, as well as in Escherichia coli, was increased twofold in the absence of functional FimW. Primer extension analysis determined that fimWtranscription is initiated from its own promoter 31 bp upstream of the translation start site. Analysis using a fimW-lacZ reporter indicated that fimW expression in serovar Typhimurium was increased under conditions that select for poorly fimbriate bacteria and low fimA expression. FimW also appears to act as an autoregulator, since expression from the fimW-lacZ reporter was increased in a fimW mutant. FimW was partially purified by fusion with the E. coli maltose-binding protein. Use of this FimW protein extract, as well as others, in DNA-binding assays was unable to identify a specific binding site for FimW in thefimA, fimZ, fimY, orfimW promoter regions. To analyze protein-protein interactions, FimW was expressed in a LexA-based two-hybrid system inE. coli. A significant interaction between FimW and the DNA-binding activator protein, FimZ, was detected using this system. These results indicate that FimW is a negative regulator of serovar Typhimurium type 1 fimbrial expression and may function by interfering with FimZ-mediated activation of fimA expression.

scholarly journals Hidden partners: Using cross-docking calculations to predict binding sites for proteins with multiple interactions

2018 ◽  
Author(s):  
Nathalie Lagarde ◽  
Alessandra Carbone ◽  
Sophie Sacquin-Mora
Keyword(s):  
Nucleic Acid ◽  
Binding Site ◽  
Protein Interactions ◽  
Binding Sites ◽  
Protein Protein Interactions ◽  
Protein Binding Sites ◽  
Cross Docking ◽  
Multiple Binding Sites ◽  
Multiple Binding ◽  
Docking Calculations

AbstractProtein-protein interactions control a large range of biological processes and their identification is essential to understand the underlying biological mechanisms. To complement experimental approaches, in silico methods are available to investigate protein-protein interactions. Cross-docking methods, in particular, can be used to predict protein binding sites. However, proteins can interact with numerous partners and can present multiple binding sites on their surface, which may alter the binding site prediction quality. We evaluate the binding site predictions obtained using complete cross-docking simulations of 358 proteins with two different scoring schemes accounting for multiple binding sites. Despite overall good binding site prediction performances, 68 cases were still associated with very low prediction quality, presenting individual area under the specificity-sensitivity ROC curve (AUC) values below the random AUC threshold of 0.5, since cross-docking calculations can lead to the identification of alternate protein binding sites (that are different from the reference experimental sites). For the large majority of these proteins, we show that the predicted alternate binding sites correspond to interaction sites with hidden partners, i.e. partners not included in the original cross-docking dataset. Among those new partners, we find proteins, but also nucleic acid molecules. Finally, for proteins with multiple binding sites on their surface, we investigated the structural determinants associated with the binding sites the most targeted by the docking partners.AbbreviationsANOVA: ANalysis Of Variance; AUC: Area Under the Curve; Best Interface: BI; CAPRI: Critical Assessment of Prediction of Interactions; CC-D: Complete Cross-Docking; DNA: DesoxyriboNucleic Acid; FDR: False Discovery Rate; FRIres(type): Fraction of each Residue type in the Interface; FP: False Positives; GI: Global Interface; HCMD: Help Cure Muscular Dystrophy; JET: Joint Evolutionary Tree; MAXDo: Molecular Association via Cross Docking; NAI: Nucleic Acid Interface; NPV: Negative Predicted Value; PDB: Protein Data Bank; PIP: Protein Interface Propensity; PiQSi: Protein Quaternary Structure investigation; PPIs: Protein-Protein Interactions; PPV: Positive Predicted Value; Prec.: Precision; PrimI: Primary Interface; RNA: RiboNucleic Acid; ROC: Receiver Operating Characteristic; SecI: Secondary Interface; Sen.: Sensitivity; Spe.: Specificity; TN: True Negatives; TP: True Positives; WCG: World Community Grid.

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