Detecting cis-regulatory binding sites for cooperatively binding proteins

Several methods are available to predict cis-regulatory modules in DNA based on position weight matrices. However, the performance of these methods generally depends on a number of additional parameters that cannot be derived from sequences and are difficult to estimate because they have no physical meaning. As the best way to detect cis-regulatory modules is the way in which the proteins recognize them, we developed a new scoring method that utilizes the underlying physical binding model. This method requires no additional parameter to account for multiple binding sites; and the only necessary parameters to model homotypic cooperative interactions are the distances between adjacent protein binding sites in basepairs, and the corresponding cooperative binding constants. The heterotypic cooperative binding model requires one more parameter per cooperatively binding protein, which is the concentration multiplied by the partition function of this protein. In a case study on the bacterial ferric uptake regulator, we show that our scoring method for homotypic cooperatively binding proteins significantly outperforms other PWM-based methods where biophysical cooperativity is not taken into account.     

Using binding profiles to predict binding sites of target RNAs

Prediction of RNA-RNA interaction is a key to elucidating possible functions of small non-coding RNAs, and a number of computational methods have been proposed to analyze interacting RNA secondary structures. In this article, we focus on predicting binding sites of target RNAs that are expected to interact with regulatory antisense RNAs in a general form of interaction. For this purpose, we propose bistaRNA, a novel method for predicting multiple binding sites of target RNAs. bistaRNA employs binding profiles that represent scores for hybridized structures, leading to reducing the computational cost for interaction prediction. bistaRNA considers an ensemble of equilibrium interacting structures and seeks to maximize expected accuracy using dynamic programming. Experimental results on real interaction data validate good accuracy and fast computation time of bistaRNA as compared with several competitive methods. Moreover, we aim to find new targets given specific antisense RNAs, which provides interesting insights into antisense RNA regulation. bistaRNA is implemented in C++. The program and Supplementary Material are available at http://rna.naist.jp/program/bistarna/.     

Glucocorticoid receptor binds cooperatively to adjacent recognition sites.

In order to define the mechanism of synergistic induction mediated by multiple glucocorticoid response elements (GRE), the affinity of the glucocorticoid receptor to a single or duplicated GRE was analyzed by gel retardation, nitrocellulose filter binding and by footprinting experiments. Direct measurement of the relative affinity and indirect determination by competition showed greater than 10-fold higher affinity of the glucocorticoid receptor to a duplicated GRE when compared to a single element. Maximal stability of the GRE-receptor complex was obtained using two closely spaced GREs positioned on the same side of the DNA helix. Increasing the distance or changing the helical position of the GREs considerably increased the off rate of the receptor. DNase I footprinting shows in addition to the protection of the GRE region, an altered pattern in the nonprotected intervening DNA indicating structural alteration of the DNA helix by the receptor bound to adjacent GREs.     

RNANetMotif: Identifying sequence-structure RNA network motifs in RNA-protein binding sites

RNA molecules can adopt stable secondary and tertiary structures, which are essential in mediating physical interactions with other partners such as RNA binding proteins (RBPs) and in carrying out their cellular functions. In vivo and in vitro experiments such as RNAcompete and eCLIP have revealed in vitro binding preferences of RBPs to RNA oligomers and in vivo binding sites in cells. Analysis of these binding data showed that the structure properties of the RNAs in these binding sites are important determinants of the binding events; however, it has been a challenge to incorporate the structure information into an interpretable model. Here we describe a new approach, RNANetMotif, which takes predicted secondary structure of thousands of RNA sequences bound by an RBP as input and uses a graph theory approach to recognize enriched subgraphs. These enriched subgraphs are in essence shared sequence-structure elements that are important in RBP-RNA binding. To validate our approach, we performed RNA structure modeling via coarse-grained molecular dynamics folding simulations for selected 4 RBPs, and RNA-protein docking for LIN28B. The simulation results, e.g., solvent accessibility and energetics, further support the biological relevance of the discovered network subgraphs.     

Determining the DNA sequence elements required for binding integration host factor to two different target sites.

Binding sites for the Escherichia coli protein integration host factor (IHF) include a set of conserved bases that can be summarized by the consensus sequence WATCAANNNNTTR (W is dA or dT, R is dA or dG, and N is any nucleotide). However, additional 5'-proximal bases, whose common feature is a high dA+dT content, are also thought to be required for binding at some sites. We examine the relative contribution of these two sequence elements to IHF binding to the H' and H1 sites in attP of bacteriophage lambda by using the bacteriophage P22-based challenge-phage system. IHF was unable to act as a repressor in the challenge-phage assay at H' sites containing the core consensus element but lacking the dA+dT-rich element. This indicates that both elements are required for IHF to bind to the H' site. In contrast, the core consensus determinant alone is sufficient for IHF binding to the H1 site, which lacks an upstream dA+dT-rich region. Fifty mutants that decreased or eliminated IHF binding to the H1 site were isolated. Sequence analysis showed changes in the bases in the core consensus element only, further indicating that this determinant is sufficient for IHF binding to the H1 site. We found that placement of a dA+dT-rich element upstream of the H1 core consensus element significantly increased the affinity, suggesting that the presence of a dA+dT-rich element enhances IHF binding.     

Evidence for two classes of 1,25-dihydroxyvitamin D3 binding sites in classical vs. nonclassical target tissues.

Possible differences in 1,25-Dihydroxyvitamin D3 [1,25(OH)2D3] binding sites in classical and nonclassical target tissues were tested by Scatchard analysis of [3H]1,25(OH)2D3 binding in parallel chromatin preparations of rat kidney vs. testis. Two distinct binding components were resolved in kidney (p less than 0.005). Moreover, the single binding site in testis exhibited a 10-fold lower Kd (p less than 0.05) than did the principal binding site in kidney (50 +/- 4 vs. 405 +/- 142 pM). Secondly, regulation of [3H]1,25(OH)2D3 binding sites also differed. 1,25(OH)2D3 injection resulted in increased 1,25(OH)2D3 binding (p less than 0.05) in kidney (92%) and intestine (415%), but not in testis, lung or heart. These results suggest that the principal 1,25(OH)2D3 binding sites in classical targets kidney and intestine may be intrinsically different from those in at least some nonclassical targets.     

The Tim8-Tim13 complex has multiple substrate binding sites and binds cooperatively to Tim23

The Tim8-Tim13 complex, located in the mitochondrial intermembrane space, functions in the TIM22 import pathway that mediates the import of the mitochondrial carriers Tim23, Tim22, and Tim17 into the mitochondrial inner membrane. The Tim8-Tim13 complex assembles as a hexamer and binds to the substrate Tim23 to chaperone the hydrophobic Tim23 across the aqueous intermembrane space. However, both structural features of the Tim8-Tim13 complex and the binding interaction to Tim23 remain poorly defined. The crystal structure of the yeast Tim8-Tim13 complex, reported here at 2.6 Å resolution, reveals that the architecture of the Tim8-Tim13 complex is similar to those of other chaperones such as Tim9-Tim10, prefoldin, and Skp, in which long helices extend from a central body like tentacles from a jellyfish. Surface plasmon resonance was applied to investigate interactions between the Tim8-Tim13 complex and Tim23. The Tim8-Tim13 complex contained approximately six binding sites and showed a complex binding interaction indicative of positive cooperativity rather than a simple bimolecular interaction. By combining results from the structural and binding studies, we provide a molecular model of the Tim8-Tim13 complex binding to Tim23. The regions where the tentacle helices attach to the body of the Tim8-Tim13 complex contain six hydrophobic pockets that likely interact with specific sequences of Tim23 and possibly other substrates. Smaller hydrophobic patches on the tentacles themselves likely interact nonspecifically with the substrate's transmembrane helices, shielding it from the aqueous intermembrane space. The central region of Tim23, which enters the intermembrane space first, may serve to nucleate the binding of the Tim8-Tim13 complex, thereby initiating the chaperoned translocation of Tim23 to the mitochondrial inner membrane.     

Specific binding sites for cations in bacteriorhodopsin.

The Asp-85 residue, located in the vicinity of the retinal chromophore, plays a key role in the function of bacteriorhodopsin (bR) as a light-driven proton pump. In the unphotolyzed pigment the protonation of Asp-85 is responsible for the transition from the purple form (lambda(max) = 570 nm) to the blue form (lambda(max) = 605 nm) of bR. This transition can also be induced by deionization (cation removal). It was previously proposed that the cations bind to the bR surface and raise the surface pH, or bind to a specific site in the protein, probably in the retinal vicinity. We have reexamined these possibilities by evaluating the interaction between Mn(2+) and a nitroxyl radical probe covalently bound to several mutants in which protein residues were substituted by cystein. We have found that Mn(2+), which binds to the highest-affinity binding site, significantly affects the EPR spectrum of a spin label attached to residue 74C. Therefore, it is concluded that the highest-affinity binding site is located in the extracellular side of the protein and its distance from the spin label at 74C is estimated to be approximately 9.8 +/- 0.7 A. At least part of the three to four low-affinity cation binding sites are located in the cytoplasmic side, because Mn(2+) bound to these binding sites affects spin labels attached to residues 103C and 163C located in the cytoplasmic side of the protein. The results indicate specific binding sites for the color-controlling cations, and suggest that the binding sites involve negatively charged lipids located on the exterior of the bR trimer structure.     

Bacterial target sites for biocide action

Although biocides have been used for a century, the number of products containing biocides has recently increased dramatically with public awareness of hygiene issues. The antimicrobial efficacy of biocides is now well documented; however, there is still a lack of understanding of their antimicrobial mechanisms of action. There is a wide range of biocides showing different levels of antimicrobial activity. It is generally accepted that, in contrast to chemotherapeutic agents, biocides have multiple target sites within the microbial cell and the overall damage to these target sites results in the bactericidal effect. Information about the antimicrobial efficacy of a biocide (i.e. the eta-value) might give some useful indications about the overall mode of action of a biocide. Bacteriostatic effects, usually achieved by a lower concentration of a biocide, might correspond to a reversible activity on the cytoplasmic membrane and/or the impairment of enzymatic activity. The bacteriostatic mechanism(s) of action of a biocide is less documented and a primary (unique?) target site within the cell might be involved. Understanding the mechanism(s) of action of a biocide has become an important issue with the emergence of bacterial resistance to biocides and the suggestion that biocide and antibiotic resistance in bacteria might be linked. There is still a lack of understanding of the mode of action of biocides, especially when used at low concentrations (i.e. minimal inhibitory concentration (MIC) or sublethal). Although this information might not be required for highly reactive biocides (e.g. alkylating and oxidizing agents) and biocides used at high concentrations, the use of biocides as preservatives or in products at sublethal concentrations, in which a bacteriostatic rather than a bactericidal activity is achieved, is driving the need to better understand microbial target sites. Understanding the mechanisms of action of biocides serves several purposes: (i) it will help to design antimicrobial formulations with an improved antimicrobial efficacy and (ii) it will ensure the prevention of the emergence of microbial resistance.