Respiratory chain is required to maintain oxidized states of the DsbA-DsbB disulfide bond formation system in aerobically growing Escherichia coli cells

DsbA, the disulfide bond catalyst of Escherichia coli, is a periplasmic protein having a thioredoxin-like Cys-30-Xaa-Xaa-Cys-33 motif. The Cys-30–Cys-33 disulfide is donated to a pair of cysteines on the target proteins. Although DsbA, having high oxidizing potential, is prone to reduction, it is maintained essentially all oxidized in vivo. DsbB, an integral membrane protein having two pairs of essential cysteines, reoxidizes DsbA that has been reduced upon functioning. It is not known, however, what might provide the overall oxidizing power to the DsbA–DsbB disulfide bond formation system. We now report that E. coli mutants defective in the hemA gene or in the ubiA-menA genes markedly accumulate the reduced form of DsbA during growth under the conditions of protoheme deprivation as well as ubiquinone/menaquinone deprivation. Disulfide bond formation of β-lactamase was impaired under these conditions. Intracellular state of DsbB was found to be affected by deprivation of quinones, such that it accumulates first as a reduced form and then as a form of a disulfide-linked complex with DsbA. This is followed by reduction of the bulk of DsbA molecules. These results suggest that the respiratory electron transfer chain participates in the oxidation of DsbA, by acting primarily on DsbB. It is remarkable that a cellular catalyst of protein folding is connected to the respiratory chain.

Structural invariance of constitutively active and inactive mutants of Acanthamoeba myosin IC bound to F-actin in the rigor and ADP-bound states

The three single-headed monomeric myosin I isozymes of Acanthamoeba castellanii (AMIs)—AMIA, AMIB, and AMIC—are among the best-studied of all myosins. We have used AMIC to study structural correlates of myosin’s actin-activated ATPase. This activity is normally controlled by phosphorylation of Ser-329, but AMIC may be switched into constitutively active or inactive states by substituting this residue with Glu or Ala, respectively. To determine whether activation status is reflected in structural differences in the mode of attachment of myosin to actin, these mutant myosins were bound to actin filaments in the absence of nucleotide (rigor state) and visualized at 24-Å resolution by using cryoelectron microscopy and image reconstruction. No such difference was observed. Consequently, we suggest that regulation may be affected not by altering the static (time-averaged) structure of AMIC but by modulating its dynamic properties, i.e., molecular breathing. The tail domain of vertebrate intestinal brush-border myosin I has been observed to swing through 31° on binding of ADP. However, it was predicted on grounds of differing kinetics that any such effects with AMIC should be small [Jontes, J. D., Ostap, E. M., Pollard, T. D. & Milligan, R. A. (1998) J. Cell Biol. 141, 155–162]. We have confirmed this hypothesis by observing actin-associated AMIC in its ADP-bound state. Finally, we compared AMIC to brush-border myosin I and AMIB, which were previously studied under similar conditions. In each case, the shape and angle of attachment to F-actin of the catalytic domain is largely conserved, but the domain structure and disposition of the tail is distinctively different for each myosin.

Top-down free-energy minimization on protein potential energy landscapes

The hierarchical properties of potential energy landscapes have been used to gain insight into thermodynamic and kinetic properties of protein ensembles. It also may be possible to use them to direct computational searches for thermodynamically stable macroscopic states, i.e., computational protein folding. To this end, we have developed a top-down search procedure in which conformation space is recursively dissected according to the intrinsic hierarchical structure of a landscape's effective-energy barriers. This procedure generates an inverted tree similar to the disconnectivity graphs generated by local minima-clustering methods, but it fundamentally differs in the manner in which the portion of the tree that is to be computationally explored is selected. A key ingredient is a branch-selection algorithm that takes advantage of statistically predictive properties of the landscape to guide searches down the tree branches that are most likely to lead to the physically relevant macroscopic states. Using the computational folding of a β-hairpin-forming peptide as an example, we show that such predictive properties indeed exist and can be used for structure prediction by free-energy global minimization.

How optimization of potential functions affects protein folding.

The relationship between the optimization of the potential function and the foldability of theoretical protein models is studied based on investigations of a 27-mer cubic-lattice protein model and a more realistic lattice model for the protein crambin. In both the simple and the more complicated systems, optimization of the energy parameters achieves significant improvements in the statistical-mechanical characteristics of the systems and leads to foldable protein models in simulation experiments. The foldability of the protein models is characterized by their statistical-mechanical properties--e.g., by the density of states and by Monte Carlo folding simulations of the models. With optimized energy parameters, a high level of consistency exists among different interactions in the native structures of the protein models, as revealed by a correlation function between the optimized energy parameters and the native structure of the model proteins. The results of this work are relevant to the design of a general potential function for folding proteins by theoretical simulations.

Neural-network-based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain.

Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.