Predictions of stress jump by a transient network model

A model proposed by De Kee and Carreau predicts a stress jump and we compare the model predictions with the experimental data of Liang and Mackay. The values of the model parameters are determined from the steady viscosity data. Using the same values of the parameters, the stress jump as a function of the shear rate is calculated. The agreement between the calculated and experimental values of the stress jump is excellent.     

Identification of key residues for protein conformational transition using elastic network model

Proteins usually undergo conformational transitions between structurally disparate states to fulfill their functions. The large-scale allosteric conformational transitions are believed to involve some key residues that mediate the conformational movements between different regions of the protein. In the present work, a thermodynamic method based on the elastic network model is proposed to predict the key residues involved in protein conformational transitions. In our method, the key functional sites are identified as the residues whose perturbations largely influence the free energy difference between the protein states before and after transition. Two proteins, nucleotide binding domain of the heat shock protein 70 and human/rat DNA polymerase β, are used as case studies to identify the critical residues responsible for their open-closed conformational transitions. The results show that the functionally important residues mainly locate at the following regions for these two proteins: (1) the bridging point at the interface between the subdomains that control the opening and closure of the binding cleft; (2) the hinge region between different subdomains, which mediates the cooperative motions between the corresponding subdomains; and (3) the substrate binding sites. The similarity in the positions of the key residues for these two proteins may indicate a common mechanism in their conformational transitions.     

Identifying essential pairwise interactions in elastic network model using the alpha shape theory

Elastic network models (ENM) are based on the idea that the geometry of a protein structure provides enough information for computing its fluctuations around its equilibrium conformation. This geometry is represented as an elastic network (EN) that is, a network of links between residues. A spring is associated with each of these links. The normal modes of the protein are then identified with the normal modes of the corresponding network of springs. Standard approaches for generating ENs rely on a cutoff distance. There is no consensus on how to choose this cutoff. In this work, we propose instead to filter the set of all residue pairs in a protein using the concept of alpha shapes. The main alpha shape we considered is based on the Delaunay triangulation of the Cα positions; we referred to the corresponding EN as EN(∞). We have shown that heterogeneous anisotropic network models, called αHANMs, that are based on EN(∞) reproduce experimental B‐factors very well, with correlation coefficients above 0.99 and root‐mean‐square deviations below 0.1 Ų for a large set of high resolution protein structures. The construction of EN(∞) is simple to implement and may be used automatically for generating ENs for all types of ENMs. © 2014 Wiley Periodicals, Inc.     

Approaches to identify, clone, and express symbiont bioactive metabolite genes

This review discusses approaches to identify, clone, and express bioactive metabolite genes from symbionts of marine invertebrates. Criteria for proving symbiotic origin of bioactive metabolites are presented, followed by a comprehensive, practically-oriented overview of techniques to be applied. The Bugula neritina/Endobugula sertula association is used as a primary example, but other symbioses are discussed. Thirty-six compounds are presented and 111 references are cited.     

Deep neural network analysis of nanoparticle ordering to identify defects in layered carbon materials

Smoothness/defectiveness of the carbon material surface is a key issue for many applications, spanning from electronics to reinforced materials, adsorbents and catalysis. Several surface defects cannot be observed with conventional analytic techniques, thus requiring the development of a new imaging approach. Here, we evaluate a convenient method for mapping such “hidden” defects on the surface of carbon materials using 1–5 nm metal nanoparticles as markers. A direct relationship between the presence of defects and the ordering of nanoparticles was studied experimentally and modeled using quantum chemistry calculations and Monte Carlo simulations. An automated pipeline for analyzing microscopic images is described: the degree of smoothness of experimental images was determined by a classification neural network, and then the images were searched for specific types of defects using a segmentation neural network. An informative set of features was generated from both networks: high-dimensional embeddings of image patches and statics of defect distribution.

Defectiveness of carbon material surface is a key issue for many applications. Pd-nanoparticle SEM imaging was used to highlight “hidden” defects and analyzed by neural networks to solve order/disorder classification and defect segmentation tasks.     

Investigations of hard elastic polypropylene with respect to the coil-strand-transition model

Hard elastic polypropylene was investigated by x-ray scattering using synchrotron radiation and by performing stress strain measurements in different liquids. It was found that the long period increases with elongation up to 1.3–1.4. Beyond this extension a change in the deformation behavior takes place. In the range from 1.1 to 1.3–1.4 the deformation measurements in the different liquids are in good agreement with the coil-strand-transition model. The proposed linear relationship between the deformation work and the elongation could be confirmed. The same holds for the surface energy. Moreover, the calculated ratio between the surface energy and the deformation work agrees with the measurements.     

Applying forces to elastic network models of large biomolecules using a haptic feedback device

Elastic network models of biomolecules have proved to be relatively good at predicting global conformational changes particularly in large systems. Software that facilitates rapid and intuitive exploration of conformational change in elastic network models of large biomolecules in response to externally applied forces would therefore be of considerable use, particularly if the forces mimic those that arise in the interaction with a functional ligand. We have developed software that enables a user to apply forces to individual atoms of an elastic network model of a biomolecule through a haptic feedback device or a mouse. With a haptic feedback device the user feels the response to the applied force whilst seeing the biomolecule deform on the screen. Prior to the interactive session normal mode analysis is performed, or pre-calculated normal mode eigenvalues and eigenvectors are loaded. For large molecules this allows the memory and number of calculations to be reduced by employing the idea of the important subspace, a relatively small space of the first M lowest frequency normal mode eigenvectors within which a large proportion of the total fluctuation occurs. Using this approach it was possible to study GroEL on a standard PC as even though only 2.3% of the total number of eigenvectors could be used, they accounted for 50% of the total fluctuation. User testing has shown that the haptic version allows for much more rapid and intuitive exploration of the molecule than the mouse version.     

Multi-scale top-down approach for modelling epileptic protein-protein interaction network analysis to identify driver nodes and pathways

Protein - Protein Interaction Network (PPIN) analysis unveils molecular level mechanisms involved in disease condition. To explore the complex regulatory mechanisms behind epilepsy and to address the clinical and biological issues of epilepsy, in silico techniques are feasible in a cost- effective manner. In this work, a hierarchical procedure to identify influential genes and regulatory pathways in epilepsy prognosis is proposed. To obtain key genes and pathways causing epilepsy, integration of two benchmarked datasets which are exclusively devoted for complex disorders is done as an initial step. Using STRING database, PPIN is constructed for modelling protein-protein interactions. Further, key interactions are obtained from the established PPIN using network centrality measures followed by network propagation algorithm -Random Walk with Restart (RWR). The outcome of the method reveals some influential genes behind epilepsy prognosis, along with their associated pathways like PI3 kinase, VEGF signaling, Ras, Wnt signaling etc. In comparison with similar works, our results have shown improvement in identifying unique molecular functions, biological processes, gene co-occurrences etc. Also, CORUM provides an annotation for approximately 60% of similarity in human protein complexes with the obtained result. We believe that the formulated strategy can put-up the vast consideration of indigenous drugs towards meticulous identification of genes encoded by protein against several combinatorial disorders.     

Cell integrity signaling and response to stress in fission yeast

Cellular responses to external signals are regulated by conserved mitogen-activated protein (MAP) kinase signaling cascades. These pathways are triggered by a vast range of stimuli. They phosphorylate numerous proteins, produce significant changes in the gene expression, and regulate diverse processes ranging from proliferation and differentiation to apoptosis in all eukaryotic cells. Three conserved MAP kinase signaling pathways have been identified in the fission yeast Schizosaccharomyces pombe. In this article, we present an overview of two of those pathways that regulate the response of fission yeast to stress and maintain cell integrity. The structure of these signaling modules and the function of the pathways, including the regulation by endogenous inhibitors, are discussed.     

Effect of frequency on insulin response to electric field stress

There are many unanswered questions regarding the precise way in which proteins respond to external stress. Since the function of proteins is critically linked to their three-dimensional structures, exposure to any form of stress which may induce changes in conformation can potentially initiate severe cellular dysfunction. This is particularly relevant with regard to the increasing presence of electromagnetic devices in today's environment and the possible effects on human health. Previously, we investigated the effect of electric field of various strengths on insulin chain-B under static and oscillating conditions. This paper expands on our previous work by subjecting the peptide to an oscillating electric field of different frequencies. We observed a frequency-dependent effect where the application of lower-frequency oscillating fields resulted in static-field-like behavior of the peptide, whereby the intrinsic flexibility of the protein is constrained, thus potentially restricting access to the protein's active state.     

HUMANN-based system to identify benzimidazole fungicides using multi-synchronous fluorescence spectra: An ensemble approach

In this paper, we approach, using neural computation and ensemble systems, a pattern classification problem in fluorescence spectrometry, the resolution of difficult multi-fungicide mixtures (overlapping), specifically the benzimidazole fungicides, benomyl, carbendazim, thiabendazole and fuberidazole. These fungicides are compounds of an important environmental interest. Because of this, from an analytical point of view, it is interesting to develop sensitive, selective and simple methods for their determination. Fluorescence spectrometry has proven to be a sensitive and selective technique for determination of many compounds of environmental interest, but in some cases it is not enough. HUMANN is a hierarchical, unsupervised, modular, adaptive neural net with high biological plausibility, which has shown to be suitable for identification of these fungicides and organochlorinated compounds of environmental interest. We propose two modular artificial intelligent systems, with a structure of pre-processing and processing stage, a multi-input HUMANN-based system, using multi-fluorescence spectra as input to the system, and a HUMANN-ensemble system. We analyze the optimal configuration of inputs and the ensemble in order to obtain better results. We study such figures as precision and sensitivity of the method. Our proposal is a smart, flexible and effective complementary method, which allows reducing the analytical and/or computational complexity of the analysis. Figure Stages in identification of benzimidazole fungicides Based on a contribution presented at the XIII International Symposium on Luminescence Spectrometry held in Bologna, Italy from Sept. 7–11, 2008.     

An optrode particle geometry to decrease response time

A hollow 3 μm sensing microcapsule containing chromoionophores within a 100 nm organosilica shell is reported. This shows a response to an ion step two orders of magnitude faster than 'filled' sensing particles of similar diameter. Incorporation of chromoionophores ETH 5294 and ETH 7061 in the capsule shell is shown with a t(90) response <2 s compared with >15 min for filled particles of similar diameters. This ultrafast geometry is also extended to dual ionophore dye incorporation and preliminary exploration of a FRET-based ratiometric method is examined to extend the pH response range, using a single excitation wavelength.     

Adhesive transition from noncontacting to contacting elastic spheres: extension of the Maugis-Dugdale model

In this paper, an adhesion model for spherical noncontact is proposed based on the Maugis-Dugdale (MD) adhesive contact model. The proposed noncontact model is combined with the MD contact model, thus providing a full range adhesion model with analytical transition from noncontacting to contacting asperity geometry. The proposed model is favorably compared with the full range improved DMT model for low surface energy values. The transition process from noncontact to contact and the adhesion instability that occurs during this transition are also investigated. It is found that jump-off points are different for displacement control and force control. Moreover, under displacement control, jump-on and jump-off points are different when the adhesion parameter lambda is greater than 0.95, whereas they are identical for lambda<0.95. By curve fitting a relationship between the critical approach under displacement and force control separately and the adhesion parameter lambda, approach prediction equations for jump-on and jump-off under different adhesion levels were obtained.     

Analysis of the bacterial heat shock response to photodynamic therapy-mediated oxidative stress

Antimicrobial photodynamic therapy (PDT) has recently emerged as an effective modality for the selective destruction of bacteria and other pathogenic microorganisms. We investigated whether PDT induced protective responses such as heat shock proteins (HSPs) in bacteria. Using the photosensitizer Toluidine Blue O (TBO) at sublethal PDT conditions, a seven-fold increase in bacterial HSP GroEL and a three-fold increase in HSP DnaK were observed in Escherichia coli post PDT. Pretreatment with 50°C heat for 30 min reduced PDT killing in both E. coli and in Enterococcus faecalis, with the most pronounced inhibition occurring at 50 μm TBO with 5 J cm(-2) 635 nm light, where E. coli killing was reduced by 2 log(10) and E. faecalis killing was reduced by 4 log(10). Finally, inhibition of the highly conserved chaperone DnaK using a small molecule benzylidene lactam HSP inhibitor potentiated (but not significantly) the effect of PDT at a TBO concentration of 2.5 μm in E. faecalis; however, this effect was not observed in E. coli presumably because inhibitor could not gain access due to Gram-negative permeability barrier. Induction of HSPs may be a mechanism whereby bacteria could become resistant to PDT and warrants the need for further study in the application of dual PDT-HSP-inhibition therapies.     

Use of spatiotemporal response information from sorption-based sensor arrays to identify and quantify the composition of analyte mixtures

Linear sensor arrays made from small molecule/carbon black composite chemiresistors placed in a low-headspace volume chamber, with vapor delivered at low flow rates, allowed for the extraction of new chemical information that significantly increased the ability of the sensor arrays to identify vapor mixture components and to quantify their concentrations. Each sensor sorbed vapors from the gas stream and, thereby, as in gas chromatography, separated species having high vapor pressures from species having low vapor pressures. Instead of producing only equilibrium-based sensor responses that were representative of the thermodynamic equilibrium partitioning of analyte between each sensor and the initial vapor phase, the sensor responses varied depending on the position of the sensor in the chamber and the time since the beginning of the analyte exposure. The concomitant spatiotemporal (ST) sensor array response therefore provided information that was a function of time, as well as of the position of the sensor in the chamber. The responses to pure analytes and to multicomponent analyte mixtures comprised of hexane, decane, ethyl acetate, chlorobenzene, ethanol, and/or butanol were recorded along each of the sensor arrays. Use of a non-negative least-squares (NNLS) method for analysis of the ST data enabled the correct identification and quantification of the composition of two-, three-, four-, and five-component mixtures from arrays using only four chemically different sorbent films. In contrast, when traditional time- and position-independent sensor response information was used, these same mixtures could not be identified or quantified robustly. The work has also demonstrated that, for ST data, NNLS yielded significantly better results than analyses using extended disjoint principal components modeling. The ability to correctly identify and quantify constituent components of vapor mixtures through the use of such ST information significantly expands the capabilities of such broadly cross-reactive arrays of sensors.