Predicting disulfide connectivity patterns

Disulfide bonds play an important role in stabilizing protein structure and regulating protein function. Therefore, the ability to infer disulfide connectivity from protein sequences will be valuable in structural modeling and functional analysis. However, to predict disulfide connectivity directly from sequences presents a challenge to computational biologists due to the nonlocal nature of disulfide bonds, i.e., the close spatial proximity of the cysteine pair that forms the disulfide bond does not necessarily imply the short sequence separation of the cysteine residues. Recently, Chen and Hwang (Proteins 2005;61:507-512) treated this problem as a multiple class classification by defining each distinct disulfide pattern as a class. They used multiple support vector machines based on a variety of sequence features to predict the disulfide patterns. Their results compare favorably with those in the literature for a benchmark dataset sharing less than 30% sequence identity. However, since the number of disulfide patterns grows rapidly when the number of disulfide bonds increases, their method performs unsatisfactorily for the cases of large number of disulfide bonds. In this work, we propose a novel method to represent disulfide connectivity in terms of cysteine pairs, instead of disulfide patterns. Since the number of bonding states of the cysteine pairs is independent of that of disulfide bonds, the problem of class explosion is avoided. The bonding states of the cysteine pairs are predicted using the support vector machines together with the genetic algorithm optimization for feature selection. The complete disulfide patterns are then determined from the connectivity matrices that are constructed from the predicted bonding states of the cysteine pairs. Our approach outperforms the current approaches in the literature.     

Fast numerical methods for simulating large-scale integrate-and-fire neuronal networks

We discuss numerical methods for simulating large-scale, integrate-and-fire (I&F) neuronal networks. Important elements in our numerical methods are (i) a neurophysiologically inspired integrating factor which casts the solution as a numerically tractable integral equation, and allows us to obtain stable and accurate individual neuronal trajectories (i.e., voltage and conductance time-courses) even when the I&F neuronal equations are stiff, such as in strongly fluctuating, high-conductance states; (ii) an iterated process of spike-spike corrections within groups of strongly coupled neurons to account for spike-spike interactions within a single large numerical time-step; and (iii) a clustering procedure of firing events in the network to take advantage of localized architectures, such as spatial scales of strong local interactions, which are often present in large-scale computational models—for example, those of the primary visual cortex. (We note that the spike-spike corrections in our methods are more involved than the correction of single neuron spike-time via a polynomial interpolation as in the modified Runge-Kutta methods commonly used in simulations of I&F neuronal networks.) Our methods can evolve networks with relatively strong local interactions in an asymptotically optimal way such that each neuron fires approximately once in operations, where N is the number of neurons in the system. We note that quantifications used in computational modeling are often statistical, since measurements in a real experiment to characterize physiological systems are typically statistical, such as firing rate, interspike interval distributions, and spike-triggered voltage distributions. We emphasize that it takes much less computational effort to resolve statistical properties of certain I&F neuronal networks than to fully resolve trajectories of each and every neuron within the system. For networks operating in realistic dynamical regimes, such as strongly fluctuating, high-conductance states, our methods are designed to achieve statistical accuracy when very large time-steps are used. Moreover, our methods can also achieve trajectory-wise accuracy when small time-steps are used. Action Editor: Nicolas Brunel     

Macro-invertebrates in a dynamic river environment: analysis of time series from artificial substrates,using a ‘white box’ neural network modelling method

New statistical modelling methods, such as neural networks (NNs), allow us to take a step further in the understanding of complex relations in aquatic ecosystems. In this paper the results from the analysis of macro-invertebrate communities in a complex riverine environment are presented. We attempted to explain observed changes in species composition and abundance with neural network modelling methods and compared the results to linear regression. The NN method used is an improved form of the RF5 algorithm, developed to effectively discover numeric laws from data. RF5 uses Product Unit Networks (PUNs), which are in effect multivariate non-discrete power functions. The data set consisted of a 10-year time series of monthly samples of macro-invertebrates on artificial substrates in the rivers Rhine and Meuse in the Netherlands. During this period the invertebrate community has largely changed coinciding with the␣invasion of Ponto-Caspian crustaceans. We used physical–chemical data and data on the abundance of the invasive taxa Corophium curvispinum and Dikerogammarus villosis to explain the observed changes in the resident invertebrate community. The analyses showed temperature, abundance of invasive taxa and peak discharges as important factors. Comparison of the results from NN modelling to linear regression revealed that the factors temperature and abundance of Dikerogammarus villosis explained equally well in both cases. Only the neural network was able to use information on peak discharge and timing of the peak in the previous winter to improve model performances. Neural networks are known to yield excellent modelling results, a drawback however is their lack of transparency or their ‘black box’ character. The use of relatively easy interpretable (white box) PUNs allows us to investigate the extracted relations in more detail and can enhance our understanding of ecosystem functioning. Our results show that peak discharges might be an important factor structuring invertebrate communities in rivers and hint on the existence of interacting effects from invasive species and discharge peaks. They finally show the value of biological data sets that are collected over a long period and in a highly standardised way.     

Research on WNN Greenhouse Temperature Prediction Method Based on GA

Temperature in agricultural production has a direct impact on the growth of crops. The emergence of greenhouses has improved the impact of the original unpredictable changes in temperature, but the temperature modeling of greenhouses is still the main direction at present. Neural network modeling relies on sufficient actual data to model greenhouses, but there is a widening gap in the application of different neural networks. This paper proposes a greenhouse temperature prediction model based on wavelet neural network with genetic algorithm (GA-WNN). With the simple network structure and the nonlinear adaptability of the wavelet basis function, wavelet neural network (WNN) improved model training speed and accuracy of prediction results compared with back propagation neural networks (BPNN), which was conducive to the prediction and control of short-term greenhouse temperature fluctuations. At the same time, the genetic algorithm (GA) was introduced to globally optimize the initial weights of the original model, which improved the insensitivity of the model to the initial weights and thresholds, and improved the training speed and stability of the model. Finally, simulation results for the greenhouse showed that the model training speed, prediction results accuracy and model stability of the GA-WNN in the greenhouse were improved in comparison to results obtained by the WNN and BPNN in the greenhouse.     

Structural and thermal characterization of hemicelluloses isolated by organic solvents and alkaline solutions from Tamarix austromongolica

Three organosolv and three alkaline hemicellulosic fractions were prepared from lignocellulosic biomass of the fast-growing shrub Tamarix austromongolica (Tamarix Linn.). Sugar analysis revealed that the organosolv-soluble fractions contained a higher content of glucose (33.7-6.5%) and arabinose (14.8-5.6%), and a lower content of xylose (62.2-54.8%) than the hemicellulosic fractions isolated with aqueous alkali solutions. A relatively high concentration of alkali resulted in a decreasing trend of the xylose/4-O-methyl-d-glucuronic acid ratio in the alkali-soluble fractions. The results of NMR analysis supported a major substituted structure based on a linear polymer of β-(1 → 4)-linked d-xylopyranosyl residues, having ramifications of α-l-arabinofuranose and 4-O-methyl-d-glucuronic acid residues monosubstituted at O-3 and O-2, respectively. Thermogravimetric analysis revealed that one step of major mass loss occurred between 200-400 °C, as hemicelluloses devolatilized with total volatile yield of about 55%. It was found that organosolv-soluble fractions are more highly ramified, and showed a higher thermal stability than the alkali-soluble fractions.     

EGID: an ensemble algorithm for improved genomic island detection in genomic sequences

Genomic islands (GIs) are genomic regions that are originally transferred from other organisms. The detection of genomic islands in genomes can lead to many applications in industrial, medical and environmental contexts. Existing computational tools for GI detection suffer either low recall or low precision, thus leaving the room for improvement. In this paper, we report the development of our Ensemble algorithm for Genomic Island Detection (EGID). EGID utilizes the prediction results of existing computational tools, filters and generates consensus prediction results. Performance comparisons between our ensemble algorithm and existing programs have shown that our ensemble algorithm is better than any other program. EGID was implemented in Java, and was compiled and executed on Linux operating systems. EGID is freely available at http://www5.esu.edu/cpsc/bioinfo/software/EGID.     

Exploiting powder X-ray diffraction for direct structure determination in structural biology: the P2X4 receptor trafficking motif YEQGL

We report the crystal structure of the 5-residue peptide acetyl-YEQGL-amide, determined directly from powder X-ray diffraction data recorded on a conventional laboratory X-ray powder diffractometer. The YEQGL motif has a known biological role, as a trafficking motif in the C-terminus of mammalian P2X4 receptors. Comparison of the crystal structure of acetyl-YEQGL-amide determined here and that of a complex formed with the μ2 subunit of the clathrin adaptor protein complex AP2 reported previously, reveals differences in conformational properties, although there are nevertheless similarities concerning aspects of the hydrogen-bonding arrangement and the hydrophobic environment of the leucine sidechain. Our results demonstrate the potential for exploiting modern powder X-ray diffraction methodology to achieve complete structure determination of materials of biological interest that do not crystallize as single crystals of suitable size and quality for single-crystal X-ray diffraction.