Dirichlet Priors for MAP Inference of Protein Conformation Abundances from SAXS

Estimation of mixture coefficients of protein conformations in solution find applications in understanding protein behavior. We describe a method for maximum a posteriori (MAP) estimation of the mixture coefficients of ensemble of conformations in a protein mixture solution using measured small angle X-ray scattering (SAXS) intensities. The proposed method builds upon a model for the measurements of crystallographically determined conformations. Assuming that a priori information on the protein mixture is available, and that priori information follows a Dirichlet distribution, we develop a method to estimate the relative abundances with MAP estimator. The Dirichlet distribution depends on concentration parameters which may not be known in practice and thus need to be estimated. To estimate these unknown concentration parameters we developed an expectation-maximization (EM) method. Adenylate kinase (ADK) protein was selected as the test bed due to its known conformations Beckstein et al. (Journal of Molecular Biology, 394(1), 160 1). Known conformations are assumed to form the full vector bases that span the measurement space. In Monte Carlo simulations, mixture coefficient estimation performances of MAP and maximum likelihood (ML) (which assumes a uniform prior on the mixture coefficients) estimators are compared. MAP estimators using known and unknown concentration parameters are also compared in terms of estimation performances. The results show that prior knowledge improves estimation accuracy, but performance is sensitive to perturbations in the Dirichlet distribution’s concentration parameters. Moreover, the estimation method based on EM algorithm shows comparable results to approximately known prior parameters.     

Novel C2-symmetric macrocycles bearing diamide-diester groups: synthesis and enantiomeric recognition for primary alkyl ammonium salts

We synthesized a series of novel macrocycles with diamide-diester groups (S,S)-1, (S,S)-2, (S,S)-3, and (R,R)-1, derived from dimethyloxalate and amino alcohols by high dilution technique, and evaluated enantiomeric recognition properties of these macrocycles toward primary alkyl ammonium salts by 1H NMR titration. Taking into account the host employed, important differences were observed in the Ka values of (R)-Am and (S)-Am for (S,S)-1 and (R,R)-1 hosts, KS/KR = 5.55 and KR/KS = 3.65, Delta Delta Go = 0.43 and -0.32 kJ mol-1, respectively. There seems a general tendency for the host to include the guests with the same absolute configuration.     

Laccase-based amperometric biosensor enhanced by Ni/Au/PPy-COOH multisegmental nanowires for selective dopamine detection

One-dimensional Ni/Au/PPy-COOH nanowires with multiple segments were synthesized in this study. Smooth surfaces and magnetic properties of nanowires were investigated by scanning transmission electron microscopy (SEM), Energy-dispersive X-ray spectroscopy (EDX), and Electron Spin Resonance (ESR) techniques. The nanowires were used to modify the screen-printed electrode surface and as a micro-environment for Trametes versicolor laccase. The ability of this enzyme biosensor to detect dopamine change in human biological samples was demonstrated by a wide linear range (0.01–50 μM) and a low LOD (2.265 nM). In addition, the biosensor exhibited excellent selectivity allowing the detection of dopamine in the presence of ascorbic acid, uric acid, L-Cys, serotonin, and glucose, with high sensitivity of reduction currents obtained at −0.2 V (vs. Ag/AgCl). The proposed biosensor allowed the detection of dopamine in commercial serum and artificial urine with recovery values close to 100 %. It also demonstrated reproducibility, reusability, and long-term storage stability. The sensitivity, K_m^app, and I_max values of the biosensor were determined as 2.05 μM and 1.03 μA, respectively. The LAC-Ni/Au/PPy-COOH/NAF/SPE biosensor is a reliable design for detecting dopamine with a wide linear range.     

Simulating electron spin resonance spectra of nitroxide spin labels from molecular dynamics and stochastic trajectories

Simulating electron spin resonance spectra of nitroxide spin labels from motional models is necessary for the quantitative analysis of experimental spectra. We present a framework for modeling the spin label dynamics by using trajectories such as those from molecular dynamics (MD) simulations combined with stochastic treatment of the global protein tumbling. This is achieved in the time domain after two efficient numerical integrators are developed: One for the quantal dynamics of the spins and the other for the classical rotational diffusion. For the quantal dynamics, we propagate the relevant part of the spin density matrix in Hilbert space. For the diffusional tumbling, we work with quaternions, which enables the treatment of anisotropic diffusion in a potential expanded as a sum of spherical harmonics. Time-averaging arguments are invoked to bridge the gap between the smaller time step of the MD trajectories and the larger time steps appropriate for the rotational diffusion and/or quantal spin dynamics.     

On the Modeling of CO2 EUA and CER Prices of EU‐ETS for the 2008–2012 Period

Increased consumption of fossil fuels in industrial production has led to a significant elevation in the emission of greenhouse gases and to global warming. The most effective international action against global warming is the Kyoto Protocol, which aims to reduce carbon emissions to desired levels in a certain time span. Carbon trading is one of the mechanisms used to achieve the desired reductions. One of the most important implications of carbon trading for industrial systems is the risk of uncertainty about the prices of carbon allowance permits traded in the carbon markets. In this paper, we consider stochastic and time series modeling of carbon market prices and provide estimates of the model parameters involved, based on the European Union emissions trading scheme carbon allowances data obtained for 2008–2012 period. In particular, we consider fractional Brownian motion and autoregressive moving average–generalized autoregressive conditional heteroskedastic modeling of the European Union emissions trading scheme data and provide comparisons with benchmark models. Our analysis reveals evidence for structural changes in the underlying models in the span of the years 2008–2012. Data‐driven methods for identifying possible change‐points in the underlying models are employed, and a detailed analysis is provided. Our analysis indicated change‐points in the European Union Allowance (EUA) prices in the first half of 2009 and in the second half of 2011, whereas in the Certified Emissions Reduction (CER) prices three change‐points have appeared, in the first half of 2009, the middle of 2011, and in the second half of 2012. These change‐points seem to parallel the global economic indicators as well. Copyright © 2016 John Wiley & Sons, Ltd.     

Elucidating the Stereochemistry of Enzymatic Benzylsuccinate Synthesis with Chirally Labeled Toluene

Benzylsuccinate synthase is a glycyl radical enzyme that initiates anaerobic toluene metabolism by adding fumarate to the methyl group of toluene to yield (R)‐benzylsuccinate. To investigate whether the reaction occurs with retention or inversion of configuration at the methyl group of toluene, we synthesized both enantiomers of chiral toluene with all three H isotopes in their methyl groups. The chiral toluenes were converted into benzylsuccinates preferentially containing ²H and ³H at their benzylic C atoms, owing to a kinetic isotope effect favoring hydrogen abstraction from the methyl groups. The configuration of the products was analyzed by enzymatic CoA‐thioester synthesis and stereospecific oxidation using enzymes involved in benzylsuccinate degradation. Assessment of the configurations of the benzylsuccinate isomers based on loss or retention of tritium showed that inversion of configuration at the methyl group occurs when the chiral toluenes react with fumarate.     

Information Regularized Sensor Fusion: Application to Localization With Distributed Motion Sensors

We propose the information regularization principle for fusing information from sets of identical sensors observing a target phenomenon. The principle basically proposes an importance-weighting scheme for each sensor measurement based on the mutual information based pairwise statistical similarity matrix between sensors. The principle is applied to maximum likelihood estimation and particle filter based state estimation. A demonstration of the proposed regularization scheme in centralized data fusion of dense motion detector networks for target tracking is provided. Simulations confirm that the introduction of information regularization significantly improves localization accuracy of both maximum likelihood and particle filter approaches compared to their baseline implementations. Outlier detection and sensor failure detection capabilities, as well as possible extensions of the principle to decentralized sensor fusion with communication constraints are briefly discussed.