Spin label EPR-based characterization of biosystem complexity

Following the widely spread EPR spin-label applications for biosystem characterization, a novel approach is proposed for EPR-based characterization of biosystem complexity. Hereto a computational method based on a hybrid evolutionary optimization (HEO) is introduced. The enormous volume of information obtained from multiple HEO runs is reduced with a novel so-called GHOST condensation method for automatic detection of the degree of system complexity through the construction of two-dimensional solution distributions. The GHOST method shows the ability of automatic quantitative characterization of groups of solutions, e.g. the determination of average spectral parameters and group contributions. The application of the GHOST condensation algorithm is demonstrated on four synthetic examples of different complexity and applied to two physiologically relevant examples--the determination of domains in biomembranes (lateral heterogeneity) and the study of the low-resolution structure of membrane proteins.     

On the suitability of different representations of solid catalysts for combinatorial library design by genetic algorithms

Genetic algorithms are widely used to solve and optimize combinatorial problems and are more often applied for library design in combinatorial chemistry. Because of their flexibility, however, their implementation can be challenging. In this study, the influence of the representation of solid catalysts on the performance of genetic algorithms was systematically investigated on the basis of a new, constrained, multiobjective, combinatorial test problem with properties common to problems in combinatorial materials science. Constraints were satisfied by penalty functions, repair algorithms, or special representations. The tests were performed using three state-of-the-art evolutionary multiobjective algorithms by performing 100 optimization runs for each algorithm and test case. Experimental data obtained during the optimization of a noble metal-free solid catalyst system active in the selective catalytic reduction of nitric oxide with propene was used to build up a predictive model to validate the results of the theoretical test problem. A significant influence of the representation on the optimization performance was observed. Binary encodings were found to be the preferred encoding in most of the cases, and depending on the experimental test unit, repair algorithms or penalty functions performed best.     

Computer-assisted method development in liquid chromatography-mass spectrometry: new proposals

A new approach is proposed for computer-assisted method development in LC-MS. The procedure consists of three stages. Firstly, an accurate retention model is developed for the peaks in the mixture to be separated by use of an iterative approach with isocratic priming data, which is calibrated and validated by means of a few gradient runs. Secondly, a specially developed LC-MS objective function, based on selectivity targets (the selectivity matrix), is calculated and used to evaluate the simulated chromatograms and drive the optimization process. Thirdly, the retention model and the selectivity matrix objective function are used with an evolutionary algorithm in which the concepts of constrained Pareto optimality are applied, to carry out the unattended optimization process. The system was applied to real data for a complex separation and compared with the results provided by a commercial tool for computer-assisted method development.     

An adaptive immune optimization algorithm for energy minimization problems

Based on the immune theory of biology, a novel evolutionary algorithm, adaptive immune optimization algorithm (AIOA), is proposed. In AIOA, density regulation and immune selection is adopted to control the individual diversity and the convergence adaptively. By an application of the algorithm to the optimization of test functions, it is shown that the algorithm is a highly efficient optimization method compared with other stochastic optimization methods. The algorithm was also applied to the optimization of Lennard-Jones clusters, and the results show that the method can find the optimal structure of N相似文献   

SimLabel: a graphical user interface to simulate continuous wave EPR spectra from site‐directed spin labeling experiments

Site‐directed spin labeling (SDSL) combined with continuous wave electron paramagnetic resonance (cw EPR) spectroscopy is a powerful technique to reveal, at the residue level, structural transitions in proteins. SDSL‐EPR is based on the selective grafting of a paramagnetic label on the protein under study, followed by cw EPR analysis. To extract valuable quantitative information from SDSL‐EPR spectra and thus give reliable interpretation on biological system dynamics, numerical simulations of the spectra are required. Such spectral simulations can be carried out by coding in MATLAB using functions from the EasySpin toolbox. For non‐expert users of MATLAB, this could be a complex task or even impede the use of such simulation tool. We developed a graphical user interface called SimLabel dedicated to run cw EPR spectra simulations particularly coming from SDSL‐EPR experiments. Simlabel provides an intuitive way to visualize, simulate, and fit such cw EPR spectra. An example of SDSL‐EPR spectra simulation concerning the study of an intrinsically disordered region undergoing a local induced folding is described and discussed. We believe that this new tool will help the users to rapidly obtain reliable simulated spectra and hence facilitate the interpretation of their results. Copyright © 2017 John Wiley & Sons, Ltd.     

Optimization and validation of an enantioselective method for a chiral drug with eight stereo-isomers in capillary electrophoresis

Highly selective capillary electrophoresis (CE) screening methods were applied to find a satisfactory separation of a chiral drug with eight stereoisomeric compounds. The initial separation conditions were further optimized using response surface modelling by applying a Box-Behnken experimental design. This approach resulted in a rapid and efficient optimization of the buffer concentration, the concentration of two cyclodextrins, and the run voltage, in order to obtain final separation conditions of the method. Further optimization and validation of the system in terms of sensitivity and robustness resulted in a method that is suitable for quality control release purposes.     

A fast annealing evolutionary algorithm for global optimization

By combining the aspect of population in genetic algorithms (GAs) and the simulated annealing algorithm (SAA), a novel algorithm, called fast annealing evolutionary algorithm (FAEA), is proposed. The algorithm is similar to the annealing evolutionary algorithm (AEA), and a very fast annealing technique is adopted for the annealing procedure. By an application of the algorithm to the optimization of test functions and a comparison of the algorithm with other stochastic optimization methods, it is shown that the algorithm is a highly efficient optimization method. It was also applied in optimization of Lennard-Jones clusters and compared with other methods in this study. The results indicate that the algorithm is a good tool for the energy minimization problem.     

Chromatographic Behavior of Fosinopril Sodium and Fosinoprilat Using Neural Networks

In this paper, the chromatographic characterization of fosinopril sodium and fosinoprilat is presented. The first stept was pK _a determination for the active substance and its degradation product using RP-LC. It was followed by optimization employing the combination of experimental design and artificial neural networks. For the definition of input and output variables, the central composite design for three factors was built. Back propagation algorithm was applied to model the system, and then the optimization of the experimental conditions was carried out in the neural network with 3-8-2 structure, which confirmed to be able to provide the maximum performance. From the method optimization, the most appropriate experimental conditions for fosinopril sodium and fosinoprilat analysis were extracted. The optimized method was validated and applied in the quality control of tablets and for forced degradation studies.     

An integrated method for degradation products detection and characterization using hybrid ion trap/time-of-flight mass spectrometry and data processing techniques: Application to study of the degradation products of danofloxacin under stressed conditions

A new strategy using hybrid ion trap/time-of-flight mass spectrometry coupled with high-performance liquid chromatography and post-acquisition data mining techniques was developed and applied to the detection and characterization of degradation products of danofloxacin. The degradation products formed under different forced conditions were separated using an ODS-C18 column with gradient elution. Accurate full-scan MS data were acquired in the first run and processed with the combination of extracted ion chromatograms and LC-UV chromatograms. These processes were able to find accurate molecular masses of possible degradation products. Then, the accurate MS/MS data acquired through data-dependent analysis mode in another run facilitated the structural elucidations of degradation products. As a result, a total of 11 degradation products of danofloxacin were detected and characterized using the developed method. Overall, this analytical strategy enables the acquisition of accurate-mass LC/MS data, search of a variety of degradation products through the post-acquisition processes, and effective structural characterization based on elemental compositions of degradation product molecules and their product ions. The ability to measure degradation products via tandem mass spectrometry coupled with accurate mass measurement, all in only two experimental runs, is one of the most attractive features of this methodology. The results demonstrate that use of the LC/MS-IT-TOF approach appears to be rapid, efficient and reliable in structural characterization of drug degradation products.     

Structural optimization of model colloidal clusters at the air-water interface using genetic algorithms

The structure of colloidal clusters observed at the air-water interface is studied theoretically in this paper using a recently developed global optimization routine based on a genetic algorithm. A model potential with a secondary minimum produced from the attractive van der Waals interaction and repulsive dipolar interaction is used for the colloid-colloid interaction. The structures predicted from the genetic algorithm are not always consistent with the experimental observations; the theoretical results predict the growth of cluster as a spreading of triangular networks, however, the experimental observations seem to suggest the formation of a circular shell structure. More thorough theoretical as well as experimental studies are called for in order to obtain more conclusive evidence regarding these matters.     

MultiMCS: a fast algorithm for the maximum common substructure problem on multiple molecules

Several efficient correspondence graph-based algorithms for determining the maximum common substructure (MCS) of a pair of molecules have been published in the literature. The extension of the problem to three or more molecules is however nontrivial; heuristics used to increase the efficiency in the two-molecule case are either inapplicable to the many-molecule case or do not provide significant speedups. Our specific algorithmic contribution is two-fold. First, we show how the correspondence graph approach for the two-molecule case can be generalized to obtain an algorithm that is guaranteed to find the optimum connected MCS of multiple molecules, and that runs fast on most families of molecules using a new divide-and-conquer strategy that has hitherto not been reported in this context. Second, we provide a characterization of those compound families for which the algorithm might run slowly, along with a heuristic for speeding up computations on these families. We also extend the above algorithm to a heuristic algorithm to find the disconnected MCS of multiple molecules and to an algorithm for clustering molecules into groups, with each group sharing a substantial MCS. Our methods are flexible in that they provide exquisite control on various matching criteria used to define a common substructure.     

Strategy for developing and optimizing liquid chromatography methods in pharmaceutical development using computer-assisted screening and Plackett-Burman experimental design

We describe a three-step method development/optimization strategy for HPLC assay/impurity methods for pharmaceuticals, which include multiple-column/mobile phase screening using a system equipped with a column-switching device, further optimization of separation by using multiple organic modifiers in the mobile phase, and multiple-factor method optimization using Plackett-Burman experimental designs. In the first two steps, commercially available chromatography optimization software, DryLab, was used to perform computer simulations. This allows the method developer to evaluate each condition (one column/mobile phase combination) with retention data from two scouting gradient runs. This approach significantly reduces the number of runs in method development. After a satisfactory separation was obtained, we used a method optimization step with Plackett-Burman experimental designs. The purpose of the 16-injection set experiments was to evaluate nine method factors with regard to method precision, accuracy, sensitivity and specificity. The results provided logical justifications in selecting method parameters such as column temperature, detection wavelength, injection volume, and sample solvent, etc. In data analysis, instead of the traditional mathematical manipulations, we used the graphical methods to examine and present data by creating the so-called main effect plots. Because replicates of design points were not run, the data did not allow the testing of statistical significance. However, it provided visual presentations in a way that is easy to understand for the method developer and end user alike.     

Application of the combined CFD and swarm intelligence for optimization of baffles number in a mixer-settler

Process optimization in a mixer-settler is of great importance. Optimization algorithm of particle swarm optimization is one of the evolutionary algorithms to solve optimization problem which is used in many fields. In this study, the optimal condition is calculated in finite volume method in terms of the number of baffles, inlet velocity of fluid, and impeller speed in a mixer-settler with a specific dimension that can be extended to industrial dimensions using the PSO algorithm and the numerical solution of Navier-Stokes equations and k-ε standard.     

Biologically driven neural platform invoking parallel electrophoretic separation and urinary metabolite screening

This work reveals a computational framework for parallel electrophoretic separation of complex biological macromolecules and model urinary metabolites. More specifically, the implementation of a particle swarm optimization (PSO) algorithm on a neural network platform for multiparameter optimization of multiplexed 24-capillary electrophoresis technology with UV detection is highlighted. Two experimental systems were examined: (1) separation of purified rabbit metallothioneins and (2) separation of model toluene urinary metabolites and selected organic acids. Results proved superior to the use of neural networks employing standard back propagation when examining training error, fitting response, and predictive abilities. Simulation runs were obtained as a result of metaheuristic examination of the global search space with experimental responses in good agreement with predicted values. Full separation of selected analytes was realized after employing optimal model conditions. This framework provides guidance for the application of metaheuristic computational tools to aid in future studies involving parallel chemical separation and screening. Adaptable pseudo-code is provided to enable users of varied software packages and modeling framework to implement the PSO algorithm for their desired use.     

Simultaneous optimization of the solid-phase extraction of organochlorine and organophosphorus pesticides using the desirability function

In a multi-response situation [solid-phase extraction (SPE) of 18 organochlorine and nine organophosphorus pesticides from aqueous solution], several response variables are considered, and the optimization problem is more complex than in the single response case. Experimental design combined with desirability function was applied for the optimization of the SPE of these pesticides in order to obtain optimum operating conditions for routine work. This multi-criterion approach was developed for the simultaneous optimization of multiple responses and it is simple, easy to apply and permits the user to make subjective judgments on the importance of each response. The desirability function is based on the search for a global optimum [D = f (Y1, Y2, ..., Yn)] by the transformation of the measured property to a dimensionless scale for each criterion. The experimental design strategy involved a central composite design. A compromise between conflicting goals, such as achievement of good yields with SPE recoveries of ca. 100% for a large number of analytes with inherent chemical differences, was found by means of the desirability function D.     

Transition State Thermodynamics of Lipid Bilayers Characterized by Differential Scanning Calorimetry

The laboratory experiment presented in this paper focuses on using differential scanning calorimetry to determine the calorimetric enthalpy and cooperativity of the gel to liquid crystalline phase transition in hydrated lipid bilayers as a function of cholesterol content. The procedure and analysis are appropriate for junior-and senior-level physics and biophysical chemistry courses in the undergraduate curriculum. The laboratory is used to emphasize the use of thermodynamic data to obtain information about structure-function relationships in biological systems. The experimental results are directly related to the authors ongoing research in lipid bilayer structure characterization and applications of hydrated lipid bilayers as model systems for the interpretation of MRI contrast. The laboratory is easily modified to study the effects of other conditions, such as degree of hydration, pH, and composition, on the thermodynamic behavior of lipid bilayers.     

GARANT-a general algorithm for resonance assignment of multidimensional nuclear magnetic resonance spectra

A new program for automatic resonance assignment of nuclear magnetic resonance (NMR) spectra of proteins, GARANT (General Algorithm for Resonance AssignmeNT), is introduced. Three principal elements used in this approach are: (a) representation of resonance assignments as an optimal match of two graphs describing, respectively, peaks expected from combined knowledge of the primary structure and the magnetization transfer pathways in the spectra used, and experimentally observed peaks; (b) a scoring scheme able to distinguish between correct and incorrect resonance assignments; and (c) combination of an evolutionary algorithm with a local optimization routine. The score that evaluates the match of expected peaks to observed peaks relies on the agreement of the information available about these peaks, most prominently, but not exclusively, the chemical shifts. Tests show that the combination of an evolutionary algorithm and a local optimization routine yields results that are clearly superior to those obtained when using either of the two techniques separately in the search for the correct assignments. GARANT is laid out for assignment problems involving peaks observed in two- and three-dimensional homonuclear and heteronuclear NMR spectra of proteins. © 1997 by John Wiley & Sons, Inc.     

Geometry optimizations of benzene clusters using a modified genetic algorithm

A modified genetic algorithm with real-number coding, non-uniform mutation and arithmetical crossover operators was described in this paper. A local minimization was used to improve the final solution obtained by the genetic algorithm. Using the exp-6-1 interatomic energy function, the modified genetic algorithm with local minimization (MGALM) was applied to the geometry optimization problem of small benzene clusters (C6H6)N(N = 2-7) to obtain the global minimum energy structures. MGALM is simple but the structures optimized are comparable to the published results obtained by parallel genetic algorithms.     

Theory for trivial trajectory parallelization of multicanonical molecular dynamics and application to a polypeptide in water

Trivial trajectory parallelization of multicanonical molecular dynamics (TTP-McMD) explores the conformational space of a biological system with multiple short runs of McMD starting from various initial structures. This method simply connects (i.e., trivially parallelizes) the short trajectories and generates a long trajectory. First, we theoretically prove that the simple trajectory connection satisfies a detailed balance automatically. Thus, the resultant long trajectory is regarded as a single multicanonical trajectory. Second, we applied TTP-McMD to an alanine decapeptide with an all-atom model in explicit water to compute a free-energy landscape. The theory imposes two requirements on the multiple trajectories. We have demonstrated that TTP-McMD naturally satisfies the requirements. The TTP-McMD produces the free-energy landscape considerably faster than a single-run McMD does. We quantitatively showed that the accuracy of the computed landscape increases with increasing the number of multiple runs. Generally, the free-energy landscape of a large biological system is unknown a priori. The current method is suitable for conformational sampling of such a large system to reduce the waiting time to obtain a canonical ensemble statistically reliable.