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Optimized green fluorescent protein variants provide improved single cell resolution of transgene expression in ascidian embryos.

The green fluorescent protein (GFP) is used extensively to monitor gene expression and protein localization in living cells, particularly in developing embryos from a variety of species. Several GFP mutations have been characterized that improve protein expression and alter the emission spectra to produce proteins that emit green, blue, cyan, and yellow wavelengths. DsRed and its variants encode proteins that emit in the orange to red wavelengths. Many of these commercially available fluorescent proteins have been "codon optimized" for maximal levels of expression in mammalian cells. We have generated several fluorescent protein color variants that have been codon optimized for maximal expression in the ascidian Ciona intestinalis. By analyzing quantitative time-lapse recordings of transgenic embryos, we demonstrate that, in general, our Ciona optimized variants are detected and expressed at higher levels than commercially available fluorescent proteins. We show that three of these proteins, expressed simultaneously in different spatial domains within the same transgenic embryo are easily detectable using optimized fluorescent filter sets for epifluorescent microscopy. Coupled with recently developed quantitative imaging techniques, our GFP variants should provide useful reagents for monitoring the simultaneous expression of multiple genes in transgenic ascidian embryos.

Animals↗

Optimization of selectivity and resolution in micellar electrokinetic capillary chromatography with a mixed micellar system of sodium dodecyl sulfate and sodium cholate.

Selectivity and resolution were studied for the separation of seven corticosteroids by micellar electrokinetic capillary chromatography (MEKC) using a mixed micellar solution of sodium dodecyl sulfate (SDS) and sodium cholate (SC), buffered with 3-(N-morpholino)propanesulfonic acid (MOPS) or 3-[(1,1-dimethyl-2-hydroxyethyl)amino]-2-hydroxypropane sulfonic acid (AMPSO). The changes in selectivity were compared for the AMPSO-SDS-SC system by varying the pH and the concentrations of AMPSO, SDS and SC. The experimental design started with the central composite design and continued in a sequential manner. The optimum selectivity for the separation of the corticosteroids was calculated from the analyte migration times and the analyte velocities, by using empirical quadratic regression models. Satisfactory regression fits and coefficients of determination for prediction were obtained with cross-validated models. To optimize the resolution, the physical parameters of capillary length and analysis time were varied under the conditions optimal for the selectivity. In both the selectivity and the resolution, optimization the overall optimum was determined by using the desirability function technique. Analysis times were controlled by using 1,3-diaminopropane to influence the electroosmotic flow velocity (veo). The voltage was kept constant, which resulted in higher electric field strength in shorter capillaries. No changes in the selectivity were observed when 1,3-diaminopropane was used to control the electroosmotic flow velocity. Such an optimization technique, where the chemical and physical factors affecting the separation are treated independently, seemed to be effective for finding the best possible resolution for the corticosteroids.

Adrenal Cortex Hormones↗

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.

Journal Article↗

Constructing irregular surfaces to enclose macromolecular complexes for mesoscale modeling using the discrete surface charge optimization (DISCO) algorithm.

Salt-mediated electrostatics interactions play an essential role in biomolecular structures and dynamics. Because macromolecular systems modeled at atomic resolution contain thousands of solute atoms, the electrostatic computations constitute an expensive part of the force and energy calculations. Implicit solvent models are one way to simplify the model and associated calculations, but they are generally used in combination with standard atomic models for the solute. To approximate electrostatics interactions in models on the polymer level (e.g., supercoiled DNA) that are simulated over long times (e.g., milliseconds) using Brownian dynamics, Beard and Schlick have developed the DiSCO (Discrete Surface Charge Optimization) algorithm. DiSCO represents a macromolecular complex by a few hundred discrete charges on a surface enclosing the system modeled by the Debye-Hückel (screened Coulombic) approximation to the Poisson-Boltzmann equation, and treats the salt solution as continuum solvation. DiSCO can represent the nucleosome core particle (>12,000 atoms), for example, by 353 discrete surface charges distributed on the surfaces of a large disk for the nucleosome core particle and a slender cylinder for the histone tail; the charges are optimized with respect to the Poisson-Boltzmann solution for the electric field, yielding a approximately 5.5% residual. Because regular surfaces enclosing macromolecules are not sufficiently general and may be suboptimal for certain systems, we develop a general method to construct irregular models tailored to the geometry of macromolecules. We also compare charge optimization based on both the electric field and electrostatic potential refinement. Results indicate that irregular surfaces can lead to a more accurate approximation (lower residuals), and the refinement in terms of the electric field is more robust. We also show that surface smoothing for irregular models is important, that the charge optimization (by the TNPACK minimizer) is efficient and does not depend on the initial assigned values, and that the residual is acceptable when the distance to the model surface is close to, or larger than, the Debye length. We illustrate applications of DiSCO's model-building procedure to chromatin folding and supercoiled DNA bound to Hin and Fis proteins. DiSCO is generally applicable to other interesting macromolecular systems for which mesoscale models are appropriate, to yield a resolution between the all-atom representative and the polymer level.

Algorithms↗

Fully quantum mechanical energy optimization for protein-ligand structure.

We present a quantum mechanical approach to study protein-ligand binding structure with application to a Adipocyte lipid-binding protein complexed with Propanoic Acid. The present approach employs a recently develop molecular fractionation with a conjugate caps (MFCC) method to compute protein-ligand interaction energy and performs energy optimization using the quasi-Newton method. The MFCC method enables us to compute fully quantum mechanical ab initio protein-ligand interaction energy and its gradients that are used in energy minimization. This quantum optimization approach is applied to study the Adipocyte lipid-binding protein complexed with Propanoic Acid system, a complex system consisting of a 2057-atom protein and a 10-atom ligand. The MFCC calculation is carried out at the Hartree-Fock level with a 3-21G basis set. The quantum optimized structure of this complex is in good agreement with the experimental crystal structure. The quantum energy calculation is implemented in a parallel program that dramatically speeds up the MFCC calculation for the protein-ligand system. Similarly good agreement between MFCC optimized structure and the experimental structure is also obtained for the streptavidin-biotin complex. Due to heavy computational cost, the quantum energy minimization is carried out in a six-dimensional space that corresponds to the rigid-body protein-ligand interaction.

Carrier Proteins↗

A dynamic lattice searching method for fast optimization of Lennard-Jones clusters.

A highly efficient unbiased global optimization method called dynamic lattice searching (DLS) was proposed. The method starts with a randomly generated local minimum, and finds better solution by a circulation of construction and searching of the dynamic lattice (DL) until the better solution approaches the best solution. The DL is constructed adaptively based on the starting local minimum by searching the possible location sites for an added atom, and the DL searching is implemented by iteratively moving the atom located at the occupied lattice site with the highest energy to the vacant lattice site with the lowest energy. Because the DL can greatly reduce the searching space and the number of the time-consuming local minimization procedures, the proposed DLS method runs at a very high efficiency, especially for the clusters of larger size. The performance of the DLS is investigated in the optimization of Lennard-Jones (LJ) clusters up to 309 atoms, and the structure of the LJ(500) is also predicted. Furthermore, the idea of dynamic lattice can be easily adopted in the optimization of other molecular or atomic clusters. It may be a promising approach to be universally used for structural optimizations in the chemistry field.

Journal Article↗

An unbiased population-based search for the geometry optimization of Lennard-Jones clusters: 2 < or = N < or = 372.

This article presents the results obtained using an unbiased Population Based Search (PBS) for optimizing Lennard-Jones clusters. PBS is able to repeatedly obtain all putative global minima, for Lennard-Jones clusters in the range 2 < or = N < or = 372, as reported in the Cambridge Cluster Database. The PBS algorithm incorporates and extends key techniques that have been developed in other Lennard-Jones optimization algorithms over the last decade. Of particular importance are the use of cut-and-paste operators, structure niching (using the cluster strain energy as a metric), two-phase local search, and a new operator, Directed Optimization, which extends the previous concept of directed mutation. In addition, PBS is able to operate in a parallel mode for optimizing larger clusters.

Journal Article↗

MDSIMAID: automatic parameter optimization in fast electrostatic algorithms.

MDSIMAID is a recommender system that optimizes parallel Particle Mesh Ewald (PME) and both sequential and parallel multigrid (MG) summation fast electrostatic solvers. MDSIMAID optimizes the running time or parallel scalability of these methods within a given error tolerance. MDSIMAID performs a run time constrained search on the parameter space of each method starting from semiempirical performance models. Recommended parameters are presented to the user. MDSIMAID's optimization of MG leads to configurations that are up to 14 times faster or 17 times more accurate than published recommendations. Optimization of PME can improve its parallel scalability, making it run twice as fast in parallel in our tests. MDSIMAID and its Python source code are accessible through a Web portal located at http://mdsimaid.cse.nd.edu.

Journal Article↗

SODOCK: swarm optimization for highly flexible protein-ligand docking.

Protein-ligand docking can be formulated as a parameter optimization problem associated with an accurate scoring function, which aims to identify the translation, orientation, and conformation of a docked ligand with the lowest energy. The parameter optimization problem for highly flexible ligands with many rotatable bonds is more difficult than that for less flexible ligands using genetic algorithm (GA)-based approaches, due to the large numbers of parameters and high correlations among these parameters. This investigation presents a novel optimization algorithm SODOCK based on particle swarm optimization (PSO) for solving flexible protein-ligand docking problems. To improve efficiency and robustness of PSO, an efficient local search strategy is incorporated into SODOCK. The implementation of SODOCK adopts the environment and energy function of AutoDock 3.05. Computer simulation results reveal that SODOCK is superior to the Lamarckian genetic algorithm (LGA) of AutoDock, in terms of convergence performance, robustness, and obtained energy, especially for highly flexible ligands. The results also reveal that PSO is more suitable than the conventional GA in dealing with flexible docking problems with high correlations among parameters. This investigation also compared SODOCK with four state-of-the-art docking methods, namely GOLD 1.2, DOCK 4.0, FlexX 1.8, and LGA of AutoDock 3.05. SODOCK obtained the smallest RMSD in 19 of 37 cases. The average 2.29 A of the 37 RMSD values of SODOCK was better than those of other docking programs, which were all above 3.0 A.

Algorithms↗

Detection of intentional sub-optimal performance on a computerized finger-tapping task.

Twenty-four undergraduates were assigned to two groups to determine if the T3 computerized finger-tapping task could discriminate between persons motivated to perform optimally and those motivated to perform sub-optimally. Group HI was instructed to simulate a head-injured person undergoing an insurance evaluation, whereas group J was instructed to simulate an applicant undergoing a job interview. Group HI tapped slower, demonstrated less fatigue, less similarity between their right- and left-hand performance, and showed less of a speed advantage for the preferred hand. While group HI displayed less variability for the non-preferred hand, the groups did not differ on variability for the preferred hand. Therefore, the T3 successfully discriminates on multiple measures between persons simulating optimal and sub-optimal performance.

Adolescent↗

Sulphonated polystyrene as an optimal substratum for the adhesion and spreading of mesenchymal cells in monovalent and divalent saline solutions.

Cell adhesion and spreading were studied on sulphonated polystyrene dishes in serum-free saline (Mn, Na, Cl, buffer) i.e., without an intervening protein layer. Spreading as a function of surface charge density, SCD, peaked around 2-10 negative charges per square nanometer, corresponding to a monomolecular layer of sulphonate ions. At optimal SCD, macrophages, BHK-C13 and whole mouse embryo secondary cells all showed considerable spreading, even in monovalent saline-more so than on a conventional tissue-culture surface. But outside this narrow range of SCD, or on protein-coated surfaces, the divalent cation was indispensable. The biphasic effect of sulphonation on cell adhesion is consistent with the theory that a substratum need not be biochemically specific, provided it is physiochemically polar, rigid and dense. According to this theory, polystyrene of sub-optimal SCD would not be sufficiently polar, while supra-optimal sulphonation would produce a hydrogel surface, lacking in local rigidity and density, due to osmotic swelling. The principle of polymer exclusion, by a surface hydrogel layer, is also consistent with observations on the inhibitory effects of adsorbed proteins-viz., albumin, collagen, serum and cellular exudate, respectively-contrasted with the ready attachment of cells to a bare, optimally charged substratum, in this minimal in vitro system.

Animals↗

Optimal feed temperature for an immobilized enzyme packed-bed reactor.

Optimal feed temperature was determined for a nonisothermal immobilized enzymatic reaction with enzyme deactivation in a packed-bed reactor. The optimal feed temperature was obtained by maximizing the average substrate conversion over a given reaction period. Simulation showed the optimal feed temperature to be strongly dependent on the flow dispersion, the reaction activation energy, the corresponding enzyme inactivation energy and the heat of reaction. It was also observed that in a plug flow reactor the enzyme reaction generally exhibited a lower optimal feed temperature and higher substrate conversion than in a continuously stirred tank reactor.

Enzymes, Immobilized↗

Optimal temperatures for inner-ear performance in gekkonoid lizards.

Temperature effects on the cochlear alternating potentials are described in lizards of the Eublepharidae (4 spp. and subspp.), Diplodactylinae (2 spp.), Gekkoninae (7 spp. and subspp.) and Sphaerodactylidae (1 sp.). Pure tones (50-15,000 Hz) were applied to the anaesthetized gecko's external ear. Sound intensity needed for a standard response was plotted (db versus frequency). A total of 249 such sensitivity functions were obtained from 50 animals, at temperatures of 15-40 degrees C. For each species, an optimal sensitivity function is definable by its extreme sensitivity and relative smoothness. This is obtained throughout a specific optimal temperature range. At lower temperatures, the function is less sensitive and its best sensitivity is at a lower frequency. The specific optimal cochlear temperature range resembles the specific ecological temperature preferendum where known. Both are broader in gekkonoid than in iguanid lizards. The temperature effects on the sensitivity function occur even with aerial or mechanical stimulation at the oval window after middle ear extirpation. Hence they arise in the inner ear. Their magnitude (db/ degrees C) is half that known from auditory nerve potentials. When accurate auditory data are desired, the specific optimal temperature and its possible seasonal variation require consideration. This increases the need for proper identification of subjects by name and origin, and for monitoring experimental thermal parameters, including pre-experimental acclimation.

Acoustic Stimulation↗

Determining and optimizing the precision of quantitative measurements of perfusion from dynamic contrast enhanced MRI.

PURPOSE: To examine the sensitivity of quantitative dynamic contrast enhanced MRI (DCE-MRI) perfusion maps to errors in the various source images and to determine optimal imaging parameters for reducing this sensitivity. MATERIALS AND METHODS: A detailed analysis of the precision of a DCE-MRI protocol was performed using the "propagation of errors" technique to investigate the effect of errors in the source images on errors in K(trans). Optimal parameter values and interactions between parameters were examined. The propagation of errors analysis was validated by Monte-Carlo simulations. RESULTS: The precision of K(trans) was found to be most sensitive to artifacts in the tissue portion of the baseline images and least sensitive to noise in the arterial portion of the dynamic images. The tip-angle strongly affected the precision, with the optimum being a function of tissue T1(0). CONCLUSION: Protocol optimization requires matching the tip-angle to the anticipated T1(0) of the tissue of interest; however such optimization yields a relatively small improvement. Future developmental efforts would be most productively focused on minimizing the artifact level.

Artifacts↗

A generic stepwise optimization strategy for liquid chromatography electrospray ionization tandem mass spectrometry methods.

The feasibility and advantages of using sophisticated chemometric tools in combination with the execution of thoroughly planned experiments to determine experimental conditions for optimal performance of an LC-ESI-MS/MS analysis is demonstrated. A stepwise strategy is proposed, which provides a controlled optimization procedure of the chromatographic quality (in terms of separation among the sample constituents) and maximizes the mass spectrometric signal of the selected product ions. Design of experiments (DOE) and response surface methodology are applied throughout the procedure. The stepwise approach has the advantage of dealing with the different optimization criteria separately, i.e. first ensuring sufficient chromatographic separation, then maximizing the amount of precursor ion entering the mass spectrometer, and finally generating high amounts of selected product ions. The experiments are performed on a linear ion trap mass spectrometer. Retention mapping using the band-tracking model is applied during LC development, which facilitates the optimization of segmented gradients. A set of different siderophores, strong iron chelates, is used as the model substances.

Journal Article↗

Optimization of near ultraviolet irradiation conditions for isolation of relaxed mutants of Escherichia coli.

We describe optimized conditions for isolation of relaxed mutants of bacteria with 4-thiouridine-containing tRNAs. The results presented here imply that besides the knowledge of the action spectra for near UV-induced growth inhibition of stringent and relaxed cells the fluence--fluence rate dependence of growth inhibition is an essential factor for optimizing the enrichment of relaxed mutants. We investigated systematically the dependence of growth inhibition of both stringent and relaxed strains of E. coli on the wavelength lambda, on the fluence F and on the fluence rate I within the ranges lambda = 301-365 nm, F = 0-48 kJ X m-2 and I = 0-60 W X m-2. The optimized conditions for selection of relaxed mutants of E. coli are lambda = 334 nm, F = 48 kJ X m-2 and I = 60 W X m-2. These optimized parameters determined for E. coli may be used in general to select relaxed mutants of other bacterial species by combining the turbidostat technique with near UV irradiation (Riesenberg et al. 1983).

Bacteriological Techniques↗

Simultaneous optimization based on artificial neural networks in ketoprofen hydrogel formula containing O-ethyl-3-butylcyclohexanol as percutaneous absorption enhancer.

The influence of the amounts of additives including 1-O-ethyl-3-n-butylcyclohexanol (OEBC), diisopropyl adipate (DIA), and isopropanol (IPA) on the penetration rate (R(p)) of ketoprofen from hydrogels through rat skin in vivo was investigated. Skin irritation evoked by the application of hydrogels was evaluated based on a microscopic observation of skin cross-sections. Both optimization techniques incorporating an artificial neural network (ANN) and a second-order polynomial regression analysis were applied to the optimization of ketoprofen hydrogel formulations. Findings indicated that the R(p) and total irritation score (TIS) of the skin were predicted quantitatively as a function of quantities of OEBC, DIA, and IPA, employing ANN. In contrast, the prediction ability of the polynomial regression equation was somewhat poorer compared with that of ANN. The observed results of R(p) and TIS in the optimal formulation coincided well with the predictions in the simultaneous optimization technique incorporating ANN.

Animals↗

Optimization of a high-performance liquid chromatography system by artificial neural networks for separation and determination of antioxidants.

A high-performance liquid chromatography (HPLC) system was used to determine the antioxidants tert-butyl-hydroquinone (TBHQ), tert-butylhydroxyanisole (BHA), and 3,5-di-tert-butylhydroxytoluene (BHT) simultaneously in oils. The paper presents a new methodology for the optimized separation of antioxidants in oils based on the coupling of experimental design and artificial neural networks. The orthogonal design and the artificial neural networks with extended delta-bar-delta (EDBD) learning algorithm were employed to design the experiments and optimize the variables. The response function (Rf) used was a weighted linear combination of two variables related to separation efficiency and retention time, according to which the optimized conditions were obtained. The above-mentioned antioxidants in rapeseed oils were separated and determined simultaneously under optimized conditions by HPLC with UV detection at 280 nm. Linearity was obtained over the range of 10-200 microg/mL with recoveries of 98.3% (TBHQ), 98.1% (BHT), and 96.2% (BHA).

Antioxidants↗