A multiplex single nucleotide polymorphism genotyping method using ligase‐based mismatch discrimination and CE‐SSCP

Accuracy, simplicity, and cost‐effectiveness are the most important criteria for a genotyping method for SNPs compatible with clinical use. One method developed for SNP genotyping, ligase‐based discrimination, is considered the simplest for clinical diagnosis. However, multiplex assays using this method are limited by the detection method. Although CE has been introduced as an alternative to error prone microarray‐based detection, the design process and multiplex assay procedure are complicated because of the DNA size‐dependent separation principle. In this study, we developed a simple and accurate multiplex genotyping method using reaction condition‐optimized ligation and high‐resolution CE‐based SSCP. With this high‐resolution CE‐SSCP system, we are able to use similar‐sized probes, thereby eliminating the complex probe design step and simplifying the optimization process. We found that this method could accurately discriminate single‐base mismatches in SNPs of the tp53 gene, used as targets for multiplex detection.     

Sparse models by iteratively reweighted feature scaling: a framework for wavelength and sample selection

In the past decade, there has been an increase in the use of sparse multivariate calibration methods in chemometrics. Sparsity describes a parsimonious state of model complexity and can be defined in terms of a subset of samples or covariates (e.g., wavelengths) that are used to define the calibration model. With respect to their classical counterparts such as principal component regression or partial least squares, sparse models are more easily interpretable and have been shown to exhibit non‐inferior prediction performance. However, sparse methods are still not as fast as the classical methods in spite of recent numerical advances. In addition, for many chemometricians, sparse methods are still “black‐box” algorithms whose internal workings are not well understood. In this paper, we describe a simple framework whereby classical multivariate calibration methods can be iteratively used to generate sparse models. Moreover, this approach allows for either wavelength or sample sparsity. We demonstrate the effectiveness of this approach on two spectroscopic data sets. Copyright © 2013 John Wiley & Sons, Ltd.     

A High‐Throughput Fluorescence Assay to Determine the Activity of Tryptophan Halogenases

Biocatalytic halogenation with tryptophan halogenases is hampered by severe limitations such as low activity and stability. These drawbacks can be overcome by directed evolution, but for screening large mutant libraries, a facile high‐throughput method is required. Therefore, we developed a quantitative halogenase assay based on a Suzuki–Miyaura cross‐coupling towards the formation of a fluorescent aryltryptophan. The technique was optimized for application in crude E. coli lysate without intermediary purification steps, and was used for quantitatively monitoring the formation of halogenated tryptophans with high specificity by facile fluorescence screening in microtiter plates. This novel screening approach was exploited to engineer a thermostable tryptophan 6‐halogenase. Libraries were constructed by error‐prone PCR and selected for improved thermal resistance simply by fluorogenic cross‐coupling. Our method led to an enzyme variant with substantially increased thermal stability and 2.5‐fold improved activity.     

Modeling structural recovery in glasses: An analysis of the peak‐shift method

Amorphous polymers below their glass‐transition temperature are inherently not at equilibrium. As a result, their structures continuously relax in an attempt to reach the equilibrium state. The current models of structural recovery can quantitatively describe the process. One of the parameters needed for the models is the nonlinearity parameter x. It has been proposed that x can be obtained from experimental data with the so‐called peak‐shift method. In this work, we use the Tool–Narayanaswamy–Moynihan model to identify the factors that determine the accuracy of the peak‐shift method and to quantify the errors in the value of x obtained from the peak‐shift method. In addition, we determine the influence of the error in x on the evaluation of the nonexponential model parameter β. Finally, the peak‐shift method is compared with the traditional curve‐fitting method for model parameter determination. © 2002 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 40: 2027–2036, 2002     

Highly multiplex and sensitive SNP genotyping method using a three‐color fluorescence‐labeled ligase detection reaction coupled with conformation‐sensitive CE

For the development of clinically useful genotyping methods for SNPs, accuracy, simplicity, sensitivity, and cost‐effectiveness are the most important criteria. Among the methods currently being developed for SNP genotyping technology, the ligation‐dependent method is considered the simplest for clinical diagnosis. However, sensitivity is not guaranteed by the ligation reaction alone, and analysis of multiple targets is limited by the detection method. Although CE is an attractive alternative to error‐prone hybridization‐based detection, the multiplex assay process is complicated because of the size‐based DNA separation principle. In this study, we employed the ligase detection reaction coupled with high‐resolution CE‐SSCP to develop an accurate, sensitive, and simple multiplex genotyping method. Ligase detection reaction could amplify ligated products through recurrence of denaturation and ligation reaction, and SSCP could separate these products according to each different structure conformation without size variation. Thus, simple and sensitive SNP analysis can be performed using this method involving the use of similar‐sized probes, without complex probe design steps. We found that this method could not only accurately discriminate base mismatches but also quantitatively detect 37 SNPs of the tp53 gene, which are used as targets in multiplex analysis, using three‐color fluorescence‐labeled probes.     

The comparison of detailed chemical kinetic mechanisms; forward versus reverse rates with CHEMRev

The computation of the reverse rate of an elementary reaction given the forward rate and requisite thermodynamic data is well established. Nevertheless the procedure is extremely laborious and error prone, in particular, when large numbers of such calculations must be performed and the results fitted to traditional or extended forms of the Arrhenius rate constant equation. We have developed a software application, CHEMRev, which automatically performs such computations for Chemkin‐style reaction mechanisms. © 2005 Wiley Periodicals, Inc. Int J Chem Kinet 37: 119–125, 2005     

Prediction of physical–chemical properties of crude oils by 1H NMR analysis of neat samples and chemometrics

In this work, we report the feasibility study to predict the properties of neat crude oil samples from 300‐MHz NMR spectral data and partial least squares (PLS) regression models. The study was carried out on 64 crude oil samples obtained from 28 different extraction fields and aims at developing a rapid and reliable method for characterizing the crude oil in a fast and cost‐effective way. The main properties generally employed for evaluating crudes' quality and behavior during refining were measured and used for calibration and testing of the PLS models. Among these, the UOP characterization factor K (K_UOP) used to classify crude oils in terms of composition, density (D), total acidity number (TAN), sulfur content (S), and true boiling point (TBP) distillation yields were investigated. Test set validation with an independent set of data was used to evaluate model performance on the basis of standard error of prediction (SEP) statistics. Model performances are particularly good for K_UOP factor, TAN, and TPB distillation yields, whose standard error of calibration and SEP values match the analytical method precision, while the results obtained for D and S are less accurate but still useful for predictions. Furthermore, a strategy that reduces spectral data preprocessing and sample preparation procedures has been adopted. The models developed with such an ample crude oil set demonstrate that this methodology can be applied with success to modern refining process requirements. Copyright © 2012 John Wiley & Sons, Ltd.     

Error surface calculations on the parameters defining oxygen depth profile models fitted to ARXPS data obtained from polystyrene exposed to an oxygen/helium plasma

Error surfaces are calculated for the fitting of concentration–depth profiles to angle‐resolved x‐ray photoelectron spectroscopy (ARXPS) data. The shapes of the error surfaces indicate that model parameters related to composition (especially at the very surface of the sample) are well constrained by the data, whereas parameters related to depth have a less significant impact on the fit. It is then shown that certain parameters in the different depth profile models employed are highly correlated and that the different models convey essentially the same information in different ways. Finally, a compromise profile definition is proposed for the fitting of constrained but flexible depth profiles to ARXPS data. Copyright © 2003 John Wiley & Sons, Ltd.     

Linking molecular models with ion mobility experiments. Illustration with a rigid nucleic acid structure

Ion mobility spectrometry experiments allow the mass spectrometrist to determine an ion's rotationally averaged collision cross section Ω_EXP. Molecular modelling is used to visualize what ion three‐dimensional structure(s) is(are) compatible with the experiment. The collision cross sections of candidate molecular models have to be calculated, and the resulting Ω_CALC are compared with the experimental data. Researchers who want to apply this strategy to a new type of molecule face many questions: (1) What experimental error is associated with Ω_EXP determination, and how to estimate it (in particular when using a calibration for traveling wave ion guides)? (2) How to generate plausible 3D models in the gas phase? (3) Different collision cross section calculation models exist, which have been developed for other analytes than mine. Which one(s) can I apply to my systems? To apply ion mobility spectrometry to nucleic acid structural characterization, we explored each of these questions using a rigid structure which we know is preserved in the gas phase: the tetramolecular G‐quadruplex [dTGGGGT]₄, and we will present these detailed investigation in this tutorial. © 2015 The Authors. Journal of Mass Spectrometry published by John Wiley & Sons Ltd.     

Application of genetic algorithm‐support vector regression (GA‐SVR) for quantitative analysis of herbal medicines

In this paper, a genetic algorithm‐support vector regression (GA‐SVR) coupled approach was proposed for investigating the relationship between fingerprints and properties of herbal medicines. GA was used to select variables so as to improve the predictive ability of the models. Two other widely used approaches, Random Forests (RF) and partial least squares regression (PLSR) combined with GA (namely GA‐RF and GA‐PLSR, respectively), were also employed and compared with the GA‐SVR method. The models were evaluated in terms of the correlation coefficient between the measured and predicted values (Rp), root mean square error of prediction, and root mean square error of leave‐one‐out cross‐validation. The performance has been tested on a simulated system, a chromatographic data set, and a near‐infrared spectroscopic data set. The obtained results indicate that the GA‐SVR model provides a more accurate answer, with higher Rp and lower root mean square error. The proposed method is suitable for the quantitative analysis and quality control of herbal medicines. Copyright © 2012 John Wiley & Sons, Ltd.     

Assessment of maximum likelihood PCA missing data imputation

Maximum likelihood principal component analysis (MLPCA) was originally proposed to incorporate measurement error variance information in principal component analysis (PCA) models. MLPCA can be used to fit PCA models in the presence of missing data, simply by assigning very large variances to the non‐measured values. An assessment of maximum likelihood missing data imputation is performed in this paper, analysing the algorithm of MLPCA and adapting several methods for PCA model building with missing data to its maximum likelihood version. In this way, known data regression (KDR), KDR with principal component regression (PCR), KDR with partial least squares regression (PLS) and trimmed scores regression (TSR) methods are implemented within the MLPCA method to work as different imputation steps. Six data sets are analysed using several percentages of missing data, comparing the performance of the original algorithm, and its adapted regression‐based methods, with other state‐of‐the‐art methods. Copyright © 2016 John Wiley & Sons, Ltd.     

A new uniform mapping algorithm for sample selection

A new algorithm for sample selection in the field of experimental design is presented. This method was designed for a multivariate data set of samples where the variables' levels are pre‐determined by the system instead of being determined by the practitioner and especially appropriate for non‐symmetrical multivariable space. Symmetrical experimental designs are not applicable in either of the cases. Anyway, this method is also applicable to symmetrical designs. Several tests have been made for symmetrical and non‐symmetrical cases and also about an already treated real case, comparing the new algorithm with others previously known in literature. Its performance has been checked considering several evaluation quality criteria like D‐optimal, A‐optimal, G‐optimal and models error prediction. Copyright © 2008 John Wiley & Sons, Ltd.     

Total Synthesis of Nominal Gobienine A

The lichen‐derived glycoconjugate gobienine A is structurally more complex than most glycolipids isolated from higher plants by virtue of the all‐cis substituted γ‐lactone substructure embedded into its macrocyclic frame. A concise entry into this very epimerization‐prone and hence challenging structural motif is presented, which relies on an enantioselective cyanohydrin formation, an intramolecular Blaise reaction, a palladium‐catalyzed alkoxycarbonylation, and a diastereoselective hydrogenation of the tetrasubstituted alkene in the resulting butenolide. This strategy, in combination with ring‐closing olefin metathesis for the formation of the macrocyclic perimeter, allowed the proposed structure of gobienine A ( 1 ) to be formed in high overall yield. The recorded spectral data show that the structure originally attributed to gobienine A is incorrect and that it is not the epimerization‐prone ester site on the butanolide ring that is the locus of misassignment; rather, the discrepancy must be more profound.     

A wavelength selection method based on random decision particle swarm optimization with attractor for near‐infrared spectral quantitative analysis

In this paper, we proposed a wavelength selection method based on random decision particle swarm optimization with attractor for near‐infrared (NIR) spectra quantitative analysis. The proposed method was incorporated with partial least square (PLS) to construct a prediction model. The proposed method chooses the current own optimal or the current global optimal to calculate the attractor. Then the particle updates its flight velocity by the attractor, and the particle state is updated by the random decision with the new velocity. Moreover, the root‐mean‐square error of cross‐validation is adopted as the fitness function for the proposed method. In order to demonstrate the usefulness of the proposed method, PLS with all wavelengths, uninformative variable elimination by PLS, elastic net, genetic algorithm combined with PLS, the discrete particle swarm optimization combined with PLS, the modified particle swarm optimization combined with PLS, the neighboring particle swarm optimization combined with PLS, and the proposed method are used for building the components quantitative analysis models of NIR spectral datasets, and the effectiveness of these models is compared. Two application studies are presented, which involve NIR data obtained from an experiment of meat content determination using NIR and a combustion procedure. Results verify that the proposed method has higher predictive ability for NIR spectral data and the number of selected wavelengths is less. The proposed method has faster convergence speed and could overcome the premature convergence problem. Furthermore, although improving the prediction precision may sacrifice the model complexity under a certain extent, the proposed method is overfitted slightly. Copyright © 2015 John Wiley & Sons, Ltd.     

Rapid parameterization of small molecules using the force field toolkit

The inability to rapidly generate accurate and robust parameters for novel chemical matter continues to severely limit the application of molecular dynamics simulations to many biological systems of interest, especially in fields such as drug discovery. Although the release of generalized versions of common classical force fields, for example, General Amber Force Field and CHARMM General Force Field, have posited guidelines for parameterization of small molecules, many technical challenges remain that have hampered their wide‐scale extension. The Force Field Toolkit (ffTK), described herein, minimizes common barriers to ligand parameterization through algorithm and method development, automation of tedious and error‐prone tasks, and graphical user interface design. Distributed as a VMD plugin, ffTK facilitates the traversal of a clear and organized workflow resulting in a complete set of CHARMM‐compatible parameters. A variety of tools are provided to generate quantum mechanical target data, setup multidimensional optimization routines, and analyze parameter performance. Parameters developed for a small test set of molecules using ffTK were comparable to existing CGenFF parameters in their ability to reproduce experimentally measured values for pure‐solvent properties (<15% error from experiment) and free energy of solvation (±0.5 kcal/mol from experiment). © 2013 Wiley Periodicals, Inc.     

ForConX: A forcefield conversion tool based on XML

The force field conversion from one MD program to another one is exhausting and error‐prone. Although single conversion tools from one MD program to another exist not every combination and both directions of conversion are available for the favorite MD programs Amber , Charmm , Dl‐ Poly , Gromacs , and Lammps . We present here a general tool for the force field conversion on the basis of an XML document. The force field is converted to and from this XML structure facilitating the implementation of new MD programs for the conversion. Furthermore, the XML structure is human readable and can be manipulated before continuing the conversion. We report, as testcases, the conversions of topologies for acetonitrile, dimethylformamide, and 1‐ethyl‐3‐methylimidazolium trifluoromethanesulfonate comprising also Urey–Bradley and Ryckaert–Bellemans potentials. © 2017 Wiley Periodicals, Inc.     

Optimization of matrix assisted desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) for the characterization of Bacillus and Brevibacillus species

Over the past few decades there has been an increased interest in using various analytical techniques for detecting and identifying microorganisms. More recently there has been an explosion in the application of matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS) for bacterial characterization, and here we optimize this approach in order to generate reproducible MS data from bacteria belonging to the genera Bacillus and Brevibacillus. Unfortunately MALDI-TOF-MS generates large amounts of data and is prone to instrumental drift. To overcome these challenges we have developed a preprocessing pipeline that includes baseline correction, peak alignment followed by peak picking that in combination significantly reduces the dimensionality of the MS spectra and corrects for instrument drift. Following this two different prediction models were used which are based on support vector machines and these generated satisfactory prediction accuracies of approximately 90%.     

Major groove substituents and polymerase recognition of a class of predominantly hydrophobic unnatural base pairs

Expansion of the genetic alphabet with an unnatural base pair is a long‐standing goal of synthetic biology. We have developed a class of unnatural base pairs, formed between d 5SICS and analogues of d MMO2 that are efficiently and selectively replicated by the Klenow fragment (Kf) DNA polymerase. In an effort to further characterize and optimize replication, we report the synthesis of five new d MMO2 analogues bearing different substituents designed to be oriented into the developing major groove and an analysis of their insertion opposite d 5SICS by Kf and Thermus aquaticus DNA polymerase I (Taq). We also expand the analysis of the previously optimized pair, d NaM –d 5SICS , to include replication by Taq. Finally, the efficiency and fidelity of PCR amplification of the base pairs by Taq or Deep Vent polymerases was examined. The resulting structure–activity relationship data suggest that the major determinants of efficient replication are the minimization of desolvation effects and the introduction of favorable hydrophobic packing, and that Taq is more sensitive than Kf to structural changes. In addition, we identify an analogue (d NMO1 ) that is a better partner for d 5SICS than any of the previously identified d MMO2 analogues with the exception of d NaM . We also found that d NaM –d 5SICS is replicated by both Kf and Taq with rates approaching those of a natural base pair.