一种基于遗传算法的肽/蛋白质结合模式虚拟筛选建模技术

“合理”QSAR模型是指在了解配体与受体相互作用模式的前提下建立定量构效关系, 这样避开了传统做法仅仅依靠样本集分子自身信息来构建预测模型的诸多弊端. 本文将此思想应用于肽/蛋白质亲和活性的研究当中, 借助于遗传算法作为虚拟受体结合靶点及相互作用模式的筛选手段得到了一种新的建模技术: 肽/蛋白质结合模式遗传虚拟筛选(genetic virtual screening of combinative mode for peptide/protein, GVSC). 该法成功解决了“合理”QSAR研究中的难题, 即大多数情况下受体结构未知而难以了解配基与之发生的结合方式. 分别使用58个血管紧张素转化酶, 18个Camel抗体蛋白cAb-lys3双位点突变残基对GVSC加以检验, 其结果表明GVSC能够较好地阐明配基与受体之间的作用机理, 并能得到优于传统方法的QSAR模型.     

Simultaneous Determination of Paracetamol,Ibuprofen, and Caffeine in Tablets by Molecular Absorption Spectroscopy Combined with Classical Least Square Method

In this paper, the classical least-squares (CLS) method with molecular absorption spectrophotometric measurement was used to determine simultaneously paracetamol (PAR), ibuprofen (IBU), and caffeine (CAF) in tablets. The absorbance spectra of the standard solutions and samples were measured over a wavelength from 220 to 300 nm with a 0.5 nm step. The concentration of PAR, IBU, and CAF in the sample solutions was calculated by using Visual Basic for Applications (VBA) and a program called CLS-Excel written in Microsoft Excel 2016. The method and the CLS-Excel program were tested on mixed standard laboratory samples with different PAR, IBU, and CAF concentration ratios, and they showed only small errors and a satisfying repeatability. An analytical procedure for tablets containing PAR, IBU, and CAF was developed. The reliability of the procedure was proved via the recovery and repeatability of the analysis results with an actual tablet sample and by comparing the mean contents of active substances in the tablets obtained from the analytical procedure with the HPLC method. The procedure is simple with a reduced cost compared with the HPLC standard method.     

Determination of enantiomeric composition of samples by multivariate regression modeling of spectral data obtained with cyclodextrin guest-host complexes-Effect of an achiral surfactant and use of mixed cyclodextrins

The determination of the enantiomeric composition of samples by chemometric modeling of spectral data was investigated for samples of N,N′-bis-(-methylbenzyl) sulfamide and tryptophan methyl ester hydrochloride. Multivariate regression models (PLS-1) were developed from spectral data obtained on solutions containing N,N′-bis-(-methylbenzyl)sulfamide or tryptophan methyl ester hydrochloride in the presence of sodium dodecyl sulfate and mixed cyclodextrin host molecules. The regression models were subsequently used to predict the enantiomeric composition of laboratory-prepared test samples of N,N′-bis(-methylbenzyl)sulfamide or tryptophan methyl ester hydrochloride. The capability of the models to accurately predict the enantiomeric composition was evaluated in terms of the root-mean-square percent relative error (RMS %R.E.) as calculated from the results obtained with independently prepared validation sets of samples. It was found that the presence of SDS in most cases either had little effect on the predictive ability of the model or it actually reduced the predictive ability of the model. Moreover, it was found that the use of mixed CDs, either in the presence or absence of SDS, reduced the predictive ability of the regression model when compared with results obtained with individual CDs.     

Quantification of endocrine disruptors and pesticides in water by gas chromatography–tandem mass spectrometry. Method validation using weighted linear regression schemes

A multi-residue methodology based on a solid phase extraction followed by gas chromatography–tandem mass spectrometry was developed for trace analysis of 32 compounds in water matrices, including estrogens and several pesticides from different chemical families, some of them with endocrine disrupting properties. Matrix standard calibration solutions were prepared by adding known amounts of the analytes to a residue-free sample to compensate matrix-induced chromatographic response enhancement observed for certain pesticides. Validation was done mainly according to the International Conference on Harmonisation recommendations, as well as some European and American validation guidelines with specifications for pesticides analysis and/or GC–MS methodology. As the assumption of homoscedasticity was not met for analytical data, weighted least squares linear regression procedure was applied as a simple and effective way to counteract the greater influence of the greater concentrations on the fitted regression line, improving accuracy at the lower end of the calibration curve. The method was considered validated for 31 compounds after consistent evaluation of the key analytical parameters: specificity, linearity, limit of detection and quantification, range, precision, accuracy, extraction efficiency, stability and robustness.     

Application of affinity capillary electrophoresis for charge heterogeneity profiling of biopharmaceuticals

Charge heterogeneity profiling is important for the quality control (QC) of biopharmaceuticals. Because of the increasing complexity of these therapeutic entities [1], the development of alternative analytical techniques is needed. In this work, flow‐through partial‐filling affinity capillary electrophoresis (FTPFACE) has been established as a method for the analysis of a mixture of two similar monoclonal antibodies (mAbs). The addition of a specific ligand results in the complexation of one mAb in the co‐formulation, thus changing its migration time in the electric field. This allows the characterization of the charged variants of the non‐shifted mAb without interferences. Adsorption of proteins to the inner capillary wall has been circumvented by rinsing with guanidine hydrochloride before each injection. The presented FTPFACE approach requires only very small amounts of ligands and provides complete comparability with a standard CZE of a single mAb.     

Selection of Wavelength Range and Number of Factors to be Used in the PLS Treatment of Spectrophotometric Data

Abstract

A procedure for selection of wavelength range and number of factors to be used in partial least square calibration that involves the calculation of prediction residual sum of squares (PRESS) in different conditions is proposed. The best model takes into account the minimum PRESS value that does not show significant differences with respect to the corresponding model with fewer factors. The ability of the proposed method to minimize errors in partial least squares (PLS) prediction is demonstrated by applying it to the resolution of phenytoine (DPH) and phenobarbital (PB) binary mixtures with errors less than 2.8%; the results are compared with those obtained using another wavelength selection procedure. The ensuing method, which was validated by high performance liquid chromatography (HPLC), also gives good results with real samples (pharmaceutical preparations).     

Determination of Quercetin in Extracts of Ginkgo biloba L. Leaves by Near‐Infrared Reflectance Spectroscopy Based on Interval Partial Least‐Squares (iPLS) Model

Abstract

This paper developed a multivariate method of analysis of quercetin in Ginkgo biloba leaf extracts, based on reflectance NIR measurements and partial least squares regression. In order to give a better correlation with the results obtained by HPLC, multiplicative scatter correction (MSC) was utilized to correct scattering effect and interval partial least squares (iPLS) to select optimum wavelength region. In general, good calibration statistics were obtained for the prediction of quercetin content, as demonstrated by some figures of merit, namely linearity, repeatability, and accuracy. And the iPLS model was more reliable than the full model.     

Random forests for functional covariates

We propose a form of random forests that is especially suited for functional covariates. The method is based on partitioning the functions' domain in intervals and using the functions' mean values across those intervals as predictors in regression or classification trees. This approach appears to be more intuitive to applied researchers than usual methods for functional data, while also performing very well in terms of prediction accuracy. The intervals are obtained from randomly drawn, exponentially distributed waiting times. We apply our method to data from Raman spectra on boar meat as well as near‐infrared absorption spectra. The predictive performance of the proposed functional random forests is compared with commonly used parametric and nonparametric functional methods and with a nonfunctional random forest using the single measurements of the curve as covariates. Further, we present a functional variable importance measure, yielding information about the relevance of the different parts of the predictor curves. Our variable importance curve is much smoother and hence easier to interpret than the one obtained from nonfunctional random forests.