The dipole approach in the lon-interaction chromatography of zwitterions— Use of a potential approximation to obtain a simplified retention equation

Summary In ion-interaction chromatography (IIC) the zwitterionic analyte is believed to interact with the stationary phase potential as a result of its electrical dipole, and to compete with the ion-interaction reagent (IIR) for available inner layer sites. The most reliable set of literature data relating to the retention behavior of zwitterions in IIC has been used for successful testing of a high surface-potential approximation which enables the fitting of retention data without detailed information about the physical and chemical properties of the mobile phase. This approach can explain quantitatively, very simply, the retention behavior of zwitterions in IIC.     

The dipole approach in ion-interaction chromatography of zwitterions

Summary The chromatographic behavior of zwitterions in Ion-interaction chromatography (IIC) is, investigated theoretically for the first time. The modification of the stationary phase in the presence of Ion-interaction reagent (IIR), and adsorption competition between test analytes and IIR for inner layer sites are shown theoretically to change the partition coefficient for zwitterions. Experimental results from the literature concerning retention behavior of zwitterions in IIC, were used to test the new thermodynamic theory. Very reasonable estimates of (i) ΔG ^o values for the IIR adsorption onto the stationary phase (II) total ligand concentration, and (iii) dipolar moments validate the present thermodynamic model for the IIC of zwitterionic analytes. Retention equations are compared to those which can be obtained, if the net charge of the analyte is zero, from the most important retention models in IIC. None of them is able to explain, even in a qualitative way, the retention behavior of zwitterions in IIC whereas, the present model is quantitatively able to do this.     

Ion-interaction chromatography of neutral molecules

Summary The chromatographic behavior of neutral molecules in ion-interaction chromatography (IIC) is investigated theoretically. The physical and chemical modification of the stationary phase in the presence of ion interaction Reagent (IIR) in the eluent, and adsorption competition between test analytes and IIR for inner layer sites are shown theoretically to change the partition coefficient of neutral molecules. The most reliable, literature experimental results, concerming retention behaviour of neutral molecules in IIC, were used to test the new theory. The wide variability among them was elucidated on the basis of the exhaustive retention model developed. Retention equations were compared to those which can be obtained, if the change of the analyte is zero, from the most important retention model in IIC.     

3D-QSAR Studies on 3-Substituted N-Benzhydryl-nortropane Analogs as Nociception Receptor Agonists

The nociceptin receptor(NOP) has been involved in multiple biological functions, including pain, anxiety, cough, substance abuse, cardiovascular control, and immunity. Thus, selective NOP agonists might have clinical potential for the treatment of related diseases. In the present work, three-dimensional quantitative structure-activity relationship(3D-QSAR) studies were performed on a series of 3-substituted N-benzhydryl-nortropane analogs as NOP agonists using comparative molecular field analysis(Co MFA) and comparative molecular similarity indices analysis(CoM SIA) techniques. The statistically significant models were obtained with 54 compounds in training set by ligand-based atom-by-atom matching alignment. The CoM FA model gave cross-validated coefficient(q2) value of 0.530 using 6 components, non-cross-validated(r2) value of 0.921 with estimated F value of 93.668, and standard error of estimate(SEE) of 0.185. The best Co MSIA model resulted in q2 = 0.592, r2 = 0.945, N = 10, SEE = 0.162, and F = 75.654, based on steric, electrostatic, hydrophobic and hydrogen bond acceptor fields. The predictive ability of the Co MFA and CoM SIA models was further validated using a test set of 18 molecules that were not included in the training set, which resulted in predictive correlation coefficients(r2pred) of 0.551 and 0.637, respectively. Moreover, the CoM FA and CoM SIA contour maps identified the features important for exhibiting potent binding affinities on NOP, and can thus serve as a useful guide for the design of potential NOP agonists.     

3D-QSAR Analysis of a Series of 1,2,3-Triazole-chromenone Derivatives as an Acetylcholinesterase Inhibitor against Alzheimer's Disease

Chromenones have attracted much attention since they are excellent acetylcholinesterase inhibitor(AChEi). The 1,2,3-triazoles are multifunctional anti-acetylcholinesterase(AChE) agents. In this paper, we report the three-dimensional quantitative structure-activity relationship(3D-QSAR) study of 25 1,2,3-triazolechromenone derivatives based comparative molecular field analysis(CoMFA) and comparative molecular similarity indices analysis(CoMSIA). To construct CoMFA and CoMSIA models, the 25 active molecules were randomly divided into the training and test sets. The obtained cross-validation Q~2 of the CoMFA model, the coefficient of non-cross-validation R~2, and the test value F are 0.597, 0.994, and 396.726, respectively. The cross-validation Q~2 of the CoMSIA model, the coefficient of the non-cross-validation R~2, and the test value F are 0.721, 0.979, and 131.107, respectively. The predictive correlation coefficient(r_(pred)~2) is 0.728 for CoMFA and 0.805 for CoMSIA, which verifies that the model is predictable. Based on the potential maps of CoMFA and CoMSIA, a library containing a set of potent AChEi was designed. The inhibitory potential of the compounds in this library was found to be greater than the inhibitory potential of the most active compounds in the data set. The results obtained from this study laid the foundation for the development of effective drugs for AChEi.     

Quantitative Point of Care Tests for Timely Diagnosis of Early-Onset Preeclampsia with High Sensitivity and Specificity

Preeclampsia is a heterogeneous and multiorgan cardiovascular disorder of pregnancy. Here, we report the development of a novel strip-based lateral flow assay (LFA) using lanthanide-doped upconversion nanoparticles conjugated to antibodies targeting two different biomarkers for detection of preeclampsia. We first measured circulating plasma FKBPL and CD44 protein concentrations from individuals with early-onset preeclampsia (EOPE), using ELISA. We confirmed that the CD44/FKBPL ratio is reduced in EOPE with a good diagnostic potential. Using our rapid LFA prototypes, we achieved an improved lower limit of detection: 10 pg ml⁻¹ for FKBPL and 15 pg ml⁻¹ for CD44, which is more than one order lower than the standard ELISA method. Using clinical samples, a cut-off value of 1.24 for CD44/FKBPL ratio provided positive predictive value of 100 % and the negative predictive value of 91 %. Our LFA shows promise as a rapid and highly sensitive point-of-care test for preeclampsia.     

QSAR as a random event: criteria of predictive potential for a chance model

The CORAL software (http://www.insilico.eu/coral) was suggested as a tool to build up quantitative structure–property/activity relationships (QSPRs/QSARs). This software is based on conception “a QSPR/QSAR model should be interpreted as a random event.” This is reflection of fact: different distributions into the training set (substances involved in modeling process) and the validation set (substances, which are not known at the moment of the modeling process) give models with significant dispersion in the statistical quality of the QSPR/QSAR. Results of experiments with the software and possible ways of further improvement of this software are discussed. The most attractive new ways to estimate predictive potential of the CORAL model seem to be the following ones: (i) index of ideality of correlation and (ii) correlation contradiction index. These can be also proposed as criteria of predictive potential for arbitrary QSPR/QSAR.

     

Extended thermodynamic approach to ion interaction chromatography. A mono- and bivariate strategy to model the influence of ionic strength

Recent breakthroughs in the theory of ion interaction chromatography (IIC) permit new analyses of the dependence of retention on different interdependent factors. The influence of the ionic strength / on the surface potential, the Donnan effect, and salting effects are taken into account to model the chromatographic behaviour of charged analytes in IIC. The most reliable experimental results found in the literature were used to test the retention equations that were developed following both a monovariate (/ changes as the concentration of H, ion interaction reagent, changes) and a bivariate (/ changes because of the simultaneous variation of H and of the background electrolyte concentrations) approach. The present extended thermodynamic model builds on the sound intuition of the electrostatic approach and proves to provide the most successful and exhaustive quantitative explanation of experimental evidence. It is also able to rationalise the less extensive agreement between the pure electrostatic approach predictions and experimental results. The adequacy of the model is supported by physically reliable estimates of the adjustable constant (ion-pair constants, deltaG degrees). Moreover statistical practice demonstrates that all the adjustable parameters (three at most) are statistically significant. A linear, zero crossing function with unit slope is obtained when k(pred) is plotted against k(exp). The mean percent error between k(pred) and k(exp) is 4.5% at most. In the absence of H the present retention equation reduces, as expected, to the relationship that describes the influence of / on the retention behaviour in reversed-phase liquid chromatography.     

Index of Ideality of Correlation: new possibilities to validate QSAR: a case study

New criterion of the predictive potential of quantitative structure – property/activity relations (QSPRs/QSARs) named Index of Ideality of Correlation (IIC) is suggested. This criterion is calculated using the correlation coefficient between experimental and calculated values of an endpoint for the calibration set, taking into account positive and negative differences between experimental and calculated values of the endpoint. Using this criterion improves the predictive potential of QSAR models of toxicity towards fathead minnow (Pimephales promelas). Comparison of IIC with other metrics of predictive potential shows that (i) the IIC is not identic to other metrics; and (ii) the IIC seems more reliable criterion at least for examined data on toxicity towards fathead minnow.     

Prediction of the acid value,peroxide value and the percentage of some fatty acids in edible oils during long heating time by chemometrics analysis of FTIR-ATR spectra

Edible oils are used in the preparation of foods as a part of their recipe or for frying. So to ensure of food safety, checking the quality of the oils before and after usage is an important subject in food control laboratories. In this study, edible oils from four different sources (canola, corn, sunflower and frying) were heated for 36 h at 170 °C and sampling was done every 6 h. The free fatty acid, peroxide value and the content of some fatty acids (C16:0, C18:0, C18:1, C18:2, C18:3) of the oil samples were determined by standard methods. Then, the ATR-FTIR spectra of the samples were collected. The partial least squares (PLS) regression combined with genetic algorithm was performed on the spectroscopic data to obtain the appropriate predictive models for the simultaneous estimation of acid value, peroxide value and the percentage of five kinds of fatty acids. The effect of some preprocessing methods on these models was also investigated. Preprocessing of data by orthogonal signal correction (OSC) resulted in the best predictive models for all oil properties. The correlation coefficients of calibration set (>0.99) and validation set (>0.86 and in most case >0.94) of the OSC–PLS model suggested suitable predictive modeling for all studied parameters in the oil samples. This method could be suggested as a rapid, economical and environmental friendly technique for simultaneous determination of seven noted parameters in the edible oils.     

The bacterial phosphoenolpyruvate: sugar phosphotransferase system (PTS): an interface between energy and signal transduction

The bacterial phosphoenolpyruvate (PEP): sugar phosphotransferase system (PTS) mediates the uptake and phosphorylation of carbohydrates, and is involved in signal transduction. It comprises two general phosphotransferase proteins (EI and HPr) and a—species dependent—variable number of sugar-specific enzyme II complexes (IIA, IIB, IIC). EI and HPr transfer phosphoryl groups from PEP to the IIA units. IIA and IIB sequentially transfer phosphates to the sugar, which is translocated by the IIC unit. The ratio of phosphorylated to non-phosphorylated IIA and IIB varies with transport activity, and the phosphorylation state of some of the IIA and IIB serves as signal input for regulation of catabolite repression, intermediate metabolism, gene expression and chemotaxis in response to the availability of carbohydrates and PEP (glycolytic activity). PTS occur in about one-third of all eubacteria and in a few archaebacteria but not in animals and plants. Uniqueness and pleiotropic function make the PTS a potential target for anti-infectives. The PTS transporter for mannose is utilized as a gate for the penetration of bacteriophage lambda DNA across, and insertion of certain bacteriocins (small antimicrobial peptides) into the inner membrane. The PTS of Escherichia coli is in the focus of this review, but occasionally comparisons with other species are made. The topics are: History; Modular design of the E. coli PTS; Structure function and catalytic mechanism of the protein modules; Regulation of and by the PTS; The PTS in pathogenicity and virulence; Computational models; Metabolic engineering.     

External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean

The external prediction capability of quantitative structure-activity relationship (QSAR) models is often quantified using the predictive squared correlation coefficient, q (2). This index relates the predictive residual sum of squares, PRESS, to the activity sum of squares, SS, without postprocessing of the model output, the latter of which is automatically done when calculating the conventional squared correlation coefficient, r (2). According to the current OECD guidelines, q (2) for external validation should be calculated with SS referring to the training set activity mean. Our present findings including a mathematical proof demonstrate that this approach yields a systematic overestimation of the prediction capability that is triggered by the difference between the training and test set activity means. Example calculations with three regression models and data sets taken from literature show further that for external test sets, q (2) based on the training set activity mean may become even larger than r (2). As a consequence, we suggest to always use the test set activity mean when quantifying the external prediction capability through q (2) and to revise the respective OECD guidance document accordingly. The discussion includes a comparison between r (2) and q (2) value ranges and the q (2) statistics for cross-validation.     

苯并咪唑类缓蚀剂的HQSAR研究及分子设计

采用分子全息定量构效关系(HQSAR)方法, 构建苯并咪唑衍生物在酸性环境中的缓蚀性能与结构之间的定量构效关系模型, 研究不同碎片区分参数及碎片大小对模型质量的影响, 寻找最优HQSAR模型, 并对其稳定性及预测能力进行评价. 结果显示: 选取碎片区分参数为原子类型(A)、化学键类型(B)、连接性(C)、氢原子(H)、手性(Ch)、氢键给体和受体(D&;A), 碎片大小为1-3 建模时, 得到的HQSAR 模型(r²(非交叉验证系数)=0.996, q²(交叉验证系数)=0.960, SE_cv(交叉验证标准误差)=3.709)具有良好的统计学稳定性及预测能力. 根据最优HQSAR模型图设计出的38种苯并咪唑类化合物理论上均具有较好的缓蚀性能. 本研究为油气田新型高效缓蚀剂研发提供可靠的理论依据.     

On the calculation of time correlation functions by potential scaling

We present and analyze a general method to calculate time correlation functions from molecular dynamics on scaled potentials for complex systems for which simulation is affected by broken ergodicity. Depending on the value of the scaling factor, correlations can be calculated for times that can be orders of magnitude longer than those accessible to direct simulations. We show that the exact value of the time correlation functions of the original system (i.e., with unscaled potential) can be obtained, in principle, using an action-reweighting scheme based on a stochastic path-integral formalism. Two tests (involving a bistable potential model and a dipeptide bond-vector orientational relaxation) are exemplified to showcase the strengths, as well as the limitations of the approach, and a procedure for the estimation of the time-dependent standard deviation error is outlined.