Density Prediction for Aqueous Multicomponent Solutions Obeying Isopiestic Relation and Isopycnotic Mixing Rule

The density predictive method based on isopiestic relation has been revised. The accuracy of density prediction based on the isopiestic mixing rule has been compared with that based on the isopycnotic mixing rule on ten aqueous ternary electrolyte systems, one quaternary electrolyte system, and one aqueous ternary nonelectrolyte system at 25°C. Of the ten ternary electrolyte systems, five systems show better predictive accuracy by the isopycnotic method, two by isopiestic relation, and three systems show comparable results. For the aqueous quaternary electrolyte and ternary nonelectrolyte systems, the isopycnotic method gives better predictive accuracy. The overall average error of density prediction based on isopiestic mixing rule is 0.00031, while that based on isopycnotic mixing rule is 0.00017.     

Neural network-based prediction of transmembrane beta-strand segments in outer membrane proteins

Prediction of transmembrane beta-strands in outer membrane proteins (OMP) is one of the important problems in computational chemistry and biology. In this work, we propose a method based on neural networks for identifying the membrane-spanning beta-strands. We introduce the concept of "residue probability" for assigning residues in transmembrane beta-strand segments. The performance of our method is evaluated with single-residue accuracy, correlation, specificity, and sensitivity. Our predicted segments show a good agreement with experimental observations with an accuracy level of 73% solely from amino acid sequence information. Further, the predictive power of N- and C-terminal residues in each segments, number of segments in each protein, and the influence of cutoff probability for identifying membrane-spanning beta-strands will be discussed. We have developed a Web server for predicting the transmembrane beta-strands from the amino acid sequence, and the prediction results are available at http://psfs.cbrc.jp/tmbeta-net/.     

Hypothetical AlF3 crystal structures

Applying a structure prediction computer programme (GRINSP=Geometrically Restrained INorganic Structure Prediction), the occurrence of 6-connected 3D networks was investigated, through AlF₆ octahedra exclusive corner sharing. The five known AlF₃ varieties were reproduced (α-, β-, η-, κ- and τ-AlF₃) and seven hypothetical models were predicted. Among these still to be synthesized AlF₃ phases, one can recognize two known structure types (TlCa₂Ta₅O₁₅, Ba₄CoTa₁₀O₃₀) and some easy to imagine intergrowths; however, a few others are completely unexpected, though simple. A comparison of the ab initio total energies of all the structures is provided, leading to the conclusion that the virtual models could well be viable.     

Prediction of retention values in linear temperature programming of narrow and mega-bore capillary columns

Retention times in linear temperature programmed gas chromatography on narrow bore and megabore capillary columns have been calculated from experimental retention times measured at three isothermal temperatures: an iterative procedure performed by BASIC programs was used to obtain the values of the constants enabling the calculation of the programmed temperature retention times. Different methods of calculation have been compared.     

Accelerated aging and lifetime prediction: Review of non-Arrhenius behaviour due to two competing processes

Lifetime prediction of polymeric materials often requires extrapolation of accelerated aging data with the suitability and confidence in such approaches being subject to ongoing discussions. This paper reviews the evidence of non-Arrhenius behaviour (curvature) instead of linear extrapolations in polymer degradation studies. Several studies have emphasized mechanistic variations in the degradation mechanism and demonstrated changes in activation energies but often data have not been fully quantified. To improve predictive capabilities a simple approach for dealing with curvature in Arrhenius plots is examined on a basis of two competing reactions. This allows for excellent fitting of experimental data as shown for some elastomers, does not require complex kinetic modelling, and individual activation energies are easily determined. Reviewing literature data for the thermal degradation of polypropylene a crossover temperature (temperature at which the two processes equally contribute) of 83 °C was determined, with the high temperature process having a considerably higher activation energy (107–156 kJ/mol) than the low temperature process (35–50 kJ/mol). Since low activation energy processes can dominate at low temperatures and longer extrapolations result in larger uncertainties in lifetime predictions, experiments focused on estimating E_a values at the lowest possible temperature instead of assuming straight line extrapolations will lead to more confident lifetime estimates.     

Prediction of programmed temperature retention times on linked capillary columns of different polarity

Systems formed by serial connection of capillary columns of different polarity were studied with methods previously used to predict the behavior of linked capillary columns under isothermal conditions and to obtain programmed temperature gas chromatography (PTGC) retention times of the individual columns starting from isothermal data. The two calculation methods were simultaneously applied in order to predict PTGC retention times of the series system starting from isothermal data obtained on the two individual columns. Experimental retention values measured using different temperature programs on the individual columns and on the series systems were found to agree with those calculated.     

Receptor-based 3D QSAR analysis of estrogen receptor ligands – merging the accuracy of receptor-based alignments with the computational efficiency of ligand-based methods

One of the major challenges in computational approaches to drug design is the accurate prediction of binding affinity of biomolecules. In the present study several prediction methods for a published set of estrogen receptor ligands are investigated and compared. The binding modes of 30 ligands were determined using the docking program AutoDock and were compared with available X-ray structures of estrogen receptor-ligand complexes. On the basis of the docking results an interaction energy-based model, which uses the information of the whole ligand-receptor complex, was generated. Several parameters were modified in order to analyze their influence onto the correlation between binding affinities and calculated ligand-receptor interaction energies. The highest correlation coefficient (r ² = 0.617, q ² _LOO = 0.570) was obtained considering protein flexibility during the interaction energy evaluation. The second prediction method uses a combination of receptor-based and 3D quantitative structure-activity relationships (3D QSAR) methods. The ligand alignment obtained from the docking simulations was taken as basis for a comparative field analysis applying the GRID/GOLPE program. Using the interaction field derived with a water probe and applying the smart region definition (SRD) variable selection, a significant and robust model was obtained (r ² = 0.991, q ² _LOO = 0.921). The predictive ability of the established model was further evaluated by using a test set of six additional compounds. The comparison with the generated interaction energy-based model and with a traditional CoMFA model obtained using a ligand-based alignment (r ² = 0.951, q ² _LOO = 0.796) indicates that the combination of receptor-based and 3D QSAR methods is able to improve the quality of the underlying model.     

Ab initio crystal structure prediction-I. Rigid molecules

A new methodology for the prediction of molecular crystal structures using only the atomic connectivity of the molecule under consideration is presented. The approach is based on the global minimization of the lattice enthalpy of the crystal. The modeling of the electrostatic interactions is accomplished through a set of distributed charges that are optimally and automatically selected and positioned based on results of quantum mechanical calculations. A four-step global optimization algorithm is used for the identification of the local minima of the lattice enthalpy surface. A parallelized implementation of the algorithm permits a much more extensive search of the solution space than has hitherto been possible, allowing the identification of crystal structures in less frequently occurring space groups and with more than one molecule in the asymmetric unit. The algorithm has been applied successfully to the prediction of the crystal structures of 3-aza-bicyclo(3.3.1)nonane-2,4-dione (P2(1)/a, Z' = 1), allopurinol (P2(1)/c, Z' = 1), 1,3,4,6,7,9-hexa-azacycl(3.3.3)azine (Pbca, Z' = 2), and triethylenediamine (P6(3)/m, Z' = 1). In all cases, the experimentally known structure is among the most stable predicted structures, but not necessarily the global minimum.     

Prediction of precision from signal and noise measurement in liquid chromatography: Mathematical relationship between integration domain and precision

Summary The precision of integration over noisy instrumental output for quantitative analysis is studied. A probability theory is developed to predict the relative standard deviation (RSD) of integration results over an integration domain from one-point integration (peak height measurement) to entire area integration in HPLC. Common integration modes of horizontal zero line and oblique zero line are taken into account, but no peak overlap is assumed. The question of the analytical superiority of peak height measurement or integration for quantitation is answered. In the HPLC apparatus used, the minimum RSD of measurements is found in the integration domain of ca. ±0.5 σ for analytes [peaks are approximated by the Gaussian signal of width, σ (standard deviation)]. The RSD of integration measurements is also shown to depend on the stochastic properties of background noise (uncorrelated noise and correlated 1/f type noise). The theoretical conclusion is verified by Monte Carlo simulation and HPLC experiments for some aromatic compounds. Second Part of series cited as Ref. [1].     

Prediction of internal standards in reversed-phase liquid chromatography: IV. Correlation and prediction of retention in reversed-phase ion-pair chromatography based on linear solvation energy relationships

This paper describes the results of the evaluation of a new solvation parameter model for reversed-phase ion-pair chromatography by linear gradient elution. This model is described as . The first six terms are the usual solvation parameter equation for neutral solutes, and the seventh term represents the contribution to retention from solute’s ionization. The last term describes the retention increase due to ion-pair effect. Retention times obtained for 60 solutes (neutral, acidic and basic) in acetonitrile/aqueous mobile phases with different ion-pair reagents (phosphoric acid, trifluoroacetic acid, heptafluorobutyric acid, perchloric acid, and hexafluorophosphoric acid) are used to evaluate the capability of the function. It is concluded that the model describes the retention of ionizable/ionized compounds under ion-pair conditions very well. Accordingly, the function extends the application of linear solvation energy relationships (LSERs) to ionizable compounds in ion-pair chromatography, and allows us to easily predict their retention for chromatographic optimization, including selectivity optimization and internal standard selection. Finally, the conclusion can be extended to ioscratic elution.