A computational model of the 5-HT3 receptor extracellular domain: search for ligand binding sites

A three-dimensional model of the 5-HT₃ receptor extarcellular domain has been derived on the basis of the nicotinic acetylcholine receptor model recently published by Tsigelny et al. Maximum complementarity between the position and characteristics of mutated residues putatively involved in ligand interaction and the pharmacophoric elements derived by the indirect approach applied on several series of 5-HT₃ ligands have been exploited to gain insights into the ligand binding modalities and to speculate on the mechanistic role of the structural components. The analysis of the three-dimensional model allows one to distinguish among amino acids that exert key roles in ligand interactions, subunit architecture, receptor assembly and receptor dynamics. For some of these, alternative roles with respect to the ones hypothesized by experimentalists are assigned. Different binding modalities for agonists and antagonists are highlighted, and residues which probably play a role in the transduction of binding into a change in conformational state of the receptor are suggested. Received: 27 July 2000 / Accepted: 15 September 2000 / Published online: 21 December 2000     

A kinetic model for predicting biodegradation

Biodegradation plays a key role in the environmental risk assessment of organic chemicals. The need to assess biodegradability of a chemical for regulatory purposes supports the development of a model for predicting the extent of biodegradation at different time frames, in particular the extent of ultimate biodegradation within a ‘10?day window’ criterion as well as estimating biodegradation half-lives. Conceptually this implies expressing the rate of catabolic transformations as a function of time. An attempt to correlate the kinetics of biodegradation with molecular structure of chemicals is presented. A simplified biodegradation kinetic model was formulated by combining the probabilistic approach of the original formulation of the CATABOL model with the assumption of first order kinetics of catabolic transformations. Nonlinear regression analysis was used to fit the model parameters to OECD 301F biodegradation kinetic data for a set of 208 chemicals. The new model allows the prediction of biodegradation multi-pathways, primary and ultimate half-lives and simulation of related kinetic biodegradation parameters such as biological oxygen demand (BOD), carbon dioxide production, and the nature and amount of metabolites as a function of time. The model may also be used for evaluating the OECD ready biodegradability potential of a chemical within the ‘10-day window’ criterion.     

The enhancement of immunosuppressive effects of cyclosporine A on human T-cells using fusogenic liposomes

The aim of this study was to prepare and characterize neutral, positively charged, negatively charged and fusogenic liposomes of different sizes that contain cyclosporine A (CyA) and to evaluate their immunosuppressive activity on human T-cells. Neutral liposomes containing CyA were prepared from dipalmitoylphosphatidylcholine (DPPC) and cholesterol using the solvent evaporation method. To prepare positively charged, negatively charged and fusogenic liposomes containing CyA; stearylamine (SA), dicetylphosphate (DCP) and dioleoylphosphatidylethanolamine (DOPE) were added to the neutral liposome formulation, respectively. To reduce the size of liposomes containing CyA, extrusion through polycarbonate filters (1000, 400 and 100 nm) was used. The liposomes were characterized by their size, zeta potential and encapsulation efficiency. The in vitro immunosuppressive effects of an aqueous solution of CyA and different liposomes containing CyA were determined on human T-cells by the MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide] assay. The mean diameter of the various multilamellar vesicle (MLV) liposomes containing CyA was between 1.76 and 2.49 μm. The encapsulation efficiency for the different MLV and extruded liposomes containing CyA ranged from 73% to 90%. In vitro immunosuppressive evaluation by T-cell culture showed that fusogenic liposomes have the best inhibitory effects on T-cell proliferation compared to the other liposomes. Reducing the size of the liposomes did not affect the in vitro immunosuppressive activity. The average IC₅₀ for the aqueous solution of CyA and the neutral, positively charged, negatively charged and fusogenic liposomes containing CyA was 4.98 × 10⁻², 7.38, 1.43, 3.84 × 10⁻³ and 7.93 × 10⁻⁵ mM, respectively. The results of this study indicate that fusogenic liposomes have the strongest immunosuppressive activity and could be considered as a suitable delivery system for CyA.     

A new method for predicting conduction anesthesia

A new method for predicting conduction anesthesia has been suggested. The method is based on calculation of theP matrix probabilities of interatomic contacts for each molecule of the compounds considered. TheP matrix enables one to evaluate the main tendencies of atoms and atomic groups to interact in biochemical sorption on the nerve fiber surface. The minimum effective concentrations calculated for 25 compounds are in good agreement with the experimental data. The correlation coefficient between the experimental and calculated values is 0.98 when the standard deviation is 0.1 mmol L⁻¹. Translated fromIzvestiya Akademii Nauk. Seriya Khimicheskaya, No. 10, pp. 1781–1784, October, 1997.     

PPM-Dom: A novel method for domain position prediction

Domains are the structural basis of the physiological functions of proteins, and the prediction of which is an advantageous process on the study of protein structure and function. This article proposes a new complete automatic prediction method, PPM-Dom (Domain Position Prediction Method), for predicting the particular positions of domains in a target protein via its atomic coordinate. The presented method integrates complex networks, community division, and fuzzy mean operator (FMO). The whole sequences are divided into potential domain regions by the complex network and community division, and FMO allows the final determination for the domain position. This method will suffice to predict regions that will form a domain structure and those that are unstructured based on completely new atomic coordinate information of the query sequence, and be able to separate different domains in the same query sequence from each other. On evaluating the performance using an independent testing dataset, PPM-Dom reached 91.41% for prediction accuracy, 96.12% for sensitivity and 92.86% for specificity. The tool bag of PPM-Dom is freely available at http://cic.scu.edu.cn/bioinformatics/PPMDom.zip.     

A computer simulation — Statistical procedure for predicting complexation equilibrium constants

The complexing properties of macrocyclic ligands have been quantitatively studied by the combined use of molecular mechanics, molecular dynamics, and multiple linear regression. The dependent variables in the regression equations are experimental equilibrium constants for known macrocycle complexes in various solvents. The independent variables are theoretical simulation results on the solvent-free ligand and its complex and additional physically motivated empirical variables to describe solvent and other important effects.The systems studied were: (a) 314 metal ion-macrocycle-solvent (including 3 mixed solvents) combinations; (b) 88 ammonium ion-crown ether-solvent (including 1 mixed solvent) combinations; (c) 24 hydrogen ion-crown ether-H₂O combinations; (d) 26 Na⁺ ion-spherand-CDCl₃ combinations; (e) 78 ammonium ion-spherand-CDCl₃ combinations; and (f) 73 complicated host-guest-solvent (including 1 mixed solvent) combinations.For each system, we report the best regression equation obtained using the AMBER force field. The standard errors in logK range from 1.42 in the largest system to 0.36 in the smallest. Regression equations were determined for several of the systems using the MMP2 force field as well, and the equations are shown to be relatively insensitive to the force field.The predictive ability of the method was tested by predicting logK for 20% of the cases chosen at random using equations derived from the remaining 80%. The errors in the predicted values are shown to be consistent with the statistical assumptions of the model.Regression equations obtained with this method can be used to predict the equilibrium constants for new complexes involving some combination of new, possibly unknown macrocycle, new host and, in certain cases new solvent. No X-ray or other structural data for the macrocycle is needed.     

Reaction pathways for model II–VI precursors: A computational study

A computational study of the reactions of model II–VI precursors, MH₂ (M=Zn, Cd, Hg) and H₂E (E=O, S, Se, Te), is reported. The present computations yield several results of relevance to OMVPE of II–VI materials from MR₂/ER precursors. Adduct formation is feasible at low temperatures, in line with recent experiments, but not at higher temperatures common in II–VI OMVPE. Calculations suggest that adducts are most likely to form after elimination has resulted in replacement of hydride with hydroxide ligands (in our model) or alkyl with alkoxide ligands (in larger models). The overall reaction MH₂+2 H₂E→M(EH)₂ is generally more favorable, kinetically and thermodynamically, for lighter metals and heavier elements. On the basis of calculated thermodynamic and kinetic quantities, we conclude that the preferred pathway from reactants (MH₂+2 H₂E), except for M=Hg and E=Te, to an M(EH)₂ intermediate is by way of a 1:2 adduct (or a similar portion of the potential energy surface in the absence of a bound adduct) followed by two successive H₂ eliminations, in preference to the alternative in which adduct formation and H₂ elimination occur in succession. ©1999 John Wiley & Sons, Inc. Int J Quant Chem 71: 47–56, 1999     

A simple model for predicting the course of photochemical reactions

The possibility of building and using a simplified model with constant (time-invariant) reaction probabilities for rapid simulation of photochemical processes with the required reliability of the prediction has been shown. A relation between the probabilities of transformations and fundamental molecular characteristics (optical transition probabilities and quantum beat frequencies referring to a nonradiative reaction transition) that can be quantitatively determined with a good degree of certainty has been revealed. For monomolecular isomerization reactions as an example, the attainable level of the accuracy of prediction by the simple model has been estimated, which has been shown to be ～10% in most cases. It is important that the prediction of the order of magnitude of the key characteristics of photochemical processes, such as quantum yield and the reaction rate, is definitely correct as is required in most applications of photochemistry.     

A computational model for non-conserved mature miRNAs from the rice genome

Several computational approaches employ the high complementarity of plant miRNAs to target mRNAs as a filter to recognize miRNA. Numerous non-conserved miRNAs are known with more recent evolutionary origin as a result of target gene duplication events. We present here a computational model with knowledge inputs from reported non-conserved mature miRNAs of Oryza sativa (rice). Sequence- and structure-based approaches were used to retrieve miRNA features based on rice Argonaute protein and develop a multiple linear regression (MLR) model (r² = 0.996, q²_cv = 0.989) which scored mature miRNAs as predicted by the MaturePred program. The model was validated by scoring test set (q² = 0.990) and computationally predicted mature miRNAs as external test set (q²_test = 0.895). This strategy successfully enhanced the confidence of retrieving most probable non-conserved miRNAs from the rice genome. We anticipate that this computational model would recognize unknown non-conserved miRNA candidates and nurture the current mechanistic understanding of miRNA sorting to unveil the role of non-conserved miRNAs in gene silencing.     

A computational model of the nicotinic acetylcholine binding site

We have derived a model of the nicotinic acetylcholine binding site. This was accomplished by using three known agonists (acetylcholine, nicotine and epibatidine) as templates around which polypeptide side chains, found to be part of the receptor cavity from published molecular biology studies, are allowed to flow freely in molecular dynamics simulations and mold themselves around these templates. The resulting supramolecular complex should thus be a complement, both in terms of steric effects as well as electronic effects, to the agonists and it should be a good estimation of the true receptor cavity structure. The shapes of those minireceptor cavities equilibrated rapidly on the simulation time scale and their structural congruence is very high, implying that a satisfactory model of the nicotinic acetylcholine binding site has been achieved. The computational methodology was internally tested against two rigid and specific antagonists (dihydro--erytroidine and erysoidine), that are expected to give rise to a somewhat differently shaped binding site compared to that derived from the agonists. Using these antagonists as templates there were structural reorganizations of the initial receptor cavities leading to distinctly different cavities compared to agonists. This indicates that adequate times and temperatures were used in our computational protocols to achieve equilibrium structures for the agonists. Overall, both minireceptor geometries for agonists and antagonists are similar with the exception of one amino acid (ARG209).     

A simple and generalized model for predicting the density of ionic liquids

A simple and accurate model to predict the density of ionic liquids is presented. The proposed model is based on a generalized correlation that has been conveniently modified and experimental literature data have been used to fit the five model parameters, to finally propose an equation that allows predicting densities of any ionic liquid. The model uses the critical temperature, the critical volume, the normal boiling temperature and the molecular mass to estimate the density at temperatures commonly used in ionic liquid applications (270–360 K). A set of 602 density data for 146 ionic liquids has been used in the study. The results were compared with predictions of ten generalized corresponding states principle correlations available in the literature. These generalized correlations have not been applied to ionic liquids before so the appropriateness and accuracy of these models to ionic liquid density estimation are unknown until now. Results show that the new simple correlation gives low deviations and can be used with confidence in thermodynamic and engineering calculations.     

Possibility of predicting separations in supercritical fluid chromatography with the solvation parameter model

In order to obtain a selection of optimal chromatographic columns for the separation of chlorotriazine pesticides in packed column supercritical fluid chromatography (pSFC), a multi-criteria approach is applied. For this purpose, prediction of the separations is carried out, based on quantitative structure–retention relationships, then Derringer's desirability function is proposed to determine the stationary phase that will result in the most desirable separation. The best SFC separation obtained was then optimized using a mobile phase gradient. Besides, the accuracy of the solvation parameter model as SFC retention predictive model is assessed.     

A kinetic model for predicting oxidative degradation rates in combined radiation-thermal environments

The complex degradation behavior of a poly(vinyl chloride) (PVC) cable jacketing material in combined radiation/temperature/air aging environments is experimentally separated into two dominant radiation dose-rate effect mechanisms. The first, operative at high dose rates, involves diffusion-limited oxidation, which leads to heterogeneously oxidized samples. The second, important at low dose rates, involves thermally-induced breakdown of intermediate peroxides. In the homogeneous degradation regime, a theoretical kinetic model is derived which, based on experimental evidence, assumes unimolecular termination kinetics and rate-determining, hydroperoxide-mediated branching reactions. Dependent upon the ratio of particular rate constants, the model predicts that dose-rate effects will either continue to increase or eventually disappear as the dose rate is lowered. Theoretical analysis of sequential (radiation followed by temperature exposures) aging experiments allows a time–temperature–dose rate shifting procedure to be developed. Using this procedure, higher temperature combined environment results can be shifted to a lower reference temperature, thereby extending the lower temperature results to lower (and experimentally inaccessible) dose rates. By applying this procedure to experimental PVC data, evidence in support of the theoretical model is obtained. In addition, model predictions are shown to agree with 12-year real-time aging results.     

A new quantitative structure-retention relationship model for predicting chromatographic retention time of oligonucleotides

An integrated approach is proposed to predict the chromatographic retention time of oligonucleotides based on quantitative structure-retention relationships(QSRR) models.First,the primary base sequences of oligonucleotides are translated into vectors based on scores of generalized base properties(SGBP),involving physicochemical,quantum chemical,topological,spatial structural properties,etc.;thereafter,the sequence data are transformed into a uniform matrix by auto cross covariance(ACC).ACC accounts for the ...     

A simple calculational model for predicting the site for nucleophilic substitution in aromatic perfluorocarbons

We present a simple model for predicting the principal site for nucleophilic substitution in aromatic perfluorocarbons. Our model is based on the relative stabilities of the Meisenheimer complexes as calculated using density functional theory with a modest basis set. (Hartree-Fock theory will do just as well.) Although such calculations were essentially impossible to carry out when early theoretical work on this topic was undertaken in the mid-1960s and early 1970s, they are now routine and full geometry optimization for any of the systems studied in this work can be completed in a matter of minutes with modern quantum chemistry programs and computational hardware. Predictions from our model agree with experimental observations for 16 aromatic perfluorocarbons, and together with additional NMR calculations, lead us to conclude that the earlier prediction that perfluoroanthracene undergoes nucleophilic substitution in the 2-position is incorrect, and is based on a misinterpretation of the experimental ¹⁹F NMR spectrum.     

A unified model for flux prediction during batch cell ultrafiltration

An analysis of the flux decline encountered during ultrafiltration (UF) in a batch cell is presented by including the combined influence of the osmotic pressure and the gel layer. A predictive model for the flux decline in unstirred and stirred batch cell UF processes is developed by unifying the osmotic pressure and gel layer models. UF experiments were performed in a batch cell with polymeric solutes (PEG, dextran and PVA) and a protein (BSA), ranging widely in molecular weights and physico-chemical properties, under various operating conditions (pressure, solution pH, and stirrer speed). The present unified model predictions match closely with the experimental flux behaviour for all cases, while individual osmotic pressure and gel layer models are found to be inadequate.     

CSSP2: an improved method for predicting contact-dependent secondary structure propensity

The calculation of contact-dependent secondary structure propensity (CSSP) has been reported to sensitively detect non-native β-strand propensities in the core sequences of amyloidogenic proteins. Here we describe a noble energy-based CSSP method implemented on dual artificial neural networks that rapidly and accurately estimate the potential for the non-native secondary structure formation in local regions of protein sequences. In this method, we attempted to quantify long-range interaction patterns in diverse secondary structures by potential energy calculations and decomposition on a pairwise per-residue basis. The calculated energy parameters and seven-residue sequence information were used as inputs for artificial neural networks (ANNs) to predict sequence potential for secondary structure conversion. The trained single ANN using the >(i, i ± 4) interaction energy parameter exhibited 74% accuracy in predicting the secondary structure of test sequences in their native energy state, while the dual ANN-based predictor using (i, i ± 4) and >(i, i ± 4) interaction energies showed 83% prediction accuracy. The present method provides a simple and accurate tool for predicting sequence potential for secondary structure conversions without using 3D structural information.