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Biomedical subjects

Mati Karelson

Publications and source records attributed to Mati Karelson.

32 records · Page 2Linked to original sources
PubMed2002

'Strain effect' descriptors for ATP and ADP derivatives with modified phosphate groups.

Semiempirical AM1 calculations were carried out for quantum chemically optimized conformations of ATP and ADP and their modified phosphate derivatives with the oxygen atoms intervening between phosphorus atoms substituted by imido or methylene groups or the double-bonded oxygen atoms substituted by sulfur. In addition to the calculation of conventional geometric and energetic parameters, the effect of these substitutions was quantified in terms of conformational 'strain energy'. The latter has been defined as the energy of transformation of the parent nucleotide (ATP or ADP) from the optimum conformation to the conformation optimized for its phosphate-modified analog. The results of calculations revealed that conformational 'strain' of phosphate-modified nucleotides depends not only on the nature of the substituent but also on its position. The respective effect had the largest magnitude when the substitution was made between two terminal phosphorus atoms. Given that the 'strain energy' characterizes the geometrical aspects of the interaction of nucleotide molecules with receptors and enzymes, an attempt was made to correlate it with the corresponding biological activities. Such correlation was significant in the case of highly specific binding sites for universal ligands like ATP.

Adenosine Diphosphate↗
PubMedDate not supplied

Correlation of the melting points of potential ionic liquids (imidazolium bromides and benzimidazolium bromides) using the CODESSA program.

The melting points of several imidazolium-based ionic liquids or ionic liquid analogues were correlated using the CODESSA program in order to develop predictive tools for determination of suitable ionic liquid salts. The data set consisted of melting point data (degrees C) for 104 substituted imidazolium bromides divided on the basis of the N-substituents into three subsets: A-57 compounds, B-29 compounds, and C-18 compounds. The 45 benzimidazolium bromides form set D. Five-parameter correlations were obtained for (i) set A with R2 = 0.7442, (ii) set B with R2 = 0.7517, and (iii) set D with R2 = 0.6899, while set C was correlated with a three parameter equation with R(2) = 0.9432. These descriptors for predicting the melting points of the imidazolium and benzimidazolium bromides were based on the size and electrostatic interactions in the cations.

Journal Article↗
PubMedDate not supplied

QSPR correlation of the melting point for pyridinium bromides, potential ionic liquids.

In an attempt to develop predictive tools for the determination of new ionic liquid solvents, QSPR models for the melting points of 126 structurally diverse pyridinium bromides in the temperature range 30-200 degrees C were developed with the CODESSA program. Six- and two-descriptor equations with squared correlation coefficients (R(2)) of 0.788 and 0.713, respectively, are reported for the melting temperatures. The models illustrate the importance of information content indices, total entropy, and the average nucleophilic reactivity index for an N atom.

Journal Article↗
PubMedDate not supplied

Prediction of ultraviolet spectral absorbance using quantitative structure-property relationships.

High performance liquid chromatography (HPLC) with ultraviolet (UV) spectrophotometric detection is a common method for analyzing reaction products in organic chemistry. This procedure would benefit from a computational model for predicting the relative response of organic molecules. Models are now reported for the prediction of the integrated UV absorbance for a diverse set of organic compounds using a quantitative structure-property relationship (QSPR) approach. A seven-descriptor linear correlation with a squared correlation coefficient (R2) of 0.815 is reported for a data set of 521 compounds. Using the sum of ZINDO oscillator strengths in the integration range as an additional descriptor allowed reduction in the number of descriptors producing a robust model for 460 compounds with five descriptors and a squared correlation coefficient 0.857. The descriptors used in the models are discussed with respect to the physical nature of the UV absorption process.

Journal Article↗
PubMedDate not supplied

A general QSPR treatment for dielectric constants of organic compounds.

Multilinear regression and neural network methods have been used to develop QSPR models for the prediction of the dielectric constant (epsilon) and Kirkwood function (epsilon - 1)/(2epsilon + 1) of organic liquids. Both methods can provide acceptable models for the prediction of these properties. The QSPR models developed from the training set of 155 diverse compounds use theoretical molecular descriptors encoding electronic properties of the molecule and the intermolecular interaction between molecules. The QSPR models for the Kirkwood function appear to be more reliable than the models for the dielectric constant. The average prediction error of the best model for the dielectric constant is 27.0%. The average prediction error of the best model for the Kirkwood function is 4.1%.

Journal Article↗
PubMedDate not supplied

General and class specific models for prediction of soil sorption using various physicochemical descriptors.

Diverse chemical descriptors were explored for use in QSAR models aimed to screen the soil sorption potential of organic compounds. The descriptors included logP, HyperChem QSARProperties descriptors, a combination of connectivity indices, geometrical, and quantum chemical measures, and two sets from the DRAGON and CODESSA program packages, respectively. Generally, the univariate logP models were capable of capturing most of the variation and give an indication of the sorption potential. The multivariate models required refined variable selection procedures but were shown to include crucial descriptors for modeling compound classes with specific chemical characteristics.

Models, Chemical↗
PubMedDate not supplied

A comprehensive docking study on the selectivity of binding of aromatic compounds to proteins.

Generally, computer-aided drug design is focused on screening of ligand molecules for a single protein target. The screening of several proteins for a ligand is a relatively new application of molecular docking. In the present study, complexes from the Brookhaven Protein Databank were used to investigate a docking approach of protein screening. Automated molecular docking calculations were applied to reproduce 44 protein-aromatic ligand complexes (31 different proteins and 39 different ligand molecules) of the databank. All ligands were docked to all different protein targets in altogether 12090 docking runs. Based on the results of the extensive docking simulations, two relative measures, the molecular interaction fingerprint (MIF) and the molecular affinity fingerprint (MAF), were introduced to describe the selectivity of aromatic ligands to different proteins. MIF and MAF patterns are in agreement with fragment and similarity considerations. Limitations and future extension of our approach are discussed.

Animals↗
PubMedDate not supplied

Quantitative structure-property relationship modeling of beta-cyclodextrin complexation free energies.

CODESSA-PRO was used to model binding energies for 1:1 complexation systems between 218 organic guest molecules and beta-cyclodextrin, using a seven-parameter equation with R2 = 0.796 and Rcv2 = 0.779. Fragment-based TRAIL calculations gave a better fit with R2 = 0.943 and Rcv2 = 0.848 for 195 data points in the database. The advantages and disadvantages of each approach are discussed, and it is concluded that a combination of the two approaches has much promise from a practical viewpoint.

Artificial Intelligence↗
PubMedDate not supplied

QSPR treatment of the soil sorption coefficients of organic pollutants.

In this study, general and class-specific QSPR models for soil sorption, logK(OC), of 344 organic pollutants (0 < logK(OC) < 4.94) were developed using a large variety of theoretical molecular descriptors based only on molecular structure. Two general models were obtained. The first model was derived for a structurally representative set of 68 chemicals (R2=0.76, s=0.44), whereas the second involved a total of 344 compounds (R2=0.76, s=0.41). The first was validated using the data for the remaining 276 pollutants (R2=0.70, s=0.45). An additional validation of both models was performed using an independent set of 48 pollutants. Both models predict the logK(OC) at the level of experimental precision, while the theoretical molecular descriptors appearing in the QSPR models give further insight into the mechanisms of soil sorption. The analysis of the distribution of the residuals of the logK(OC) values calculated by both general models indicated the need and possible advantages of modeling soil sorption for smaller data sets related to individual classes of chemicals. Accordingly, QSPR models were also developed for 14 chemical classes. The descriptors appearing in these models were discussed as related to the possible interaction mechanisms in soil sorption.

Journal Article↗
PubMedDate not supplied

Open computing grid for molecular science and engineering.

Grid is an emerging infrastructure for distributed computing that provides secure and scalable mechanisms for discovering and accessing remote software and data resources. Applications built on this infrastructure have great potential for addressing and solving large scale chemical, pharmaceutical, and material science problems. The article describes the concept behind grid computing and will present the OpenMolGRID system that is an open computing grid for molecular science and engineering. This system provides grid enabled components, such as a data warehouse for chemical data, software for building QSPR/QSAR models, and molecular engineering tools for generating compounds with predefined chemical properties or biological activities. The article also provides an overview about the availability of chemical applications in the grid.

Journal Article↗
PubMedDate not supplied

Neural networks convergence using physicochemical data.

An investigation of the neural network convergence and prediction based on three optimization algorithms, namely, Levenberg-Marquardt, conjugate gradient, and delta rule, is described. Several simulated neural networks built using the above three algorithms indicated that the Levenberg-Marquardt optimizer implemented as a back-propagation neural network converged faster than the other two algorithms and provides in most of the cases better prediction. These conclusions are based on eight physicochemical data sets, each with a significant number of compounds comparable to that usually used in the QSAR/QSPR modeling. The superiority of the Levenberg-Marquardt algorithm is revealed in terms of functional dependence of the change of the neural network weights with respect to the gradient of the error propagation as well as distribution of the weight values. The prediction of the models is assessed by the error of the validation sets not used in the training process.

ATP Binding Cassette Transporter, Subfamily B, Mem↗