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A substrate and inhibitor analysis of the thrombin interaction with synthetic peptide substrates and inhibitors of differing hydrophobicity and volume of the side amino acid residue, localized in the sub-centers thrombin S2 and S3 were carried out. The kinetic parameters of individual stages of the enzymatic reaction process (K(S), k2, k3) were estimated. It is shown that the efficiency of acylation and deacylation stages of the enzymatic reaction decreases with increasing hydrophobicity of the substituent in P2 as well as P3, at the same time the affinity of selected peptides toward enzyme is steadily increasing. With the aim to evaluate the hydrophobicity of compounds a LogP value was calculated and was made an attempt to compare them with the correspondent Ki values. Comparative kinetic analysis of Z-Arg-OMe and its uncharged analogue Z-Cit-OMe has shown the absence of uncharged analog hydrolysis, however, the mentioned citrulline derivate inhibits the hydrolysis of the charged analogue. These findings confirm the important role of hydrophobic moiety in the structure of thrombin inhibitors in preferential binding mode and inhibition of thrombin active side.

The estimation of accuracy and applicability of QSAR and QSPR models for biological and physicochemical properties represents a critical problem. The developed parameter of "distance to model" (DM) is defined as a metric of similarity between the training and test set compounds that have been subjected to QSAR/QSPR modeling. In our previous work, we demonstrated the utility and optimal performance of DM metrics that have been based on the standard deviation within an ensemble of QSAR models. The current study applies such analysis to 30 QSAR models for the Ames mutagenicity data set that were previously reported within the 2009 QSAR challenge. We demonstrate that the DMs based on an ensemble (consensus) model provide systematically better performance than other DMs. The presented approach identifies 30-60% of compounds having an accuracy of prediction similar to the interlaboratory accuracy of the Ames test, which is estimated to be 90%. Thus, the in silico predictions can be used to halve the cost of experimental measurements by providing a similar prediction accuracy. The developed model has been made publicly available at http://ochem.eu/models/1 .

Interaction of two groups of bioregulators, which oppositely affect activity of adenylate cyclase and phosphoinositide cellular signaling systems, with the Langmuir monolayer films made of natural lecithin was studied. Most significant influence on the structural and energy characteristics of lipid monolayers was revealed for the group of bioregulators, which inhibit polyphosphoinositide signaling system or/and activate adenylate cyclase signaling system. It is shown, that using the cluster analysis the bioregulators can be divided into two groups according to general orientation of their action on the considered systems of transduction of a signal.

Internet technology offers an excellent opportunity for the development of tools by the cooperative effort of various groups and institutions. We have developed a multi-platform software system, Virtual Computational Chemistry Laboratory, http://www.vcclab.org, allowing the computational chemist to perform a comprehensive series of molecular indices/properties calculations and data analysis. The implemented software is based on a three-tier architecture that is one of the standard technologies to provide client-server services on the Internet. The developed software includes several popular programs, including the indices generation program, DRAGON, a 3D structure generator, CORINA, a program to predict lipophilicity and aqueous solubility of chemicals, ALOGPS and others. All these programs are running at the host institutes located in five countries over Europe. In this article we review the main features and statistics of the developed system that can be used as a prototype for academic and industry models.

The Maximal Margin (MAMA) linear programming classification algorithm has recently been proposed and tested for cancer classification based on expression data. It demonstrated sound performance on publicly available expression datasets. We developed a web interface to allow potential users easy access to the MAMA classification tool. Basic and advanced options provide flexibility in exploitation. The input data format is the same as that used in most publicly available datasets. This makes the web resource particularly convenient for non-expert machine learning users working in the field of expression data analysis.

Using experimental transcription model in vitro, and method of electron-topological calculations the elements of pharmacological and structural community have been found in the groups of physiologically active compounds (PAC), blocking and activating external cellular receptors. This finding confirms the necessity of subdivision special groups of bio-substrates blockators and activators for hierarchical classification, of PAC.

The influence of physical and chemical properties of some sites of transmembrane receptor domains on the receptors ability to interact with nonspecific antagonists was investigated mathematically. The properties of sites located in 3rd and 7th transmembrane domains are most likely to explain pharmacological characteristics of the receptors. The possibility of receptor blocking by nonspecific antagonists not by competing with agonists but by influencing the receptor conformation is discussed.