Biomedical subjects

Vladimir Poroikov

Publications and source records attributed to Vladimir Poroikov.

4 recordsLinked to original sources

PubMed2007

CYCLONET--an integrated database on cell cycle regulation and carcinogenesis.

Computational modelling of mammalian cell cycle regulation is a challenging task, which requires comprehensive knowledge on many interrelated processes in the cell. We have developed a web-based integrated database on cell cycle regulation in mammals in normal and pathological states (Cyclonet database). It integrates data obtained by 'omics' sciences and chemoinformatics on the basis of systems biology approach. Cyclonet is a specialized resource, which enables researchers working in the field of anticancer drug discovery to analyze the wealth of currently available information in a systematic way. Cyclonet contains information on relevant genes and molecules; diagrams and models of cell cycle regulation and results of their simulation; microarray data on cell cycle and on various types of cancer, information on drug targets and their ligands, as well as extensive bibliography on modelling of cell cycle and cancer-related gene expression data. The Cyclonet database is also accessible through the BioUML workbench, which allows flexible querying, analyzing and editing the data by means of visual modelling. Cyclonet aims to predict promising anticancer targets and their agents by application of Prediction of Activity Spectra for Substances. The Cyclonet database is available at http://cyclonet.biouml.org.

Animals↗

PubMed2006

Prediction of protein functional specificity without an alignment.

We propose a new approach to predict functional specificity of proteins from their amino acid sequences. Our approach is based on two things: structural Multilevel Neighborhoods of Atom (MNA) descriptors and an original Bayesian algorithm. Usually, a protein sequence is presented as a string of amino acid symbols. Here we introduce a new description of an amino acid sequence: a set of structural MNA descriptors. The MNA descriptor is a string describing an atom and its neighbor atoms according to the selected level. In this work, we also use description of a protein sequence as a set of peptides (strings of amino acid symbols). We performed a case study on two subsubclasses of enzyme nomenclature (EC). It is shown that B-statistics give a sufficient predictive power of enzyme specificity prediction for both MNA descriptors and peptides. We also showed that MNA descriptors give higher accuracy values in comparison with peptides and also provide a choice of MNA descriptor levels for best accuracy prediction. The highest average accuracy prediction that was achieved was 0.98.

Amino Acid Sequence↗

PubMed2005

Why relevant chemical information cannot be exchanged without disclosing structures.

Both society and industry are interested in increasing the safety of pharmaceuticals. Potentially dangerous compounds could be filtered out at early stages of R&D by computer prediction of biological activity and ADMET characteristics. Accuracy of such predictions strongly depends on the quality & quantity of information contained in a training set. Suggestion that some relevant chemical information can be added to such training sets without disclosing chemical structures was generated at the recent ACS Symposium. We presented arguments that such safety exchange of relevant chemical information is impossible. Any relevant information about chemical structures can be used for search of either a particular compound itself or its close analogues. Risk of identifying such structures is enough to prevent pharma industry from relevant chemical information exchange.

Computer Simulation↗

PubMed2004

Design, synthesis, computational and biological evaluation of new anxiolytics.

New anxiolytics have been discovered by prediction of biological activity with computer programs pass and derek for a heterogeneous set of 5494 highly chemically diverse heterocyclic compounds (thiazoles, pyrazoles, isatins, a-fused imidazoles and others). The majority of tested compounds exhibit the predicted anxiolytic effect. The most potent activity was found in 2-(4-nitrophenyl)-3-(4-phenylpiperazinomethyl)imidazo[1,2-a]pyridine 8, 1-[(4-bromophenyl)-2-oxoethyl]-3-(1,3-dioxolano)-2-indolinone 3, 5-hydroxy-3-methoxycarbonyl-1-phenylpyrazole 5 and 2-(4-fluorophenyl)-3-(4-methylpiperazinomethyl)imidazo[1,2-a]pyridine 7. The application of the computer-assisted approach significantly reduced the number of synthesized and tested compounds and increased the chance of finding new chemical entities (NCEs).

Animals↗