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Computer simulation has been used to identify peptides that mimic the natural target of the SARS-CoV-2 coronavirus spike (S) protein, the angiotensin converting enzyme type 2 (ACE2) cell receptor. Based on the structure of the complex of the protein S receptor-binding domain (RBD) and ACE2, the design of chimeric molecules consisting of two 22-23-mer peptides linked to each other by disulfide bonds was carried out. The chimeric molecule X1 was a disulfide dimer, in which edge cysteine residues in the precursor molecules h1 and h2 were connected by the S-S bond. In the chimeric molecule X2, the disulfide bond was located in the middle of the molecule of each of the precursor peptides. The precursors h1 and h2 modelled amino acid sequences of α1- and α2-helices of the extracellular peptidase domain of ACE2, respectively, keeping intact most of the amino acid residues involved in the interaction with RBD. The aim of the work was to evaluate the binding efficiency of chimeric molecules and their RBD-peptides (particularly in dependence of the middle and edge methods of fixing the initial peptides h1 and h2). The proposed polypeptides and chimeric molecules were synthesized by chemical methods, purified (to 95-97% purity), and characterized by HPLC and MALDI-TOF mass spectrometry. The binding of the peptides to the SARS-CoV-2 RBD was evaluated by microthermophoresis with recombinant domains corresponding in sequence to the original Chinese (GenBank ID NC_045512.2) and the British (B. 1.1.7, GISAID EPI_ISL_683466) variants. Binding to the original RBD of the Chinese variant was detected in three synthesized peptides linear h2 and both chimeric variants. Chimeric peptides were also bound to the RBD of the British variant with micromolar constants. The antiviral activity of the proposed peptides in Vero cell culture was also evaluated.Antibiotic resistance of bacteria is a topical problem on a global scale. Sometimes vigorous human activity leads to an increase in the number of bacteria carrying resistance genes in the environment. Antimicrobial peptides (AMPs) and similar compounds are potential candidates for combating antibiotic-resistant bacteria. Previously, we proposed and successfully tested on Thermus thermophilus a new mechanism of AMP action. This mechanism of directed coaggregation is based on the interaction of a peptide capable of forming fibrils with a target protein. In this work, we discuss the criteria for choosing a target for the targeted action of AMP, describe the features of the "parental" S1 ribosomal proteins T. thermophilus and Escherichia coli and the studied peptides using bioinformatic analysis methods, assess the antimicrobial effect of the synthesized peptides on a model organism of E. coli and cytotoxicity on cells of human fibroblasts. The obtained results will be important for the creation of new AMPs for pathogenic organisms.Accumulation of genetic data in the field of Parkinson's disease research culminated in identifying risk factors and confident prediction of the disease occurrence. To find new gene-targets for diagnostics and therapy we have to reconstruct gene network of the disease, to cluster genes in the network, to reveal key (hub) genes with largest number of interactions in the network. Using the on-line bioinformatics tools OMIM, PANTHER, gProfiler, GeneMANIA, and STRING-DB, we have analyzed the current array of data related to Parkinson's disease, calculated the categories of gene ontologies for a large list of genes, visualized them, and built gene networks containing the identified key objects and their relationships. However, translating the results into biological understanding is still a promising major challenge. The analysis of the genes associated with the disease, the assessment of their place in the gene network (connectivity) allows us to evaluate them as target genes for medicinal effects.To search for new targets of therapy, it is necessary to reconstruct the gene network of the disease, and identify the interaction of genes, proteins, and drug compounds. Using the online bioinformatics tools we have analyzed the current data set related to the metabolism of xenobiotics, mediated by the N-acetyltransferase 2 (NAT2) gene. The study of allelic polymorphism of the NAT2 gene has a prognostic value, allowing to determine the risk of a number of oncological diseases, the degree of increased risk due to smoking and exposure to chemical carcinogens, including drugs. The aim of this study was to determine the frequencies of two important "slow" variants of the NAT2 gene (NAT2*5, rs1801280 and NAT2*7, rs1799931), which significantly affected the rate of xenobiotic acetylation among the indigenous Nenets population of Northern Siberia. The obtained frequencies of polymorphic variants among the Nenets occupy an intermediate value between those for Europeans and Asians, which might indicate specific features of adaptation. We present a model of the distribution of two polymorphic variants of the NAT2 gene involved in the biotransformation of xenobiotics to study the characteristics of their metabolism in the indigenous inhabitants of Yamal.Glioblastoma multiforme (GBM) is a highly malignant brain tumor with average survival time of 15 months. Less than 2% of the patients survive beyond 36 months. To understand the molecular mechanism responsible for poor prognosis, we analyzed GBM samples of TCGA microarray (n=560) data. We have identified 720 genes that have a significant impact upon survival based on univariate cox regression. [https://www.selleckchem.com/products/dt-061-smap.html SMAP activator] We applied the Genome Enhancer pipeline to analyze potential mechanisms of regulation of activity of these genes and to build gene regulatory networks. We identified 12 transcription factors enriched in the promoters of these genes including the key molecule of GBM - STAT3. We found that STAT3 had significant differential expression across extreme survivor groups (short-term survivors- survival 36 months) and also had a significant impact on survival. In the next step, we identified master regulators in the signal transduction network that regulate the activity of these transcription factors. Master regulators are filtered based on their differential expression across extreme survivors groups and impact on survival.