Biomeditsinskaya khimiya最新文献

The review summarizes recent achievements and future prospects in the use of chronobiotics for regulating circadian rhythms regulation. Special attention is paid to the mechanisms' action, their classification, and the impact of chemical interventions on the biological clock. Chronobiotics defined as a diverse group of compounds capable of restoring disrupted circadian functions, addressing challenges such as irregular work schedules, artificial light exposure or ageing. The review categorizes these compounds by their pharmacological effects, molecular targets, and chemical structures, underlining their ability to enhance or inhibit key circadian components like CLOCK, BMAL1, PER, and CRY. A particular focus is placed on the therapeutic applications of chronobiotics, including their potential for treating sleep disorders, metabolic issues, and age-related rhythm disturbances, underscoring their wide-ranging applicability in health care. Chronobiotic compounds have promising roles in maintaining physiological rhythms, supporting healthy aging, and enhancing personalised health care. Given their diverse therapeutic potential, chronobiotics are positioned as a significant avenue for further clinical application, marking them as a crucial area of ongoing research and innovation.

Anxiety disorders are one of the most common mental health pathologies in the world. They require searc h and development of novel effective pharmacologically active substances. Thus, the development of new approaches to the search for anxiolytic substances by artificial intelligence methods is an important area of modern bioinformatics and pharmacology. In this work, a multi-target model of the dependence of the anxiolytic activity of chemical compounds on their integral affinity to relevant target proteins based on the correlation convolution of multiple docking energy spectra has been constructed using the method of artificial neural networks. The training set of the structure and activity of 537 known anxiolytic substances was formed on the basis of the previously created database, and optimized 3D models of these compounds were built. 22 biotargets presumably relevant to anxiolytic activity were identified and their valid 3D models were found. For each biotarget, 27 multiple docking spaces have been formed throughout its entire volume. Multiple ensemble molecular docking of 537 known anxiolytic compounds into all spaces of relevant target proteins has been performed. The correlation convolution of the calculated energy spectra of multiple docking was carried out. Using seven training options based on artificial multilayer perceptron neural networks, the multi-target model of depending anxiolytic activity chemical compounds on 22 parameters of the correlation convolution of the multiple docking spectra energy was constructed. The predictive ability of the created model was characterized Acc = 91.2% and AUCROC = 94.4%, with statistical significance of p < 1×10⁻¹⁵. The found model is currently used in the search for new substances with high anxiolytic activity.

The phosphorylation reaction, catalyzed by the enzyme protein kinase A (PKA), plays one of the key roles in the work of the glutamatergic system, primarily involved in memory functioning. The analysis of the dynamic behavior of the enzyme-substrate complex allows one to learn the mechanism of the enzymatic reaction. According to the results of classical molecular dynamics calculations followed by hierarchical clustering, the most preferred proton acceptor during the phosphorylation reaction catalyzed by PKA is the carboxyl group of the amino acid residue Asp166; however, the γ-phosphate group of ATP can also act as an acceptor.

Traditional testing methods in pharmaceutical development can be time-consuming and costly, but in silico evaluation tools can offer a solution. Our in-house Active-IT system, a Ligand-Based Virtual Screening (LBVS) tool, was developed to predict the biological and pharmacological activities of small organic molecules. It includes four independent modules for generating molecular descriptors (3D-Pharma), machine learning modeling (ExCVBA), a database of bioactivity models, and a prediction module. Activity data collected from the PubChem BioAssay database was used for modelling SVM and Naïve Bayes machine learning methods. Models have been constructed using a recursive stratified partition method and validated through an activity randomization (Y-random) process. Over 3500 bioassays were modeled, each comprising 30 SVM and 30 Naïve Bayes models and 60 randomized models. Bioassays with low performance or discrimination between regular and randomized were discarded. Using the Active-IT system we have evaluated three bioactive compounds of Ayahuasca tea. The predictions were thoroughly validated using known targets described in several public databases. The external validation results are noteworthy, with 16 of 33 (48.5% with p-value.

The development of effective antivirals is of great importance due to the threat associated with the rapid spread of viral infections. The accumulation of data in scientific publications and in databases of biologically active compounds provides an opportunity to extract specific information about interactions between chemicals and their viral and host targets. This information can be used for elucidation of knowledge about potential antiviral activity of chemical compounds, their side effects and toxicities. Our study aims to extract knowledge about virus-host interactions and potential antiviral agents based on the mining of massive amounts of scientific publications. With a set of previously developed algorithms, we have extracted comprehensive information on virus-host interactions and chemical compounds that interact with both viral and host targets. We collected data on the interactions of several viruses, including hepatitis B and C viruses, SARS-CoV-2, influenza A and B, and herpes simplex viruses, with (1) the host (human body), (2) potential antiviral agents, and, also extracted information on the interactions between potential antiviral agents and host proteins. Based on the data analysis performed, we created a freely available knowledge base on the interaction of chemical compounds with viral proteins and their host targets, allowing the exploration of both well-studied and recently discovered novel virus-host-chemical-compound interactions.

Anuclear blood cells, platelets, are the basis for the formation of blood clots in human vessels. While antiplatelet therapy is most often used after ischemic events, there is a need for its personalization due to the limited effectiveness and risks of bleeding. Previously, we developed a series of computational models to describe intracellular platelet signaling and a set of experimental methods to characterize the platelets of a given patient. To build a personalized model of platelet signaling, we also conducted research on platelet proteomics. The aim of this study was to personalize the central module of intracellular platelet signaling responsible for the formation of calcium oscillations in response to activation. The model consists of 26 ordinary differential equations. To personalize the model, proteomics data were used and unknown model parameters were selected based on experimental data on the shape and frequency of calcium oscillations in single platelets. As a result of the study, it has been shown that the key personalized parameters of the platelet oscillatory response are the degree of asymmetry of a single calcium spike and the maximum frequency of oscillations. Based on the listed experimentally determined parameters and proteomics data, an algorithm for personalization of the model has been proposed. Here we considered three healthy pediatric donors of different ages. Based on the models, personal curves of platelet calcium response to activation were obtained. The analysis of the models has shown that while there is a large heterogeneity of individual indicators of intracellular signaling, such as the activity of calcium pumps (SERCA) and inositoltriphosphate (IP₃) receptors (IP₃R), these indicators compensate each other in each donors. This observation is confirmed by the analysis of proteomics data from 15 healthy patients: this analysis demonstrates a correlation between the total amount of SERCA and IP₃R. Thus, several new features of human platelet calcium signaling are shown and an algorithm for personalizing its model is proposed.

Currently non-protein amino acids and synthetic peptides are widely used as blocks in drug design. Many proteases are of great interest for pharmacology due to their key role in various pathologies. Bacterial collagenase (EC 3.4.24.3) is quite an attractive target for drug development as the inhibitors of bacterial collagenolytic protease may stop propagation of diseases caused by infections. The interaction of peptides containing unusual amino acids with Clostridium histolyticum collagenase has been evaluated by molecular docking followed by the measurement of enzyme inhibition by selected compounds. According to the docking analysis, 4 compounds were selected and synthesized for further research. Measurement of enzyme activity revealed that all tested compounds inhibited collagenase activity with IC50 values ranging within 1.45-2.08 μM. The antibacterial activity of synthesized compounds against some resistant strains was characterized by MICs values ranging within 4.6-9.2 μg/ml.

Major depressive disorder (MDD) is one of the most common diseases affecting millions of people worldwide. The use of existing antidepressants in many cases does not allow achieving stable remission, probably due to insufficient understanding of pathological mechanisms. This indicates the need for the development of more effective drugs based on in-depth understanding of MDD's pathophysiology. Since the high costs and long duration of the development of new drugs, the drug repositions may be the promising alternative. In this study we have applied the recently developed DIGEP-Pred approach to identify drugs that induce changes in expression of genes associated with the etiopathogenesis of MDD, followed by identification of their potential MDD-related targets and molecular mechanisms of the antidepressive effects. The applied approach included the following steps. First, using structure-activity relationships (SARs) we predicted drug-induced gene expression changes for 3690 worldwide approved drugs. Disease enrichment analysis applied to the predicted genes allowed to identify drugs that significantly altered expression of known MDD-related genes. Second, potential drug targets, which are probable master regulators responsible for drug-induced gene expression changes, have been identified through the SAR-based prediction and network analysis. Only those drugs whose potential targets were clearly associated with MDD according to the published data, were selected for further analysis. Third, since potential new antidepressants must distribute into brain tissues, drugs with an oral route of administration were selected and their blood-brain barrier permeability was estimated using available experimental data and in silico predictions. As a result, we identified 19 drugs, which can be potentially repurposed for the MDD treatment. These drugs belong to various therapeutic categories, including adrenergic/dopaminergic agents, antiemetics, antihistamines, antitussives, and muscle relaxants. Many of these drugs have experimentally confirmed or predicted interactions with well-known MDD-related protein targets such as monoamine (serotonin, adrenaline, dopamine) and acetylcholine receptors and transporters as well as with less trivial targets including galanin receptor type 3 (GALR3), G-protein coupled estrogen receptor 1 (GPER1), tyrosine-protein kinase JAK3, serine/threonine-protein kinase ULK1. Importantly, that the most of 19 drugs act on two or more MDD-related targets, which may produce the stronger action on gene expression changes and achieve a potent therapeutic effect. Thus, the revealed 19 drugs may represent the promising candidates for the treatment of MDD.

Cerebral malaria (CM) is a fatal complication of Plasmodium falciparum infection. The biological and physiological links between CM, inflammation, and inflammasome, point to the complexity of its pathology. Resistance to available and affordable drugs, worsening economic crisis, and urgent need for integration of orthodox with traditional/alternative medicine, actualized the search for sustainable pharmacotherapy. Previous works from our team on the medicinal properties of bitter honey have established botanical and bioactive markers, inhibitory effects on pancreatic alpha-amylase activity, and anti-dyslipidemia, cardio-protective, and ameliorative effects on hepatorenal damage in streptozotocin-induced diabetic rats. In this study, we have identified bitter honey (BH) derived phytochemicals using gas chromatography coupled with mass spectrometry (GC-MS), and 9 targets from genes associated with CM, inflammation, inflammasome, and BH phytochemicals. Network analysis revealed significant functional and physical interactions among these targets and NOD-, LRR-, and pyrin domain-containing protein 3 (NLRP3). Molecular docking of bitter honey-derived phytochemicals against these targets identified 3 most promising phytochemical candidates for further experimental validation. Based on these results, we predict that bitter honey may aid in the suppression of CM-mediated inflammasome cell death via its interactions with these targets.

A previously developed algorithm for the preliminary identification of protein proteoforms associated with post-translational modifications (PTMs) based on 2D electrophoresis data (DOI: 10.18097/BMCRM00191) has been used in this study for analysis of experimental data obtained using mice and reported in two papers by different authors. The authors of the first paper identified 8 groups of spots on 2D electrophoretic maps corresponding to 8 proteins with at least two unconcretised proteoforms. The authors of the second paper analyzed brain samples by means of the LC-MS/MS. In this study identification of peptides with PTMs was repeated using the raw data from the second paper. Among the 8 target proteins, 7 were identified in most of the biological samples. For 4 of them, 17 possible peptides with modifications were found. The 5 proteoform variants with identified PTMs matched the spots on the 2D electrophoresis maps. Thus, the prediction of pI values for proteins with hypothetical PTMs allows to form a set of hypotheses about the presence of particular proteoforms on the 2D electrophoretic maps.