Computational Prediction Research Articles

Experimental and numerical comparison of aerodynamics for a wind turbine rotor model under turbulent inflow conditions

Computational prediction of aerodynamics for a two-bladed wind turbine rotor model at the profile, blade, and rotor scale are compared to wind tunnel experimental data. Wind tunnel tests are carried out in the large subsonic wind tunnel at the University of Orleans with an instrumented rotor model to obtain power and thrust coefficients, bending moments and pressure chordwise distributions. A passive uniform turbulence grid is used to obtain a turbulence intensity of 3.8% at the front of the rotor. A k − ω SST unsteady Reynolds-averaged Navier-Stockes (URANS) simulation with the ISIS-CFD flow solver is conducted with a fully resolved rotor, with and without automatic grid refinement. The focus of this study is to examine the prediction capabilities of the simulation for various physical variables. The simulation demonstrates a relatively good prediction of power and thrust coefficients compared with experimental data with a maximum scatter of 8.5%. The prediction of the pressure coefficient distribution at three different radial positions is satisfactory, but shows discrepancies when it comes to accurately predicting flow separation and Reynolds effects.

Harnessing Computational Modeling for Efficient Drug Design Strategies

Abstract: Computational modeling has become a crucial tool in drug design, offering efficiency and cost-effectiveness. This paper discusses the various computational modeling techniques used in drug design and their role in enabling efficient drug discovery strategies. Molecular docking predicts the binding affinity of a small molecule to a target protein, allowing the researchers to identify potential lead compounds and optimize their interactions. Molecular dynamics simulations provide insights into protein-ligand complexes, enabling the exploration of conformational changes, binding free energies, and fundamental protein-ligand interactions. Integrating computational modeling with machine learning algorithms, such as QSAR modeling and virtual screening, enables the prediction of compound properties and prioritizes potential drug candidates. High-performance computing resources and advanced algorithms are essential for accelerating drug design workflows, with parallel computing, cloud computing, and GPU acceleration reducing computational time. The paper also addresses the challenges and limitations of computational modeling in drug design, such as the accuracy of scoring functions, protein flexibility representation, and validation of predictive models. It emphasizes the need for experimental validation and iterative refinement of computational predictions to ensure the reliability and efficacy of designed drugs.

Conservation of HLA Spike Protein Epitopes Supports T Cell Cross-Protection in SARS-CoV-2 Vaccinated Individuals against the Potentially Zoonotic Coronavirus Khosta-2.

Heterologous vaccines, which induce immunity against several related pathogens, can be a very useful and rapid way to deal with new pandemics. In this study, the potential impact of licensed COVID-19 vaccines on cytotoxic and helper cell immune responses against Khosta-2, a novel sarbecovirus that productively infects human cells, was analyzed for the 567 and 41 most common HLA class I and II alleles, respectively. Computational predictions indicated that most of these 608 alleles, covering more than 90% of the human population, contain sufficient fully conserved T-cell epitopes between the Khosta-2 and SARS-CoV-2 spike-in proteins. Ninety percent of these fully conserved peptides for class I and 93% for class II HLA molecules were verified as epitopes recognized by CD8+ or CD4+ T lymphocytes, respectively. These results show a very high correlation between bioinformatic prediction and experimental assays, which strongly validates this study. This immunoinformatics analysis allowed a broader assessment of the alleles that recognize these peptides, a global approach at the population level that is not possible with experimental assays. In summary, these findings suggest that both cytotoxic and helper cell immune protection elicited by currently licensed COVID-19 vaccines should be effective against Khosta-2 virus infection. Finally, by being rapidly adaptable to future coronavirus pandemics, this study has potential public health implications.

Calibrationless monocular vision musculoskeletal simulation during gait

With computer vision technology and prediction of ground reaction forces (GRF), a previous study performed markerless motion capture and musculoskeletal simulation with two smartphones (OpenCap). A recent approach can reconstruct 3D human motion from a single video without calibration and it may further simplify the motion capture process. However it has not been combined with musculoskeletal simulation and the validity is unclear. Therefore, the purpose of this study was to determine the validity of the musculoskeletal simulation using a monocular vision approach. An open-source dataset that contains motion capture and video data during gait from 10 healthy participants was used. Human motion reconstruction with the skinned human (SMPL) model was performed on each video. Virtual marker data was generated by extracting the position data from the SMPL skin vertices. Inverse kinematics, GRF prediction (only for monocular vision approach), inverse dynamics and static optimization were performed using a musculoskeletal model for experimental motion capture data and the generated virtual markers from videos. Mean absolute errors (MAE) between motion capture based and monocular vision based simulation outcomes were calculated. The MAE were 8.4° for joint angles, 5.0 % bodyweight for GRF, 1.1 % bodyweight*height for joint moments and 0.11 for estimated muscle activations from 16 muscles. The entire MAE was larger but some were comparable to OpenCap. Using the monocular vision approach, motion capture and musculoskeletal simulation can be done with no preparations and is beneficial for clinicians to quantify the daily gait assessment.

Computational prediction of fast ion conductor for all-solid-state lithium-ion batteries: A case study of Li3ZrI6

All-solid-state lithium (Li) ion batteries (ASSLIBs) have attracted widespread attention due to their sufficient energy density and safety. Further advancement of ASSLIBs necessitates the discovery of solid-state electrolyte (SSE) materials. In this work, we conducted a computational investigation utilizing first-principles calculations to identify the functional parameters and interfacial properties of a novel iodide-based ion conductor (Li3ZrI6). The Li3ZrI6 exhibited excellent Li-ion conductivity and robust chemical/electrochemical stability, emerging as a promising SSE candidate. This research could facilitate the pursuit of high-performance SSEs for related applications.

Protein language models enable prediction of polyreactivity of monospecific, bispecific, and heavy-chain-only antibodies.

Early assessment of antibody off-target binding is essential for mitigating developability risks such as fast clearance, reduced efficacy, toxicity, and immunogenicity. The baculovirus particle (BVP) binding assay has been widely utilized to evaluate polyreactivity of antibodies. As a complementary approach, computational prediction of polyreactivity is desirable for counter-screening antibodies from in silico discovery campaigns. However, there is a lack of such models. Herein, we present the development of an ensemble of three deep learning models based on two pan-protein foundational protein language models (ESM2 and ProtT5) and an antibody-specific protein language model (PLM) (Antiberty). These models were trained in a transfer learning network to predict the outcomes in the BVP assay and the bovine serum albumin binding assay, which was developed as a complement to the BVP assay. The training was conducted on a large dataset of antibody sequences augmented with experimental conditions, which were collected through a highly efficient application system. The resulting models demonstrated robust performance on canonical mAbs (monospecific with heavy and light chain), bispecific Abs, and single-domain Fc (VHH-Fc). PLMs outperformed a model built using molecular descriptors calculated from AlphaFold 2 predicted structures. Embeddings from the antibody-specific and foundational PLMs resulted in similar performance. To our knowledge, this represents the first application of PLMs to predict assay data on bispecifics and VHH-Fcs.

Modeling Charged Defects Using the Defect Energy Formalism With a Charge Sublattice Within a CALPHAD Framework

AbstractThe non‐stoichiometry of material compounds significantly influences their functional properties. In semiconductors, point defects determine whether a material is n‐type or p‐type and the concentration of charge carriers. Even in structural alloys and ion conductors used in batteries and fuel cells, non‐stoichiometry and dilute defects play an integral role in their function. The design of metal alloys has advanced rapidly with the aid of thermodynamic modeling using calculation of phase diagrams (CALPHAD) to predict the phases produced during different processing conditions. While thermodynamic modeling has been done previously by fitting experimental data, the advent of first‐principle techniques has enabled the computational prediction of material properties. The defect energy formalism (DEF) is described for modeling charged and uncharged defects in compounds, showing that it accurately predicts non‐stoichiometry using density functional theory (DFT), without fitting experiments. The model reproduces the expected defect and free‐charge carrier concentrations using the statistical mechanics approach commonly used in most DFT defect studies. Finally, the model is used to accurately predict the single‐phase region of PbTe with no fitting to measured defect concentrations. This method can revolutionize materials development of insulators, semiconductors, and even metals by allowing rapid DFT calculations to replace laborious experiments when dilute defects are involved.

MALT1 substrate cleavage: what is it good for?

CARD-BCL10-MALT1 (CBM) signalosomes connect distal signaling of innate and adaptive immune receptors to proximal signaling pathways and immune activation. Four CARD scaffold proteins (CARD9, 10, 11, 14) can form seeds that nucleate the assembly of BCL10-MALT1 filaments in a cell- and stimulus-specific manner. MALT1 (also known as PCASP1) serves a dual function within the assembled CBM complexes. By recruiting TRAF6, MALT1 acts as a molecular scaffold that initiates IκB kinase (IKK)/NF-κB and c-Jun N-terminal kinase (JNK)/AP-1 signaling. In parallel, proximity-induced dimerization of the paracaspase domain activates the MALT1 protease which exerts its function by cleaving a set of specific substrates. While complete MALT1 ablation leads to immune deficiency, selective destruction of either scaffolding or protease function provokes autoimmune inflammation. Thus, balanced MALT1-TRAF6 recruitment and MALT1 substrate cleavage are critical to maintain immune homeostasis and to promote optimal immune activation. Further, MALT1 protease activity drives the survival of aggressive lymphomas and other non-hematologic solid cancers. However, little is known about the relevance of the cleavage of individual substrates for the pathophysiological functions of MALT1. Unbiased serendipity, screening and computational predictions have identified and validated ~20 substrates, indicating that MALT1 targets a quite distinct set of proteins. Known substrates are involved in CBM auto-regulation (MALT1, BCL10 and CARD10), regulation of signaling and adhesion (A20, CYLD, HOIL-1 and Tensin-3), or transcription (RelB) and mRNA stability/translation (Regnase-1, Roquin-1/2 and N4BP1), indicating that MALT1 often targets multiple proteins involved in similar cellular processes. Here, we will summarize what is known about the fate and functions of individual MALT1 substrates and how their cleavage contributes to the biological functions of the MALT1 protease. We will outline what is needed to better connect critical pathophysiological roles of the MALT1 protease with the cleavage of distinct substrates.

Computational Strategies in Drug Discovery: Leveraging Subtractive Genomic Analysis for Target Identification.

The utilization of In-silico subtractive genomic analysis has emerged as an important and essential method in modern drug discovery and development since it significantly improves the process of identifying and validating potential targets for discovering novel therapeutic compounds to treat severe infections caused by Antimicrobial-resistant (AMR) - pathogenic species. This review provides a complete overview of the methodology, advantages, disadvantages, and prospects, associated with subtractive genomic research in the context of drug discovery and development. The initial phase of analysis encompasses the retrieval of data, which serves as a foundation for the subsequent data mining process in Phase 1. After data mining, Phase 2 utilizes subtractive channels for the target's non-homology and essentiality analysis. Phase 3 of the study aims to provide a comprehensive understanding of prospective targets by their qualitative characterization. Further, Phase 4 of the study emphasizes on conducting structure-based analyses, which involves the determination, refinement, evaluation, and validation of three-dimensional structures of the target proteins, along with their active site prediction and selection of the novel therapeutic compounds against that active site on the obtained targets through virtual screening and docking studies by utilizing various databases and servers. The therapeutic compounds obtained can be then validated by in vitro and in vivo testing, thereby establishing a connection between the computational predictions and real applications.

Identification and molecular docking of novel antioxidant peptides from Candida utilis

The current trend is the promotion of antioxidants that are beneficial for both health and the environment. Candida utilis have garnered considerable attention due to their commendable attributes such as non-toxicity and the ability to thrive in waste. Therefore, Candida utilis was used as raw material to isolate and identify new antioxidant peptides by employing methods such as ultrafiltration, DEAE Sepharose Fast Flow, and liquid chromatography-tandem mass spectrometry. The antioxidant mechanism of peptides was investigated by molecular docking. The properties of antioxidant peptides were evaluated using a variety of computational tools. This study resulted in the identification of two novel antioxidant peptides. According to the molecular docking results, the antioxidant mechanism of Candida utilis peptides operates by obstructing the entry to the myeloperoxidase activity cavity. The (−) CDOCKER energy of antioxidant peptides was 6.2 and 6.1 kcal/mol, respectively. Additionally, computer predictions indicated that antioxidant peptides exhibited non-toxicity and poor solubility.

Molecular Signatures for Microbe-Associated Colorectal Cancers.

Genetic factors and microbial imbalances play crucial roles in colorectal cancers (CRCs), yet the impact of infections on cancer initiation remains poorly understood. While bioinformatic approaches offer valuable insights, the rising incidence of CRCs creates a pressing need to precisely identify early CRC events. We constructed a network model to identify continuum states during CRC initiation spanning normal colonic tissue to pre-cancer lesions (adenomatous polyps) and examined the influence of microbes and host genetics. A Boolean network was built using a publicly available transcriptomic dataset from healthy and adenoma affected patients to identify an invariant Microbe-Associated Colorectal Cancer Signature (MACS). We focused on Fusobacterium nucleatum ( Fn ), a CRC-associated microbe, as a model bacterium. MACS-associated genes and proteins were validated by RT-qPCR, RNA seq, ELISA, IF and IHCs in tissues and colon-derived organoids from genetically predisposed mice ( CPC-APC Min+/- ) and patients (FAP, Lynch Syndrome, PJS, and JPS). The MACS that is upregulated in adenomas consists of four core genes/proteins: CLDN2/Claudin-2 (leakiness), LGR5/leucine-rich repeat-containing receptor (stemness), CEMIP/cell migration-inducing and hyaluronan-binding protein (epithelial-mesenchymal transition) and IL8/Interleukin-8 (inflammation). MACS was induced upon Fn infection, but not in response to infection with other enteric bacteria or probiotics. MACS induction upon Fn infection was higher in CPC-APC Min+/- organoids compared to WT controls. The degree of MACS expression in the patient-derived organoids (PDOs) generally corresponded with the known lifetime risk of CRCs. Computational prediction followed by validation in the organoid-based disease model identified the early events in CRC initiation. MACS reveals that the CRC-associated microbes induce a greater risk in the genetically predisposed hosts, suggesting its potential use for risk prediction and targeted cancer prevention.

A framework for the biophysical screening of antibody mutations targeting solvent-accessible hydrophobic and electrostatic patches for enhanced viscosity profiles

The formulation of high-concentration monoclonal antibody (mAb) solutions in low dose volumes for autoinjector devices poses challenges in manufacturability and patient administration due to elevated solution viscosity. Often many therapeutically potent mAbs are discovered, but their commercial development is stalled by unfavourable developability challenges. In this work, we present a systematic experimental framework for the computational screening of molecular descriptors to guide the design of 24 mutants with modified viscosity profiles accompanied by experimental evaluation. Our experimental observations using a model anti-IL8 mAb and eight engineered mutant variants reveal that viscosity reduction is influenced by the location of hydrophobic interactions, while targeting positively charged patches significantly increases viscosity in comparison to wild-type anti-IL-8 mAb. We conclude that most predicted in silico physicochemical properties exhibit poor correlation with measured experimental parameters for antibodies with suboptimal developability characteristics, emphasizing the need for comprehensive case-by-case evaluation of mAbs. This framework combining molecular design and triage via computational predictions with experimental evaluation aids the agile and rational design of mAbs with tailored solution viscosities, ensuring improved manufacturability and patient convenience in self-administration scenarios.

Deposited footprints let cells switch between confined, oscillatory, and exploratory migration

For eukaryotic cells to heal wounds, respond to immune signals, or metastasize, they must migrate, often by adhering to extracellular matrix (ECM). Cells may also deposit ECM components, leaving behind a footprint that influences their crawling. Recent experiments showed that some epithelial cell lines on micropatterned adhesive stripes move persistently in regions they have previously crawled over, where footprints have been formed, but barely advance into unexplored regions, creating an oscillatory migration of increasing amplitude. Here, we explore through mathematical modeling how footprint deposition and cell responses to footprint combine to allow cells to develop oscillation and other complex migratory motions. We simulate cell crawling with a phase field model coupled to a biochemical model of cell polarity, assuming local contact with the deposited footprint activates Rac1, a protein that establishes the cell's front. Depending on footprint deposition rate and response to the footprint, cells on micropatterned lines can display many types of motility, including confined, oscillatory, and persistent motion. On two-dimensional (2D) substrates, we predict a transition between cells undergoing circular motion and cells developing an exploratory phenotype. Small quantitative changes in a cell's interaction with its footprint can completely alter exploration, allowing cells to tightly regulate their motion, leading to different motility phenotypes (confined vs. exploratory) in different cells when deposition or sensing is variable from cell to cell. Consistent with our computational predictions, we find in earlier experimental data evidence of cells undergoing both circular and exploratory motion.

Computational prediction of new therapeutic effects of probiotics

Probiotics are living microorganisms that provide health benefits to their hosts, potentially aiding in the treatment or prevention of various diseases, including diarrhea, irritable bowel syndrome, ulcerative colitis, and Crohn’s disease. Motivated by successful applications of link prediction in medical and biological networks, we applied link prediction to the probiotic-disease network to identify unreported relations. Using data from the Probio database and International Classification of Diseases-10th Revision (ICD-10) resources, we constructed a bipartite graph focused on the relationship between probiotics and diseases. We applied customized link prediction algorithms for this bipartite network, including common neighbors, Jaccard coefficient, and Adamic/Adar ranking formulas. We evaluated the results using Area under the Curve (AUC) and precision metrics. Our analysis revealed that common neighbors outperformed the other methods, with an AUC of 0.96 and precision of 0.6, indicating that basic formulas can predict at least six out of ten probable relations correctly. To support our findings, we conducted an exact search of the top 20 predictions and found six confirming papers on Google Scholar and Science Direct. Evidence suggests that Lactobacillus jensenii may provide prophylactic and therapeutic benefits for gastrointestinal diseases and that Lactobacillus acidophilus may have potential activity against urologic and female genital illnesses. Further investigation of other predictions through additional preclinical and clinical studies is recommended. Future research may focus on deploying more powerful link prediction algorithms to achieve better and more accurate results.

Predicting functional UTR variants by integrating region-specific features.

The untranslated region (UTR) of messenger ribonucleic acid (mRNA), including the 5'UTR and 3'UTR, plays a critical role in regulating gene expression and translation. Variants within the UTR can lead to changes associated with human traits and diseases; however, computational prediction of UTR variant effect is challenging. Current noncoding variant prediction mainly focuses on the promoters and enhancers, neglecting the unique sequence of the UTR and thereby limiting their predictive accuracy. In this study, using consolidated datasets of UTR variants from disease databases and large-scale experimental data, we systematically analyzed more than 50 region-specific features of UTR, including functional elements, secondary structure, sequence composition and site conservation. Our analysis reveals that certain features, such as C/G-related sequence composition in 5'UTR and A/T-related sequence composition in 3'UTR, effectively differentiate between nonfunctional and functional variant sets, unveiling potential sequence determinants of functional UTR variants. Leveraging these insights, we developed two classification models to predict functional UTR variants using machine learning, achieving an area under the curve (AUC) value of 0.94 for 5'UTR and 0.85 for 3'UTR, outperforming all existing methods. Our models will be valuable for enhancing clinical interpretation of genetic variants, facilitating the prediction and management of disease risk.

SincFold: end-to-end learning of short- and long-range interactions in RNA secondary structure.

Coding and noncoding RNA molecules participate in many important biological processes. Noncoding RNAs fold into well-defined secondary structures to exert their functions. However, the computational prediction of the secondary structure from a raw RNA sequence is a long-standing unsolved problem, which after decades of almost unchanged performance has now re-emerged due to deep learning. Traditional RNA secondary structure prediction algorithms have been mostly based on thermodynamic models and dynamic programming for free energy minimization. More recently deep learning methods have shown competitive performance compared with the classical ones, but there is still a wide margin for improvement. In this work we present sincFold, an end-to-end deep learning approach, that predicts the nucleotides contact matrix using only the RNA sequence as input. The model is based on 1D and 2D residual neural networks that can learn short- and long-range interaction patterns. We show that structures can be accurately predicted with minimal physical assumptions. Extensive experiments were conducted on several benchmark datasets, considering sequence homology and cross-family validation. sincFold was compared with classical methods and recent deep learning models, showing that it can outperform the state-of-the-art methods.

A computational analysis of the role of integrins and Rho-GTPases in the emergence and disruption of apical-basal polarization in renal epithelial cells.

Apical-basal polarization in renal epithelial cells is crucial to renal function and an important trigger for tubule formation in kidney development. Loss of polarity can induce epithelial-to-mesenchymal transition (EMT), which can lead to kidney pathologies. Understanding the relative and combined roles of the involved proteins and their interactions that govern epithelial polarity may provide insights for controlling the process of polarization via chemical or mechanical manipulations in an in vitro or in vivo setting. Here, we developed a computational framework that integrates several known interactions between integrins, Rho-GTPases Rho, Rac and Cdc42, and polarity complexes Par and Scribble, to study their mutual roles in the emergence of polarization. The modeled protein interactions were shown to induce the emergence of polarized distributions of Rho-GTPases, which in turn led to the accumulation of apical and basal polarity complexes Par and Scribble at their respective poles, effectively recapitulating polarization. Our multiparametric sensitivity analysis suggested that polarization depends foremost on the mutual inhibition between Rac and Rho. Next, we used the computational framework to investigate the role of integrins and GTPases in the generation and disruption of polarization. We found that a minimum concentration of integrins is required to catalyze the process of polarization. Furthermore, loss of polarization was found to be only inducible via complete degradation of the Rho-GTPases Rho and Cdc42, suggesting that polarization is fairly stable once it is established. Comparison of our computational predictions against data from in vitro experiments in which we induced EMT in renal epithelial cells while quantifying the relative Rho-GTPase levels, displayed that EMT coincides with a large reduction in the Rho-GTPase Rho. Collectively, these results demonstrate the essential roles of integrins and Rho-GTPases in the establishment and disruption of apical-basal polarity and thereby provide handles for the in vitro or in vivo regulation of polarity.

Machine learning-assisted high-throughput screening of MOFs for efficient adsorption and separation of CF4/N2

There is an imperative need for top-performing materials with extraordinary adsorption selectivity and working capacity, in order to achieve productive adsorption of CF4 in a CF4/N2 mixture. In this work, the High-Throughput Grand Canonical Monte Carlo (HT-GCMC) simulation method and the Machine Learning (ML) method were employed to predict and screen the adsorption performance of 10 143 computation-ready experimental metal-organic frameworks (CoRE-MOFs) for separating CF4/N2 mixed gas. Through computational simulation and ML prediction, 15 and 73 highly promising adsorbents were selected out of the 690 randomly sampled MOFs and the CoRE-MOFs database. The selection process was based on criteria that balanced favorable CF4 selectivity, working capacity, and regenerability: selectivity >60, working capacity >70 mg g−1 (0.8 mmol g−1) and regenerability >70%. The maximum observed capacity of the 15 top evaluated metal-organic frameworks (MOFs) was: 52.85 mg g−1 (0.6 mmol g−1) at 1 bar; and 204.90 mg g−1 (2.3 mmol g−1) at 10 bar. The maximum working capacity was 152.06 mg g−1 (1.7 mmol g−1) and the highest selectivity reached was 118.12 (YEGCUJ) and 101.80 (VEHLIE) at 1 bar and 10 bar, respectively. Notably, the most promising MOFs exhibited elevated Zn content relative to the overall MOF population and also possessed a significant nitrogen content. This result should serve as a compelling motivation to further investigate the utilisation of MOFs with a high Zn content (e. g. zeolitic imidazolate frameworks), for enhanced adsorption applications.

IN SILICO PREDICTION OF POTENTIAL DERMATOLOGICAL EFFECTS OF A SYNTHETIC ANTIOXIDANT

Nowadays, in silico prediction has become standard in the development of new drugs. Computer screening of known drugs provides an efficient approach to repurposing these agents for new indications. This sparked our interest in conducting an in silico analysis of ethylmethylhydroxypyridine succinate (EMHPS) for pharmacological effects potentially useful in dermatology. The aim of this study is to perform an in silico search for the effects of the synthetic antioxidant EMHPS that are potentially useful for the treatment of skin diseases. Retrospective computer prediction of the EMGPS structure was performed using the Drug2ways methodical approach and the PASS (Prediction of Activity Spectra for Substances) program online. The EMHPS molecule consists of two active fragments, which were analyzed separately. In the structure of 2-ethyl-6-methyl-3-hydroxypyridine, 125 types of activity were predicted, while in succinate - 734 types of activity. From the array of prognostic data, effects that may be relevant for the treatment of skin diseases were extracted. These include anti-seborrheic, anti-alopecia, anti-eczema, anti-pruritic, and anti-infectious effects, as well as photosensitizing action and lack of skin irritation. According to the forecast, these effects may be due to the influence of the components of the EMHPS molecule on membranes, antioxidant protection, the state of connective tissue, enzymes, and signaling pathways involved in the development of inflammation, as well as on the pathogenicity factors of microorganisms. Therefore, in silico screening of new types of activity in EMHPS allowed us to predict some effects useful in the treatment of dermatological pathology. Undoubtedly, the results of the forecast must be verified by practice, but they allow us to create a working hypothesis and approach the planning of experiments more rationally, which corresponds to modern trends in pharmacology.

STUDYING THE HYPOLYPIDEMIC ACTIVITY OF FLAVONOIDS AND ISOFLAVONOIDS OF THE ONONIS ARVENSIS L. METHODS BY IN SILICO

Introduction. Today, one of the reliably known causes of mortality in the Russian Federation is diseases of the cardiovascular system, a significant part of which is associated with atherosclerotic disease. Combination therapy for diseases of the cardiovascular system includes, among other things, the use of modern lipid-lowering drugs, the use of which is often limited due to their pronounced side effects. In this regard, it seemed appropriate to search for new compounds of natural origin that potentially have lipid-lowering activity with minimal side effects. According to the scientific literature, natural phenolic compounds, namely substances from the group of flavonoids and isoflavonoids, have a set of such characteristics. In this regard, steelgrass (Ononis arvensis L.), the chemical composition of which is extremely rich and diverse in terms of flavonoids and isoflavonoids, can be a ra-ther promising source for searching and screening compounds with a given activity. The aim of the study. The purpose of the work was to study and predict the hypolipidemic activity of flavonoids and isoflavonoids of Ononis arvensis L. using in silico methods. Material and methods. The objects of the study were the structural formulas of flavonoids and isoflavonoids of field steelhead. Calculation of mo-lecular properties was carried out using the Molinspiration chemoinformatic software. Computer prediction of lipid-lowering activity was carried out us-ing the PASS-online service. Molecular docking was performed using the CB-Dock2 services for blind docking and Webina 1.0.5 for active site docking. Hepatotoxicity, mutagenicity and cytotoxicity of the analyzed biologically active substances were studied using the ProTox-II resource. Results. As a result of in silico studies, it was found that most of the studied flavonoids and isoflavonoids correspond to the Lipinski rule and the drug-likeness concept. In addition, for all studied biologically active substances, activities associated with a decrease in lipid fractions in the body were predicted. The results of molecular docking indicate that all analyzed compounds are capable of potentially inhibiting the enzyme HMG-CoA reductase, which makes it possible to predict the required lipid-lowering effect. Studying the toxicity of the research objects, most of them in silico demonstrated a high level of safety. Conclusions. The prospects for further research on the development of targeted technology for obtaining herbal preparations from steelhead, enriched with flavonoids and isoflavonoids, as well as subsequent tests to confirm hypolipidemic activity in in vitro and in vivo experiments are shown.