Molecular Modeling Of Interaction Research Articles

Molecular permeation through large pore channels: computational approaches and insights.

Computational methods such as molecular dynamics (MD) have illuminated how single-atom ions permeate membrane channels and how selectivity among them is achieved. Much less is understood about molecular permeation through eukaryotic channels that mediate the flux of small molecules (e.g. connexins, pannexins, LRRC8s, CALHMs). Here we describe computational methods that have been profitably employed to explore the movements of molecules through wide pores, revealing mechanistic insights, guiding experiments, and suggesting testable hypotheses. This review illustrates MD techniques such as voltage-driven flux, potential of mean force, and mean first-passage-time calculations, as applied to molecular permeation through wide pores. These techniques have enabled detailed and quantitative modeling of molecular interactions and movement of permeants at the atomic level. We highlight novel contributors to the transit of molecules through these wide pathways. In particular, the flexibility and anisotropic nature of permeant molecules, coupled with the dynamics of pore-lining residues, lead to bespoke permeation dynamics. As more eukaryotic large-pore channel structures and functional data become available, these insights and approaches will be important for understanding the physical principles underlying molecular permeation and as guides for experimental design.

Mechanisms of RORα-dependent effects of melatonin

The transcription factor RORα has not traditionally been attributed a fundamental role in the development of Th17 cells, but recent studies have shown that it is necessary for the formation of a pathogenic variant of Th17 cells, the so-called Th1-polarized Th17 (Th17.1). Since the transcriptional activity of RORα depends on ligand binding, the search for such ligands is highly relevant, and in this regard, melatonin is of particular interest. The question of the ability of RORα to directly bind melatonin remains open today; data on this problem are extremely contradictory. In 1995, I. Wiesenberg and colleagues identified RORα as a nuclear receptor for melatonin, demonstrating the hormone’s ability to enhance the binding of this factor to DNA and determining the dissociation constant for the interaction of RORα with melatonin using classical Scatchard analysis. In 2011, P.J. Lardone and colleagues “rediscovered” RORα as a receptor for melatonin by demonstrating the coprecipitation of melatonin with RORα. And in 2016, A.J. Slominski and colleagues published a paper that cast doubt on the possibility of melatonin binding to RORα based on molecular modeling of ligand-receptor interactions supported by functional studies. However, a careful analysis of these data indicates the ambiguity of this conclusion, allowing us to speak, rather, of medium or low binding affinity of the hormone to RORα, but not of its absence. This conclusion is also supported by the fact that RORα mediates many of the effects of melatonin, both physiological and pharmacological, including the regulation of circadian rhythms and oxidative metabolism, neuro- and cardioprotection, and control of the immune response. In general, the data available today allow us to consider the transcription factor RORα as a receptor for melatonin with medium affinity, although indirect regulation of this factor by the hormone is not excluded, and RORα-dependent mechanisms should contribute to the cellular response to melatonin, both under physiological conditions and in the case of pharmacological use of the hormone.

Asymptotically consistent and computationally efficient modeling of short-ranged molecular interactions between curved slender fibers undergoing large 3D deformations

This article proposes a novel computational modeling approach for short-ranged molecular interactions between curved slender fibers undergoing large 3D deformations, and gives a detailed overview how it fits into the framework of existing fiber or beam interaction models, either considering microscale molecular or macroscale contact effects. The direct evaluation of a molecular interaction potential between two general bodies in 3D space would require to integrate molecule densities over two 3D volumes, leading to a sixfold integral to be solved numerically. By exploiting the short-range nature of the considered class of interaction potentials as well as the fundamental kinematic assumption of undeformable fiber cross-sections, as typically applied in mechanical beam theories, a recently derived, closed-form analytical solution is applied for the interaction potential between a given section of the first fiber (slave beam) and the entire second fiber (master beam), whose geometry is linearly expanded at the point with smallest distance to the given slave beam section. This novel approach based on a pre-defined section–beam interaction potential (SBIP) requires only one single integration step along the slave beam length to be performed numerically. In addition to significant gains in computational efficiency, the total beam–beam interaction potential resulting from this approach is shown to exhibit an asymptotically consistent angular and distance scaling behavior. Critically for the numerical solution scheme, a regularization of the interaction potential in the zero-separation limit as well as the finite element discretization of the interacting fibers, modeled by the geometrically exact beam theory, are presented. In addition to elementary two-fiber systems, carefully chosen to verify accuracy and asymptotic consistence of the proposed SBIP approach, a potential practical application in form of adhesive nanofiber-grafted surfaces is studied. Involving a large number of helicoidal fibers undergoing large 3D deformations, arbitrary mutual fiber orientations as well as frequent local fiber pull-off and snap-into-contact events, this example demonstrates the robustness and computational efficiency of the new approach.

The Interaction between ADK and SCG10 Regulate the Repair of Nerve Damage

The cytoskeleton must be remodeled during neurite outgrowth, and Superior Cervical Ganglion 10 (SCG10) plays a critical role in this process by depolymerizing Microtubules (MTs), conferring highly dynamic properties to the MTs. However, the precise mechanism of action of SCG10 in the repair of injured neurons remains largely uncertain. Using transcriptomic identification, we discovered that SCG10 expression was downregulated in neurons after Spinal Cord Injury (SCI). Additionally, through mass spectrometry identification, immunoprecipitation, and pull-down assays, we established that SCG10 could interact with Adenosine Kinase (ADK). Furthermore, we developed an excitotoxicity-induced neural injury model and discovered that ADK suppressed injured neurite re-growth, whereas, through overexpression and small molecule interference experiments, SCG10 enhanced it. Moreover, we discovered ADK to be the upstream of SCG10. More importantly, the application of the ADK inhibitor called 5-Iodotubercidin (5-ITu) was found to significantly enhance the recovery of motor function in mice with SCI. Consequently, our findings suggest that ADK plays a negative regulatory role in the repair of injured neurons. Herein, we propose a molecular interaction model of the SCG10-ADK axis to regulate neuronal recovery.

Dynamic Boolean modeling of molecular and cellular interactions in psoriasis predicts drug target candidates

Psoriasis arises from complex interactions between keratinocytes and immune cells, leading to uncontrolled inflammation, immune hyperactivation, and a perturbed keratinocyte life cycle. Despite the availability of drugs for psoriasis management, the disease remains incurable. Treatment response variability calls for new tools and approaches to comprehend the mechanisms underlying disease development. We present a Boolean multiscale population model that captures the dynamics of cell-specific phenotypes in psoriasis, integrating discrete logical formalism and population dynamics simulations. Through simulations and network analysis, the model predictions suggest that targeting neutrophil activation in conjunction with inhibition of either prostaglandin E2 (PGE2) or STAT3 shows promise comparable to interleukin-17 (IL-17) inhibition, one of the most effective treatment options for moderate and severe cases. Our findings underscore the significance of considering complex intercellular interactions and intracellular signaling in psoriasis and highlight the importance of computational approaches in unraveling complex biological systems for drug target identification.

Nucleation Process in Explosive Boiling Phenomena of Water on Micro-Platinum Wire.

The maximum temperature limit at which liquid boils explosively is referred to as the superheat limit of liquid. Through various experimental studies on the superheating limit of liquids, rapid evaporation of liquids has been observed at the superheating limit. This study explored the water nucleation process at the superheat limit achieved in micro-platinum wires using a molecular interaction model. According to the molecular interaction model, the nucleation rate and time delay at 576.2 K are approximately 2.1 × 1011/(μm3μs) and 5.7 ns, respectively. With an evaporation rate (116.0 m/s) much faster than that of hydrocarbons (14.0 m/s), these readings show that explosive boiling or rapid phase transition from liquid to vapor can occur at the superheat limit of water. Subsequent bubble growth after bubble nucleation was also considered.

Biological and mutational analyses of CXCR4-antagonist interactions and design of new antagonistic analogs.

The chemokine receptor CXCR4 has become an attractive therapeutic target for HIV-1 infection, hematopoietic stem cell mobilization, and cancer metastasis. A wide variety of synthetic antagonists of CXCR4 have been developed and studied for a growing list of clinical applications. To compare the biological effects of different antagonists on CXCR4 functions and their common and/or distinctive molecular interactions with the receptor, we conducted head-to-head comparative cell-based biological and mutational analyses of the interactions with CXCR4 of eleven reported antagonists, including HC4319, DV3, DV1, DV1 dimer, V1, vMIP-II, CVX15, LY2510924, IT1t, AMD3100, and AMD11070 that were representative of different structural classes of D-peptides, L-peptide, natural chemokine, cyclic peptides, and small molecules. The results were rationalized by molecular modeling of CXCR4-antagonist interactions from which the common as well as different receptor binding sites of these antagonists were derived, revealing a number of important residues such as W94, D97, H113, D171, D262, and E288, mostly of negative charge. To further examine this finding, we designed and synthesized new antagonistic analogs by adding positively charged residues Arg to a D-peptide template to enhance the postulated charge-charge interactions. The newly designed analogs displayed significantly increased binding to CXCR4, which supports the notion that negatively charged residues of CXCR4 can engage in interactions with moieties of positive charge of the antagonistic ligands. The results from these mutational, modeling and new analog design studies shed new insight into the molecular mechanisms of different types of antagonists in recognizing CXCR4 and guide the development of new therapeutic agents.

Natural Products Inhibition of Cytochrome P450 2B6 Activity and Methadone Metabolism.

Methadone is cleared predominately by hepatic cytochrome P450 (CYP) 2B6-catalyzed metabolism to inactive metabolites. CYP2B6 also catalyzes the metabolism of several other drugs. Methadone and CYP2B6 are susceptible to pharmacokinetic drug-drug interactions. Use of natural products such as herbals and other botanicals is substantial and growing, and concomitant use of prescription medicines and non-prescription herbals is common and may result in interactions, often precipitated by CYP inhibition. Little is known about herbal product effects on CYP2B6 activity, and CYP2B6-catalyzed methadone metabolism. We screened a family of natural product compounds used in traditional medicines, herbal teas, and synthetic analogs of compounds found in plants, including kavalactones, flavokavains, chalcones and gambogic acid, for inhibition of expressed CYP2B6 activity and specifically inhibition of CYP2B6-mediated methadone metabolism. An initial screen evaluated inhibition of CYP2B6-catalyzed 7-ethoxy-4-(trifluoromethyl) coumarin O-deethylation. Hits were further evaluated for inhibition of racemic methadone metabolism, including mechanism of inhibition and kinetic constants. In order of decreasing potency, the most effective inhibitors of methadone metabolism were dihydromethysticin (competitive, K i 0.074 µM), gambogic acid (noncompetitive, K i 6 µM), and 2,2'-dihydroxychalcone (noncompetitive, K i 16 µM). Molecular modeling of CYP2B6-methadone and inhibitor binding showed substrate and inhibitor binding position and orientation and their interactions with CYP2B6 residues. These results show that CYP2B6 and CYP2B6-catalyzed methadone metabolism are inhibited by certain natural products, at concentrations which may be clinically relevant. SIGNIFICANCE STATEMENT: This investigation identified several natural product constituents which inhibit in vitro human recombinant CYP2B6 and CYP2B6-catalyzed N-demethylation of the opioid methadone. The most potent inhibitors (K i) were dihydromethysticin (0.074 µM), gambogic acid (6 µM) and 2,2'-dihydroxychalcone (16 µM). Molecular modeling of ligand interactions with CYP2B6 found that dihydromethysticin and 2,2'-dihydroxychalcone bound at the active site, while gambogic acid interacted with an allosteric site on the CYP2B6 surface. Natural product constituents may inhibit CYP2B6 and methadone metabolism at clinically relevant concentrations.

Optimizing Excipient Properties to Prevent Aggregation in Biopharmaceutical Formulations.

Excipients are included within protein biotherapeutic solution formulations to improve colloidal and conformational stability but are generally not designed for the specific purpose of preventing aggregation and improving cryoprotection in solution. In this work, we have explored the relationship between the structure and antiaggregation activity of excipients by utilizing coarse-grained molecular dynamics modeling of protein-excipient interaction. We have studied human serum albumin as a model protein, and we report the interaction of 41 excipients (polysorbates, fatty alcohol ethoxylates, fatty acid ethoxylates, phospholipids, glucosides, amino acids, and others) in terms of the reduction of solvent accessible surface area of aggregation-prone regions, proposed as a mechanism of aggregation prevention. Polyoxyethylene sorbitan had the greatest degree of interaction with aggregation-prone regions, decreasing the solvent accessible surface area of APRs by 20.7 nm2 (40.1%). Physicochemical descriptors generated by Mordred are employed to probe the structure-property relationship using partial least-squares regression. A leave-one-out cross-validated model had a root-mean-square error of prediction of 4.1 nm2 and a mean relative error of prediction of 0.077. Generally, longer molecules with a large number of alcohol-terminated PEG units tended to interact more, with qualitatively different protein interactions, wrapping around the protein. Shorter or less ethoxylated compounds tend to form hemimicellar clusters at the protein surface. We propose that an improved design would feature many short chains of 5 to 10 PEG units in many distinct branches and at least some hydrophobic content in the form of medium-length or greater aliphatic chains (i.e., six or more carbon atoms). The combination of molecular dynamics simulation and quantitative modeling is an important first step in an all-purpose protein-independent model for the computer-aided design of stabilizing excipients.

Instability onset of Rayleigh-Bénard convection for diatomic rarefied gases: Effects of molecular interaction models and flow parameters

Studies on the stability of Rayleigh-Bénard convection in rarefied gases mainly focus on monatomic gases, whereas diatomic gases are the prevailing medium in actual flows. This paper conducts a study on the Rayleigh-Bénard convection stability of diatomic rarefied gases based on the Navier-Stokes (NS) equations modified with slip boundary conditions. We aim to elaborate the effects of different molecular interaction models and flow parameters (Knudsen number Kn, Froude number Fr, Prandtl number Pr, etc.). And several interesting phenomena have been discovered: (1) Compared with monatomic gases, the instability region of diatomic gases expands towards higher Knudsen numbers and smaller Froude numbers; (2) It is generally believed that increasing rarefaction leads to flow stabilization. However, we have found that in the case of small wave numbers within the scope of our research, the rarer the gas, the higher the growth rate, which contradicts traditional cognition; (3) Competition mechanism between buoyancy and gravity in the stability of Rayleigh-Bénard convection exists; (4) When considering the variations of Prandtl number and specific heat ratio with temperature, the relative deviation of the growth rate of the most unstable mode is significant.

Metformin as a promising target for DPP4 expression: computational modeling and experimental validation

Metformin is a regularly prescribed and low-cost generic medication. Metformin has been proposed as a target for Dipeptidyl-peptidase 4 (DPP4) expression in various clinical disorders. We provide insilco investigations on molecular docking and dynamic modeling of metformin and DPP4 potential interactions. Moreover, we conducted bioinformatic studies to highlight the clinical significance of DPP4 expression and mutation in various types of malignancies, as well as the invasion of different immune cells into the tumor microenvironment. We believe the present proposal’s findings have crucial implications for understanding how metformin may confer health advantages by targeting DPP4 expression in malignancies.Graphical abstract

A Study on the Adsorption Mechanism and Compactness of the TFS Coating Interfacial Layer

Chrome-plated plates, also known as tin-free plates (TFS), are the latest substrates for coating plates. The coating plate cannot be separated from the TFS during the stamping and extension process, and the interface layer of the TFS coating plate cannot produce pores to ensure good corrosion resistance and the appearance of the metal packaging cans. This requires the TFS coating plate interfacial layer to have good adsorption and compactness. In this paper, the molecular simulation model of the interfacial layer interaction of the TFS coating plate was established by using molecular mechanics simulation, Monte Carlo simulation, and molecular dynamics simulation, and the influential rules of chromium oxide crystalline structure, coating functional group type, and coating pressure on the adsorption and compactness of interfacial layer were analyzed and verified by experiments. The results show that the adsorption is stronger when the surface of the TFS is a chromium oxide (110) crystalline surface and contains hydroxide ions. The adsorption of polyester polyurethane coating and polyether polyurethane coating for and the adsorption of polyester polyurethane coating functional groups is stronger than polyether functional groups, and the adsorption of other functional groups is ranked by the same method. The interfacial layer compactness increases with an increase in coating pressure. For this experimental sample, the value of the film pressure sensor is 18,940 g when meeting the requirements of adsorption and compactness of the interfacial layer of the TFS coating plate, which can be extended for other coating plates.

Molecular Modelling of Aromatic Interactions between Pyrene Derivatives and Carbon Nanotubes: Materials for Biomedical Applications

In previous works, we investigated the possibility of using carbon nanotubes functionalized with organic molecules as effective contrast agents for Magnetic Resonance Imaging. This a very useful method for clinical diagnosis, whose effectiveness is conditioned by the development of new contrast agents increasing the quality, resolution, and specificity of the Magnetic Resonance images. Solubilization and functionalization of carbon nanotubes have been previously reported using pyrene derivatives through π–π interactions. In this work, we used dispersion-corrected Density Functional Theory calculations to analyze interactions between carbon nanotubes and several pyrene derivatives. We built two different positions of the aromatic molecule relative to the carbon nanotube (parallel and perpendicular) and calculated binding energies, electrostatic potential surfaces, and electronic charges, in order to shed some light on the interaction strength between both molecules and their preferred orientations. A good interaction between carbon nanotubes and pyrene derivatives is key for the synthesis of materials that work efficiently in biomedical imaging. The results clearly indicate a large influence of the nature of functional groups and orientation of the aromatic molecule relative to the carbon nanotube surface on the adhesion strength.

Lactiplantibacillus plantarum and Saussurea costus as Therapeutic Agents against a Diabetic Rat Model-Approaches to Investigate Pharmacophore Modeling of Human IkB Kinase and Molecular Interaction with Dehydrocostus Lactone of Saussurea costus.

Lactic acid bacteria is well-known as a vital strategy to alleviate or prevent diabetes. Similarly, the plant Saussurea costus (Falc) Lipsch is a preventive power against diabetes. Here, we aimed to determine whether lactic acid bacteria or Saussurea costus is more effective in treating a diabetic rat model in a comparative study manner. An in vivo experiment was conducted to test the therapeutic activity of Lactiplantibacillus plantarum (MW719476.1) and S. costus plants against an alloxan-induced diabetic rat model. Molecular, biochemical, and histological analyses were investigated to evaluate the therapeutic characteristics of different treatments. The high dose of S. costus revealed the best downregulated expression for the IKBKB, IKBKG, NfkB1, IL-17A, IL-6, IL-17F, IL-1β, TNF-α, TRAF6, and MAPK genes compared to Lactiplantibacillus plantarum and the control groups. The downregulation of IKBKB by S. costus could be attributed to dehydrocostus lactone as an active compound with proposed antidiabetic activity. So, we performed another pharmacophore modeling analysis to test the possible interaction between human IkB kinase beta protein and dehydrocostus lactone as an antidiabetic drug. Molecular docking and MD simulation data confirmed the interaction between human IkB kinase beta protein and dehydrocostus lactone as a possible drug. The target genes are important in regulating type 2 diabetes mellitus signaling, lipid and atherosclerosis signaling, NF-κB signaling, and IL-17 signaling pathways. In conclusion, the S. costus plant could be a promising source of novel therapeutic agents for treating diabetes and its complications. Dehydrocostus lactone caused the ameliorative effect of S. costus by its interaction with human IkB kinase beta protein. Further, future studies could be conducted to find the clinical efficacy of dehydrocostus lactone.

Abstract 229: Endothelial Cell Handling Of Atherogenic ApoB-carrying Lipoproteins

Atherosclerosis is triggered by the subendothelial retention of cholesterol-rich, apoB -containing LDL (which carries apoB100) and chylomicron remnants (which carry apoB48, the 48% N terminal sequence of apoB). In addition, multiple studies have established elevated postprandial TRLs as an independent risk factor for CVD. However, the mechanisms whereby postprandial TRLs (which circulate mostly within chylomicrons) exert their atherogenic effects remain unknown. Two receptors - SR-BI and ALK1 - have been shown to bind apoB and mediate transcytosis of LDL across the endothelium. We have shown that SR-BI also mediates EC uptake of nascent chylomicrons. RNAseq analysis revealed that ECs exposed to chylomicrons express a host of inflammatory and immune response genes, which does not occur with knockdown of SR-BI. In addition, following chylomicron uptake ECs release small extracellular vesicles that induce lipid accumulation in macrophages and lead to reduced eNOS phosphorylation in naïve ECs. Conversely, although binding of ALK1 to apoB is believed to occur through the N-terminus of apoB, the receptor is not involved in chylomicron uptake. Using a series of N terminal apoB peptides (apoB8-18, with sequences comprising the 8-18% N terminus of apoB), we found that chylomicron uptake was inhibited by apoB15 and apoB18, and LDL uptake was additionally inhibited by apoB12. These observations suggest that a) the binding sequence to SR-BI is between residues 547-684 of apoB, and b) an additional sequence between residues 365-547 participates in the binding to ALK1. Molecular modeling of apoB interactions identified an additional sequence in the C terminus of apoB potentially involved in the binding to ALK1. This sequence is absent in chylomicrons, which may explain why ALK1 inhibition and competition with apoB12 fail to significantly inhibit chylomicron uptake. Consistently, uptake of apoB100-carrying chylomicrons from Apobec -/- mice was inhibited by both ALK1 siRNA and competition with apoB12. Our observations suggest that a) the N terminus of apoB harbors distinct binding sequences to SR-BI and ALK1, and b) chylomicron uptake by ECs contributes to atherogenesis by inducing endothelial dysfunction, inflammation, and macrophage foam cell formation.

Deep learning techniques for biomedical data processing

The interest in Deep Learning (DL) has seen an exponential growth in the last ten years, producing a significant increase in both theoretical and applicative studies. On the one hand, the versatility and the ability to tackle complex tasks have led to the rapid and widespread diffusion of DL technologies. On the other hand, the dizzying increase in the availability of biomedical data has made classical analyses, carried out by human experts, progressively more unlikely. Contextually, the need for efficient and reliable automatic tools to support clinicians, at least in the most demanding tasks, has become increasingly pressing. In this survey, we will introduce a broad overview of DL models and their applications to biomedical data processing, specifically to medical image analysis, sequence processing (RNA and proteins) and graph modeling of molecular data interactions. First, the fundamental key concepts of DL architectures will be introduced, with particular reference to neural networks for structured data, convolutional neural networks, generative adversarial models, and siamese architectures. Subsequently, their applicability for the analysis of different types of biomedical data will be shown, in areas ranging from diagnostics to the understanding of the characteristics underlying the process of transcription and translation of our genetic code, up to the discovery of new drugs. Finally, the prospects and future expectations of DL applications to biomedical data will be discussed.

A Micro-In-Macro Gastroretentive System for the Delivery of Narrow-Absorption Window Drugs.

A micro-in-macro gastroretentive and gastrofloatable drug delivery system (MGDDS), loaded with the model-drug ciprofloxacin, was developed in this study to address the limitations commonly experienced in narrow-absorption window (NAW) drug delivery. The MGDDS, which consists of microparticles loaded in a gastrofloatable macroparticle (gastrosphere) was designed to modify the release of ciprofloxacin, allowing for an increased drug absorption via the gastrointestinal tract. The prepared inner microparticles (1-4 µm) were formed by crosslinking chitosan (CHT) and Eudragit® RL 30D (EUD), with the outer gastrospheres prepared from alginate (ALG), pectin (PEC), poly(acrylic acid) (PAA) and poly(lactic-co-glycolic) acid (PLGA). An experimental design was utilized to optimize the prepared microparticles prior to Fourier Transition Infrared (FTIR) spectroscopy, Scanning Electron Microscopy (SEM) and in vitro drug release studies. Additionally, the in vivo analysis of the MGDDS, employing a Large White Pig model and molecular modeling of the ciprofloxacin-polymer interactions, were performed. The FTIR results determined that the crosslinking of the respective polymers in the microparticle and gastrosphere was achieved, with the SEM analysis detailing the size of the microparticles formed and the porous nature of the MGDDS, which is essential for drug release. The in vivo drug release analysis results further displayed a more controlled ciprofloxacin release profile over 24 h and a greater bioavailability for the MGDDS when compared to the marketed immediate-release ciprofloxacin product. Overall, the developed system successfully delivered ciprofloxacin in a control-release manner and enhanced its absorption, thereby displaying the potential of the system to be used in the delivery of other NAW drugs.

Computational Modeling on Drugs Effects for Left Ventricle in Cardiomyopathy Disease.

Cardiomyopathy is associated with structural and functional abnormalities of the ventricular myocardium and can be classified in two major groups: hypertrophic (HCM) and dilated (DCM) cardiomyopathy. Computational modeling and drug design approaches can speed up the drug discovery and significantly reduce expenses aiming to improve the treatment of cardiomyopathy. In the SILICOFCM project, a multiscale platform is developed using coupled macro- and microsimulation through finite element (FE) modeling of fluid-structure interactions (FSI) and molecular drug interactions with the cardiac cells. FSI was used for modeling the left ventricle (LV) with a nonlinear material model of the heart wall. Simulations of the drugs' influence on the electro-mechanics LV coupling were separated in two scenarios, defined by the principal action of specific drugs. We examined the effects of Disopyramide and Dygoxin which modulate Ca2+ transients (first scenario), and Mavacamten and 2-deoxy adenosine triphosphate (dATP) which affect changes of kinetic parameters (second scenario). Changes of pressures, displacements, and velocity distributions, as well as pressure-volume (P-V) loops in the LV models of HCM and DCM patients were presented. Additionally, the results obtained from the SILICOFCM Risk Stratification Tool and PAK software for high-risk HCM patients closely followed the clinical observations. This approach can give much more information on risk prediction of cardiac disease to specific patients and better insight into estimated effects of drug therapy, leading to improved patient monitoring and treatment.

CPSF6 assembly dynamics and binding avidity on HIV-1 capsid are regulated by interactions between prion-like low complexity regions.

The cleavage and polyadenylation specificity factor 6 (CPSF6) is a cellular protein that play crucial role in HIV-1 capsid nuclear entry. Higher-ordered oligomerization of CPSF6 forms string-like assemblies templated by the viral capsid lattice, despite low affinity of the CA-binding FG peptide of CPSF6. Structural studies revealed that multivalent CPSF6 assembly is mediated by interactions between prion-like low complexity regions (LCR), which are templated by binding of CPSF6 FG peptides to the CA monomer. In this work, we elucidate the CPSF6 assembly dynamical mechanism, specifically how FG-CA and LCR-LCR interactions work in concert to drive CPSF6 oligomerization on the HIV-1 capsid lattice. We first develop “bottom-up” coarse-grained (CG) model of CPSF6-CA and CPSF6-CPSF6 molecular interactions from atomistic simulation of a tri-CPSF6 complex complexed to 4 CA hexamers. Our simulations demonstrate that weakening the interactions between the low-complexity region of CPSF6 not only abrogates CPSF6 assembly but also weakens the binding avidity of the FG peptide to the CA binding pocket. The assembled CPSF6 oligomers are highly diffusive on the CA lattice exhibiting an ensemble of conformations, typical of unstructured proteins. At the high curvature region of the capsid lattice the string-like assemblies of CPSF6 appears to be disrupted. Our findings elucidate a network of interactions that regulate the CPSF6 assembly templated by the viral capsid lattice.

Molecular docking as a stage in the development of new drugs for veterinary use

The aim of the work is to substantiate the application of the priority use of the "molecular docking" methodology as the development of new drugs for veterinary medicine with the study of the main methodological approaches.Scientific novelty of the publication in the complex of studies of an observed study in the field of promising developments of pharmaceutical substances (including those based on digital transformation), such as the molecular docking method, with a description of the main clinical approaches and observations. The main author's hypothesis of this study is the possibility of the most promising approaches from the point of view of veterinary pharmacology for their stable possible application in industry practice.The information retrieval methodology was based on such general scientific methods of cognition as: a review of specialized search engines and databases of scientific and research data (Scopus, WoS, PubMed) over the past 15 years, analysis of the identified results, and their comparison by relevance.Molecular docking refers to modeling (including computer modeling) of molecular interaction in the context of complementarity and the search for optimal conformations to achieve the desired pharmacological effect.Molecular docking involves finding the maximum mode or mode of binding of a ligand to a target cell. The way it binds to receptors is often used and decides its state variables. They include its position in the cavity, its orientation and its conformation (torsion angles for each rotating). It is revealed that this leads to a description of the degree of freedom in a multidimensional space, their boundaries describe the degree of search.Molecular docking refers to modeling (including computer modeling) of molecular interaction in the context of complementarity and the search for optimal conformations to achieve the desired pharmacological effect.The proposed algorithms for scientific work on the consideration of applied problems of pharmacology and bioinformatics. The analyzed approach is well suited for solving the tasks.