In silico evidence of beauvericin antiviral activity against SARS-CoV-2

Cimicifugoside H-2 as an Inhibitor of IKK1/Alpha: A Molecular Docking and Dynamic Simulation Study

One of the most challenging issues scientists face is finding a suitable non-invasive treatment for cancer, as it is widespread around the world. The efficacy of phytochemicals that target oncogenic pathways appears to be quite promising and has gained attention over the past few years. We investigated the effect of docking phytochemicals isolated from the rhizomes of the Cimicifuga foetida plant on different domains of the IκB kinase alpha (IKK1/alpha) protein. The Cimicifugoside H-2 phytochemical registered a high docking score on the activation loop of IKK1/alpha amongst the other phytochemicals compared to the positive control. The interaction of the protein with Cimicifugoside H-2 was mostly stabilized by hydrogen bonds and hydrophobic interactions. A dynamic simulation was then performed with the Cimicifugoside H-2 phytochemical on the activation loop of IKK1/alpha, revealing that Cimicifugoside H-2 is a possible inhibitor of this protein. The pharmacokinetic properties of the drug were also examined to assess the safety of administering the drug. Therefore, in this in silico study, we discovered that the Cimicifugoside H-2 phytochemical inhibits the actively mutated conformation of IKK1/alpha, potentially suppressing the nuclear factor kappa light chain enhancer of activated B cells (NF-κB) pathway.

Entomopathogenic fungi in crops protection with an emphasis on bioactive metabolites and biological activities

Plant pathogens with their abundance are harmful and cause huge damage to different agricultural crops and economy of a country as well as lead towards the shortage of food for humans. For their management, the utilization of entomopathogenic fungi is an eco-friendly technique, sustainable to the environment, safe for humans and has promising effect over chemical-based pesticides. This process requires a biochemical mechanism, including the production of enzymes, toxins, and other metabolites that facilitate host infection and invasion. Essential enzymes such as chitinase, proteinase, and lipase play a direct role in breaking down the host cuticle, the primary barrier to EPF (Entomopathogenic Fungi) infection. Additionally, secondary metabolites such as destruxins in Metarhizium, beauvericin in Beauveria, hirsutellides in Hirsutella, isarolides in Isaria, cordyols in Cordyceps, and vertihemipterins in Verticillium, among others, act both directly and indirectly to disable the defense mechanisms of insect hosts, thereby accelerating the EPF infection process. The chemical composition of these secondary metabolites varies, ranging from simple non-peptide pigments such as oosporine to highly complex piperazine derivatives such as vertihemiptellides. The biocontrol efficacy of EPF is extensively studied, with numerous fungal strains commercially available on a large scale for managing arthropod pests. This review emphasizes the role of proteins and enzymes against crop pathogens, detailing their mode of action, and describing the metabolites from entomopathogenic fungi and their biological activities. In doing so, these findings contribute to establishing a symbiotic equilibrium between agricultural productivity and environmental conservation.

ABC transporter inhibition by beauvericin partially overcomes drug resistance in Leishmania tropica

Leishmaniasis is a neglected tropical disease infecting the world's poorest populations. Miltefosine (ML) remains the primary oral drug against the cutaneous form of leishmaniasis. The ATP-binding cassette (ABC) transporters are key players in the xenobiotic efflux, and their inhibition could enhance the therapeutic index. In this study, the ability of beauvericin (BEA) to overcome ABC transporter-mediated resistance of Leishmania tropica to ML was assessed. In addition, the transcription profile of genes involved in resistance acquisition to ML was inspected. Finally, we explored the efflux mechanism of the drug and inhibitor. The efficacy of ML against all developmental stages of L. tropica in the presence or absence of BEA was evaluated using an absolute quantification assay. The expression of resistance genes was evaluated, comparing susceptible and resistant strains. Finally, the mechanisms governing the interaction between the ABC transporter and its ligands were elucidated using molecular docking and dynamic simulation. Relative quantification showed that the expression of the ABCG sub-family is mostly modulated by ML. In this study, we used BEA to impede resistance of Leishmania tropica. The IC50 values, following BEA treatment, were significantly reduced from 30.83, 48.17, and 16.83 µM using ML to 8.14, 11.1, and 7.18 µM when using a combinatorial treatment (ML + BEA) against promastigotes, axenic amastigotes, and intracellular amastigotes, respectively. We also demonstrated a favorable BEA-binding enthalpy to L. tropica ABC transporter compared to ML. Our study revealed that BEA partially reverses the resistance development of L. tropica to ML by blocking the alternate ATP hydrolysis cycle.

Computational Applications: Beauvericin from a Mycotoxin into a Humanized Drug

Drug discovery was initially attributed to coincidence or experimental research. Historically, the traditional approaches were complex, lengthy, and expensive, entailing costly random screening of synthesized compounds or natural products coupled with in vivo validation largely depending on the availability of appropriate animal models. Currently, in silico modeling has become a vital tool for drug discovery and repurposing. Molecular docking and dynamic simulations are being used to find the best match between a ligand and a molecule, an approach that could help predict the biomolecular interactions between the drug and the target host. Beauvericin (BEA) is an emerging mycotoxin produced by the entomopathogenic fungus Beauveria bassiana, being originally studied for its potential use as a pesticide. BEA is now considered a molecule of interest for its possible use in diverse biotechnological applications in the pharmaceutical industry and medicine. In this manuscript, we provide an overview of the repurposing of BEA as a potential therapeutic agent for multiple diseases. Furthermore, considerable emphasis is given to the fundamental role of in silico techniques to (i) further investigate the activity spectrum of BEA, a secondary metabolite, and (ii) elucidate its mode of action.

In silico characterization of the novel SDR42E1 as a potential vitamin D modulator

The short-chain dehydrogenase/reductase (SDR) superfamily encompasses enzymes that play essential roles in the metabolism of steroid hormones and lipids. Despite an enigmatic function, recent genetic studies have linked the novel SDR 42 extended-1 (SDR42E1) gene to 25-hydroxyvitamin D levels. This study investigated the potential SDR42E1 functions and interactions with vitamin D using bioinformatics and molecular docking studies. Phylogenetic analysis unveiled that the nucleotide sequences of human SDR42E1 exhibit high evolutionary conservation across nematodes and fruit flies. Molecular docking analysis identified strong binding affinities between SDR42E1 and its orthologs with vitamin D3 and essential precursors, 8-dehydrocholesterol, followed by 7-dehydrocholesterol and 25-hydroxyvitamin D. The hydrophobic interactions observed between the protein residues and vitamin D compounds supported the predicted transmembrane localization of SDR42E1. Our investigation provides valuable insights into the potential role of SDR42E1 in skin vitamin D biosynthesis throughout species. This provides the foundation for future research and development of targeted therapies for vitamin D deficiency and related health conditions.

Evaluation of computed tomography images under deep learning in the diagnosis of severe pulmonary infection

This work aimed to explore the diagnostic value of a deep convolutional neural network (CNN) combined with computed tomography (CT) images in patients with severe pneumonia complicated with pulmonary infection. A total of 120 patients with severe pneumonia complicated by pulmonary infection admitted to the hospital were selected as research subjects and underwent CT imaging scans. The empty convolution (EC) and U-net phase were combined to construct an EC-U-net, which was applied to process the CT images. The results showed that the learning rate of the EC-U-net model decreased substantially with increasing training times until it stabilized and reached zero after 40 training times. The segmentation result of the EC-U-net model for the CT image was very similar to that of the mask image, except for some deviations in edge segmentation. The EC-U-net model exhibited a significantly smaller cross-entropy loss function (CELF) and a higher Dice coefficient than the CNN algorithm. The diagnostic accuracy of CT images based on the EC-U-net model for severe pneumonia complicated with pulmonary infection was substantially higher than that of CT images alone, while the false negative rate (FNR) and false positive rate (FPR) were substantially lower (P < 0.05). Moreover, the true positive rates (TPRs) of CT images based on the EC-U-net model for patchy high-density shadows, diffuse ground glass density shadows, pleural effusion, and lung consolidation were obviously higher than those of the original CT images (P < 0.05). In short, the EC-U-net model was superior to the traditional algorithm regarding the overall performance of CT image segmentation, which can be clinically applied. CT images based on the EC-U-net model can clearly display pulmonary infection lesions, improve the clinical diagnosis of severe pneumonia complicated with pulmonary infection, and help to screen early pulmonary infection and carry out symptomatic treatment.

Identifying health research in the era of COVID-19: A scoping review

Background

Health improvements are considered one of the most important fields of research. Since the coronavirus disease 2019 was declared a pandemic, it might have impacted clinical and public health research in various forms.

Objectives

The goal of this study is to explore health research approaches in the era of coronavirus disease 2019.

Methods

In this scoping review, we reviewed published medical full-text studies and identified potential areas of interest of health research in the era the coronavirus disease 2019 pandemic during the last 3 years within a higher educational setting. A bibliometric analysis was used to compare among published works.

Results

Among the 93 studies that met the inclusion criteria, most focused on mental health (n = 23; 24.7%). Twenty-one publications targeted coronavirus disease 2019 and its consequences on general health. Other studies have described hemato-oncological, cardiovascular, respiratory, and endocrinological diseases. 42 studies were cross-sectional or cohort studies and most of them published in Q1 journals. Almost half of them belonged to the Faculty of Medicine (49.5%) followed by the School of Arts, Sciences, and Psychology (26.9%).

Conclusions

Health research has been demonstrated to be important, at all times, especially during crises. Therefore, researchers need to invest more efforts into seeking new medical updates in various health-related fields, regardless of their correlation with coronavirus disease 2019.

Prediction and validation of monoclonal antibodies separation in aqueous two-phase system using molecular dynamic simulation

In order to predict how mAbs partition in 20% ethylene oxide/80% propylene oxide (v/v) random copolymer (EO₂₀PO₈₀)/water aqueous two-phase system (ATPS), a molecular dynamic simulation model was developed using Gromacs and then validated by experiments. The ATPS was applied with seven kinds of salt, including buffer salt and strong dissociation salt that were commonly employed in the purification of protein. Na₂SO₄ was shown to have the best effects on lowering EO₂₀PO₈₀ content in the aqueous phase and enhancing recovery. The content of EO₂₀PO₈₀ in the sample solution was decreased to 0.62%±0.25% and the recovery of rituximab increased to 97.88%±0.95% by adding 300 mM Na₂SO₄ into back extraction ATPS. The viability determined by ELISA was 95.57% at the same time. A strategy for constructing a prediction model for the distribution of mAbs in ATPS was proposed in consideration of this finding. Partition of trastuzumab in ATPS was predicted by the model created using this method and the prediction result was further validated by experiments. The recovery of trastuzumab reached 95.63%±2.86% under the ideal extraction conditions suggested by the prediction model.

Interaction of the new inhibitor paxlovid (PF-07321332) and ivermectin with the monomer of the main protease SARS-CoV-2: A volumetric study based on molecular dynamics, elastic networks, classical thermodynamics and SPT

The COVID-19 pandemic has accelerated the study of drugs, most notably ivermectin and more recently Paxlovid (PF-07321332) which is in phase III clinical trials with experimental data showing covalent binding to the viral protease Mpro. Theoretical developments of catalytic site-directed docking support thermodynamically feasible non-covalent binding to Mpro. Here we show that Paxlovid binds non-covalently at regions other than the catalytic sites with energies stronger than reported and at the same binding site as the ivermectin B1a homologue, all through theoretical methodologies, including blind docking. We volumetrically characterize the non-covalent interaction of the ivermectin homologues (avermectins B1a and B1b) and Paxlovid with the mMpro monomer, through molecular dynamics and scaled particle theory (SPT). Using the fluctuation-dissipation theorem (FDT), we estimated the electric dipole moment fluctuations at the surface of each of complex involved in this study, with similar trends to that observed in the interaction volume. Using fluctuations of the intrinsic volume and the number of flexible fragments of proteins using anisotropic and Gaussian elastic networks (ANM+GNM) suggests the complexes with ivermectin are more dynamic and flexible than the unbound monomer. In contrast, the binding of Paxlovid to mMpro shows that the mMpro-PF complex is the least structurally dynamic of all the species measured in this investigation. The results support a differential molecular mechanism of the ivermectin and PF homologues in the mMpro monomer. Finally, the results showed that Paxlovid despite beingbound in different sites through covalent or non-covalent forms behaves similarly in terms of its structural flexibility and volumetric behaviour.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.