Predicting FFAR4 agonists using structure-based machine learning approach based on molecular fingerprints.

Free Fatty Acid Receptor 4 (FFAR4), a G-protein-coupled receptor, is responsible for triggering intracellular signaling pathways that regulate various physiological processes. FFAR4 agonists are associated with enhancing insulin release and mitigating the atherogenic, obesogenic, pro-carcinogenic, and pro-diabetogenic effects, normally associated with the free fatty acids bound to FFAR4. In this research, molecular structure-based machine-learning techniques were employed to evaluate compounds as potential agonists for FFAR4. Molecular structures were encoded into bit arrays, serving as molecular fingerprints, which were subsequently analyzed using the Bayesian network algorithm to identify patterns for screening the data. The shortlisted hits obtained via machine learning protocols were further validated by Molecular Docking and via ADME and Toxicity predictions. The shortlisted compounds were then subjected to MD Simulations of the membrane-bound FFAR4-ligand complexes for 100 ns each. Molecular analyses, encompassing binding interactions, RMSD, RMSF, RoG, PCA, and FEL, were conducted to scrutinize the protein-ligand complexes at the inter-atomic level. The analyses revealed significant interactions of the shortlisted compounds with the crucial residues of FFAR4 previously documented. FFAR4 as part of the complexes demonstrated consistent RMSDs, ranging from 3.57 to 3.64, with minimal residue fluctuations 5.27 to 6.03 nm, suggesting stable complexes. The gyration values fluctuated between 22.8 to 23.5 nm, indicating structural compactness and orderliness across the studied systems. Additionally, distinct conformational motions were observed in each complex, with energy contours shifting to broader energy basins throughout the simulation, suggesting thermodynamically stable protein-ligand complexes. The two compounds CHEMBL2012662 and CHEMBL64616 are presented as potential FFAR4 agonists, based on these insights and in-depth analyses. Collectively, these findings advance our comprehension of FFAR4's functions and mechanisms, highlighting these compounds as potential FFAR4 agonists worthy of further exploration as innovative treatments for metabolic and immune-related conditions.

Uncovering PPAR-γ agonists: An integrated computational approach driven by machine learning.

Peroxisome proliferator-activated receptor gamma (PPAR-γ) serves as a nuclear receptor with a pivotal function in governing diverse facets of metabolic processes. In diabetes, the prime physiological role of PPAR-γ is to enhance insulin sensitivity and regulate glucose metabolism. Although PPAR-γ agonists such as Thiazolidinediones are effective in addressing diabetes complications, it is vital to be mindful that they are associated with substantial side effects that could potentially give rise to health challenges. The recent surge in the discovery of selective modulators of PPAR-γ inspired us to formulate an integrated computational strategy by leveraging the promising capabilities of both machine learning and in silico drug design approaches. In pursuit of our objectives, the initial stage of our work involved constructing an advanced machine learning classification model, which was trained utilizing chemical information and physicochemical descriptors obtained from known PPAR-γ modulators. The subsequent application of machine learning-based virtual screening, using a library of 31,750 compounds, allowed us to identify 68 compounds having suitable characteristics for further investigation. A total of four compounds were identified and the most favorable configurations were complemented with docking scores ranging from -8.0 to -9.1 kcal/mol. Additionally, the compounds engaged in hydrogen bond interactions with essential conserved residues including His323, Leu330, Phe363, His449 and Tyr473 that describe the ligand binding site. The stability indices investigated herein for instance root-mean-square fluctuations in the backbone atoms indicated higher mobility in the region of orthosteric site in the presence of agonist with the deviation peaks in the range of 0.07-0.69 nm, signifying moderate conformational changes. The deviations at global level revealed that the average values lie in the range of 0.25-0.32 nm. In conclusion, our identified hits particularly, CHEMBL-3185642 and CHEMBL-3554847 presented outstanding results and highlighted the stable conformation within the orthosteric site of PPAR-γ to positively modulate the activity.

Perovskite materials in X-ray detection and imaging: recent progress, challenges, and future prospects.

Perovskite materials have attracted significant attention as innovative and efficient X-ray detectors owing to their unique properties compared to traditional X-ray detectors. Herein, chronologically, we present an in-depth analysis of X-ray detection technologies employing organic-inorganic hybrids (OIHs), all-inorganic and lead-free perovskite material-based single crystals (SCs), thin/thick films and wafers. Particularly, this review systematically scrutinizes the advancement of the diverse synthesis methods, structural modifications, and device architectures exploited to enhance the radiation sensing performance. In addition, a critical analysis of the crucial factors affecting the performance of the devices is also provided. Our findings revealed that the improvement from single crystallization techniques dominated the film and wafer growth techniques. The probable reason for this is that SC-based devices display a lower trap density, higher resistivity, large carrier mobility and lifetime compared to film- and wafer-based devices. Ultimately, devices with SCs showed outstanding sensitivity and the lowest detectable dose rate (LDDR). These results are superior to some traditional X-ray detectors such as amorphous selenium and CZT. In addition, the limited performance of film-based devices is attributed to the defect formation in the bulk film, surfaces, and grain boundaries. However, wafer-based devices showed the worst performance because of the formation of voids, which impede the movement of charge carriers. We also observed that by performing structural modification, various research groups achieved high-performance devices together with stability. Finally, by fusing the findings from diverse research works, we provide a valuable resource for researchers in the field of X-ray detection, imaging and materials science. Ultimately, this review will serve as a roadmap for directing the difficulties associated with perovskite materials in X-ray detection and imaging, proposing insights into the recent status, challenges, and promising directions for future research.

Synthesis of Competitive and Noncompetitive Inhibitors of Alpha-Glucosidase and Anticancer Agents.

Imidazoles and phenylthiazoles are an important class of heterocycles that demonstrate a wide range of biological activities against various types of cancers, diabetes mellitus and pathogenic microorganisms. The heterocyclic structure having oxothiazolidine moiety is an important scaffold present in various drugs, with potential for enzyme inhibition. In an effort to discover new heterocyclic compounds, we synthesized 26 new 4,5-diphenyl-1H-imidazole, phenylthiazole, and oxothiazolidine heterocyclic analogues that demonstrated potent α-glucosidase inhibition and anticancer activities. Majority of the compounds noncompetitively inhibited α-glucosidase except for two that exhibited competitive inhibition of the enzyme. Docking results suggested that the noncompetitive inhibitors bind to an apparent allosteric site on the enzyme located in the vicinity of the active site. Additionally, the analogues also exhibited significant activity against various types of cancers including non-small lung cancer. Since tubulin protein plays an important role in the pathogenesis of non-small lung cancer, molecular docking with one of the target compounds provided important clues to its binding mode. The current work on imidazoles and phenylthiazole derivatives bears importance for designing of new antidiabetic and anticancer drugs.

Taming the cytokine storm: small molecule inhibitors targeting IL-6/IL-6α receptor.

Given the increasing effectiveness of immune-based therapies, management of their associated toxicities is of utmost importance. Cytokine release syndrome (CRS), characterized by elevated levels of cytokine, poses a significant challenge following the administration of antibodies and CAR-T cell therapies. CRS also contributes to multiple organ dysfunction in severe viral infections, notably in COVID-19. Given the pivotal role of IL-6 cytokine in initiating CRS, it has been considered a most potential therapeutic target to mitigate hyperactivated immune responses. While monoclonal antibodies of IL-6 show promise in mitigating cytokine storm, concerns about immunotoxicity persist, and small molecule IL-6 antagonists remain unavailable. The present study employed sophisticated computational techniques to identify potential hit compounds as IL-6 inhibitors, with the aim of inhibiting IL-6/IL-6R protein-protein interactions. Through ligand-based pharmacophore mapping and shape similarity in combination with docking-based screening, we identified nine hit compounds with diverse chemical scaffolds as potential binders of IL-6. Further, the MD simulation of 300 ns of five virtual hits in a complex with IL-6 was employed to study the dynamic behavior. To provide a more precise prediction, binding free energy was also estimated. The identified compounds persistently interacted with the residues lining the binding site of the IL-6 protein. These compounds displayed low binding energy during MMPBSA calculations, substantiating their strong association with IL-6. This study suggests promising scaffolds as potential inhibitors of IL-6/IL-6R protein-protein interactions and provides direction for lead optimization.

A deep learning-based theoretical protocol to identify potentially isoform-selective PI3Kα inhibitors.

Phosphoinositide 3-kinase alpha (PI3Kα) is one of the most frequently dysregulated kinases known for their pivotal role in many oncogenic diseases. While the side effects linked to existing drugs against PI3Kα-induced cancers provide an avenue for further research, the significant structural conservation among PI3Ks makes it extremely difficult to develop new isoform-selective PI3Kα inhibitors. Embracing this challenge, we herein designed a hybrid protocol by integrating machine learning (ML) with in silico drug-designing strategies. A deep learning classification model was developed and trained on the physicochemical descriptors data of known PI3Kα inhibitors and used as a screening filter for a database of small molecules. This approach led us to the prediction of 662 compounds showcasing appropriate features to be considered as PI3Kα inhibitors. Subsequently, a multiphase molecular docking was applied to further characterize the predicted hits in terms of their binding affinities and binding modes in the targeted cavity of the PI3Kα. As a result, a total of 12 compounds were identified whereas the best poses highlighted the efficiency of these ligands in maintaining interactions with the crucial residues of the protein to be targeted for the inhibition of associated activity. Notably, potential activity of compound 12 in counteracting PI3Kα function was found in a previous in vitro study. Following the drug-likeness and pharmacokinetic characterizations, six compounds (compounds 1, 2, 3, 6, 7, and 11) with suitable ADME-T profiles and promising bioavailability were selected. The mechanistic studies in dynamic mode further endorsed the potential of identified hits in blocking the ATP-binding site of the receptor with higher binding affinities than the native inhibitor, alpelisib (BYL-719), particularly the compounds 1, 2, and 11. These outcomes support the reliability of the developed classification model and the devised computational strategy for identifying new isoform-selective drug candidates for PI3Kα inhibition.

Identification of novel small molecule inhibitors for solute carrier SGLT1; a computational exploration.

Diabetes results in substantial disabilities, diminished quality of life, and mortality that imposes a huge economic burden on societies and governments worldwide. Despite the absence of specific oral therapies at present, there exists an urgent requirement to develop a novel drug for the treatment of diabetes mellitus. The membrane protein sodium glucose co-transporters (SGLT1) present a captivating therapeutic target for diabetes, given its pivotal role in facilitating glucose absorption in the small intestine, offering immense promise for potential therapeutic intervention. In this connection, the present study is aimed at identifying potential inhibitors of SGLT1 from a small molecule database, including compounds from both natural as well as synthetic origins. A comprehensive approach was employed, by integrating homology modeling, ligand-based pharmacophore modeling, virtual screening, and molecular docking simulation. The process resulted in the identification of 16 new compounds, featuring similar attributes as observed for the documented actives. In a systematic screening procedure, five potential virtual hits were selected for simulation studies followed by subsequent binding free energy calculations, providing deeper insight into the time-dependent behavior of protein-ligand complexes in a dynamic state. In conclusion, our findings demonstrated that the identified compounds, particularly compounds 81 and 91, exhibit enhanced stability and favorable binding affinities with the target protein, marking them promising candidates for further investigations.Communicated by Ramaswamy H. Sarma.

Physical-Vapor-Deposited Metal Oxide Thin Films for pH Sensing Applications: Last Decade of Research Progress.

In the last several decades, metal oxide thin films have attracted significant attention for the development of various existing and emerging technological applications, including pH sensors. The mandate for consistent and precise pH sensing techniques has been increasing across various fields, including environmental monitoring, biotechnology, food and agricultural industries, and medical diagnostics. Metal oxide thin films grown using physical vapor deposition (PVD) with precise control over film thickness, composition, and morphology are beneficial for pH sensing applications such as enhancing pH sensitivity and stability, quicker response, repeatability, and compatibility with miniaturization. Various PVD techniques, including sputtering, evaporation, and ion beam deposition, used to fabricate thin films for tailoring materials' properties for the advanced design and development of high-performing pH sensors, have been explored worldwide by many research groups. In addition, various thin film materials have also been investigated, including metal oxides, nitrides, and nanostructured films, to make very robust pH sensing electrodes with higher pH sensing performance. The development of novel materials and structures has enabled higher sensitivity, improved selectivity, and enhanced durability in harsh pH environments. The last decade has witnessed significant advancements in PVD thin films for pH sensing applications. The combination of precise film deposition techniques, novel materials, and surface functionalization strategies has led to improved pH sensing performance, making PVD thin films a promising choice for future pH sensing technologies.

Identification of potent anti-immunogenic agents through virtual screening, 3D-QSAR studies, and in vitro experiments.

A wealth of literature has highlighted the discovery of various immune modulators, frequently used in clinical practice, yet associated with numerous drawbacks. In light of this pharmacological deficiency, medical scientists are motivated to develop new immune modulators with minimized adverse effects yet retaining the improved therapeutic potential. T-cell differentiation and growth are central to human defense and are regulated by interleukin-2 (IL-2), an immune-modulatory cytokine. However, scientific investigation is hindered due to its flat binding site and widespread hotspot residues. In this regard, a prompt and logical investigation guided by integrated computational techniques was undertaken to unravel new and potential leads against IL-2. In particular, the combination of score-based and pharmacophore-based virtual screening approaches were employed, reducing the data from millions of small molecules to a manageable number. Subsequent docking and 3D-QSAR prediction via CoMFA further helped remove false positives from the data. The reliability of the model was assessed via standard metrics, which explain the model's fitness and the robustness of the model in predicting the activity of new compounds. The extensive virtual screening herein led to the identification of a total of 24 leads with potential anti-IL-2 activity. Furthermore, the theoretical findings were corroborated with in vitro testing, further endorsing the anti-inflammatory potential of the identified leads.

Compounds Related to Saudin and Three New Series of Diterpenoids from Clutia lanceolata.

Clutia lanceolata is a medicinal plant native to Ethiopia and sub-Saharan Africa and to the Arabian Peninsula. It is used traditionally in Saudi Arabia for the treatment of diabetes. Previous phytochemical analysis of this species has been limited to the identification of methylthiocoumarins. Further work has led to isolation of 19 new diterpenoids in three structural classes. Their structures were established by HRMS and by a range of NMR techniques (1H, 13C, COSY, NOESY, HSQC, HMBC), with confirmation for some examples by X-ray crystallography. NOESY and 1H-1H NMR coupling constants gave the relative stereochemical configurations and conformational information, with absolute configurations being established through X-ray crystallography. One example closely related to the known hypoglycemic compound saudin (found in C. richardiana and also in C. lanceolata) and one with a different core tetracycle were found to enhance strongly the glucose-triggered release of insulin from murine pancreatic islets. Biosynthetic proposals for the three groups of new diterpenoids by alternative cyclization of a common precursor are put forward. Lanceolide P (16) is proposed as a lead compound for further development for the treatment of diabetes.

Structural basis for the mutation-induced dysfunction of the human IL-15/IL-15α receptor complex.

In silico strategies offer a reliable, fast, and inexpensive, way compared to the clumsy in vitro approaches to boost understanding of the effect of amino acid substitution on the structure and consequently the associated function of proteins. In the present work, we report an atomistic-based, reliable in silico structural and energetic framework of the interactions between the receptor-binding domain of the Interleukin-15 (IL-15) protein and its receptor Interleukin-15α (IL-15α), consequently, providing qualitative and quantitative details of the key molecular determinants in ligand/receptor recognition. Molecular dynamics simulations were used to investigate the dynamic behavior of the specific binding between IL-15 and IL-15α followed by estimation of the free energies via molecular mechanics/generalized Born surface area (MM/GBSA). In particular, residues Y26, E46, E53, and E89 of the IL-15 protein receptor-binding domain are identified as main hot spots, shaping and governing the stability of the assembly. These results can be used for the development of neutralizing antibodies and the effective structure-based design of protein-protein interaction inhibitors against the so-called orphan disease, vitiligo.

Insilico structure based drug design approach to find potential hits in ventilator-associated pneumonia caused by Pseudomonas aeruginosa.

Tetraacyldisaccharide 4'-kinase (LpxK) is the prime enzyme responsible for the biosynthesis of lipid A. LpxK is a key antibacterial drug target, but it is less exploitation in Pseudomonas aeruginosa and other bacterial species limits its therapeutic use. Pseudomonas aeruginosa is responsible for severe infections like pneumonia and urinary tract infections. The precautionary measures of Pseudomonas aeruginosa infections are decisive as it results in extensive drug resistance, systemic bacteremia, and ventilator-associated pneumonia. The current rational approach highlights exploiting the use of computer-aided drug design approaches to counter Pseudomonas aeruginosa specific LpxK. The various approaches used were exploring the metabolic pathway database (Metacyc), drug target validation using DEG, protein modeling, ligand docking, e-pharmacophore assisted virtual screening, physicochemical and Toxicity profile prediction studies, and molecular simulations in spotting out novel potential hits compounds. The virtual hits which have highly ranked in the study were STOCK4S-16119, STOCK1S -60869, STOCK6S -43621, STOCK6S -3328, and STOCKS-39892 which can act as a scaffold for the establishment of new hits against LpxK and can result in control of Pseudomonas aeruginosa infectivity.

The anti-oxidative, anti-cell proliferative and anti-microbial efficacies of cold-adapted Crepis flexuosa: HPTLC and GC/MS analyses.

The genus Crepis constitutes cold-adapted plant spp., of these some are traditionally used in folk medicine against inflammation or fungal infections without scientific validations. Here, we report the biological activities of Crepis flexuosa total ethanol-extract (CF-EtOH) and its hexane (CF-Hex), ethyl acetate (CF-EtOA), butanol (CF-ButOH), and aqueous (CF-Aqua) fractions. Our in vitro DPPH and ABTS radical-scavenging assays showed CF-EtOH, CF-ButOH and CF-Aqua with maximal, CF-EtOA with moderate, and CF-Hex with mild anti-oxidant activities. When tested on human cancer cell lines, high cytotoxicity was demonstrated by CF-EtOH (IC50: 42.45 µg/ml) and CF-Aqua (IC50: 46.37 µg/ml) on HepG2, followed by CF-Hex (IC50: 63.24 µg/ml) and CF-ButOH (IC50: 65.32 µg/ml) on MCF7 cells. The human primary cell line (HUVEC) had comparatively lower cytotoxicity for the tested samples. Moreover, when assessed for anti-microbial efficacy, CF-ButOH and CF-Aqua exhibited the strongest activity (MIC: 156.25 µg/ml) against S. aureus, E. faecalis and C. albicans. Further, while the developed RP-HPTLC identified the bioactive flavonoid luteolin-7-O-glucoside (17.58 mg/g), GS/MS analysis revealed sixteen compounds in C. flexuosa extract. In conclusion, we for the first time show the promising anti-oxidative, anti-cell proliferative and anti-microbial efficacies of C. flexuosa. This warrants further phytochemical and bio-efficacy studies towards isolations and identifications of active principles.

Structure-Based Discovery of Potent Staphylococcus aureus Thymidylate Kinase Inhibitors by Virtual Screening.

INTRODUCTION: Multidrug-resistant bacteria are rapidly increasing worldwide, increasing antibiotic resistance. The exploitation, misuse, overuse, and decrease of the therapeutic potential of currently available antibiotics have resulted in the development of resistance against bacteria. As the most common bacterial pathogen in humans, Staphylococcus aureus can cause many adverse health effects. In fighting multidrug-resistant Staphylococcus aureus, scientists have identified an extremely relevant target - SaTMPK. SaTMPK is essential for DNA synthesis, which, in turn, is necessary for the replication and cell division of bacteria. OBJECTIVE: To perform multi-stage screening using the ZINC database, followed by molecular docking, ADMET profiling, molecular dynamics simulations, and energy calculations. METHODS: Based on the similar pharmacophoric characteristics of existing SaTMPK crystal structures, a model of interaction-based pharmacophores was developed. We then performed molecular docking studies on the positive hits obtained from the pharmacophore screening. Compounds that exhibited good molecular interactions within the SaTMPK binding sites were further evaluated using in-silico ADMET profiling. RESULTS: In a multi-stage screening campaign, three compounds were shortlisted that exhibited physicochemical characteristics suitable for human administration. CONCLUSION: The findings from this study should contribute to in vitro and in vivo studies for clinical applications.

Structure-Based Virtual Screening to Identify Negative Allosteric Modulators of NMDA.

BACKGROUND: NMDA (N-methyl-D-aspartate) receptor is one of the ionotropic receptor subtypes of glutamate, the most abundant excitatory neurotransmitter in the human brain. Besides physiological roles in learning and memory, neuronal plasticity and somatosensory function NMDAR overstimulation are also implicated in a pathophysiological mechanism of 'excitotoxicity.' In this study, an allosteric site has been focused on to design inhibitors of the most abundant form of this receptor of utility in many acute (stroke, traumatic brain injury) and chronic neurodegenerative diseases such as Parkinson's disease, Huntington's, Alzheimer's, and others. METHODS: In order to target this specific site at the interdimer interface of the ligand-binding domain of GluN2A-containing NMDA-Rs, blood-brain barrier-permeable potentially therapeutic compounds, as opposed to only pharmacological tools currently available, were sought. Pharmacophorebased virtual screening, docking, computational ADME prediction techniques, and MD simulation studies were used. RESULTS: Proceeding through the in-silico methodology, the study was successful at reaching 5 compounds from ChEMBL Database, which were predicted to be potential NMDA inhibitor drugs. CONCLUSION: The products of the study are compounds that have been validated through pharmacophore and score-based screening and MD simulation techniques to be allosterically inhibiting NMDA receptors and with favorable pharmacokinetic profiles. They are likely to be therapeutic agents ready for in-vitro and in-vivo testing.

Virtual Screening, Synthesis and Biological Evaluation of Streptococcus mutans Mediated Biofilm Inhibitors.

Dental caries, a global oral health concern, is a biofilm-mediated disease. Streptococcus mutans, the most prevalent oral microbiota, produces extracellular enzymes, including glycosyltransferases responsible for sucrose polymerization. In bacterial communities, the biofilm matrix confers resistance to host immune responses and antibiotics. Thus, in cases of chronic dental caries, inhibiting bacterial biofilm assembly should prevent demineralization of tooth enamel, thereby preventing tooth decay. A high throughput screening was performed in the present study to identify small molecule inhibitors of S. mutans glycosyltransferases. Multiple pharmacophore models were developed, validated with multiple datasets, and used for virtual screening against large chemical databases. Over 3000 drug-like hits were obtained that were analyzed to explore their binding mode. Finally, six compounds that showed good binding affinities were further analyzed for ADME (absorption, distribution, metabolism, and excretion) properties. The obtained in silico hits were evaluated for in vitro biofilm formation. The compounds displayed excellent antibiofilm activities with minimum inhibitory concentration (MIC) values of 15.26-250 µg/mL.

Probing the mechanism of peptide binding to REV response element RNA of HIV-1; MD simulations and free energy calculations.

Ribonucleic acid (RNA) of HIV-1 contains a 350 nucleotide, highly structured, cis-acting element called RRE (REV-response-element RNA), essential for virus replication. REV is a natural peptide that binds to RRE and transports it from the nucleus to cytoplasm where it is expressed into a new virus. The synthetic peptide known as RSG-1.2 also binds the RRE element and competes with REV. The purpose of study is to rationally design novel peptides such as RSG peptide with improved binding affinity to prevent the transport of HIV-1 RNA and so replication of virus. Herein, we performed MD simulation and free energy calculations to evaluate the interactions and binding free energies of REV (PDB ID: 4PMI) and RSGs peptides (PDB IDs: 1G70 and 1I9F) with RRE. The protein-RNA interactions were analyzed using the MM-PBSA method. Results suggest that REV has more binding free energy -188.41 kcal/mol than two RSG peptides with total binding free energy -120.97 and -141.46 kcal/mol. The ARG and ASN were found to be important residues of REV. In the RRE sequence, the nucleotides 62-67 and 78-84 were found to be important contributors in binding free energy. This study play a major role in elaboration of binding REV and RSG1-2 with RRE element and pave the way for further synthesis of peptide that can bind with RRE element and can be selected as therapeutic agent for HIV.Communicated by Ramaswamy H. Sarma.

Mechanistic insights into the inhibitory activity of FDA approved ivermectin against SARS-CoV-2: old drug with new implications.

The novel corona virus (Covid-19) has become a great challenge worldwide since 2019, as no drug has been reported yet. Different clinical trials are still under way. Among them is Ivermectin (IVM), an FDA approved drug which was recently reported as a successful candidate to reduce SARS-CoV-2 viral load by inhibiting Importin-α1 (IMP-α1) protein which subsequently affects nuclear transport of viral proteins but its basic binding mode and inhibitory mechanism is unknown. Therefore, we aimed to explore the inhibitory mechanism and binding mode of IVM with IMP-α1 via different computational methods. Initially, comparative docking of IVM was performed against two different binding sites (Nuclear Localization Signal (NLS) major and minor sites) of IMP-α1 to predict the probable binding mode of IVM. Then, classical MD simulation was performed (IVM/NLS-Major site and IVM/NLS-Minor site), to predict its comparative stability dynamics and probable inhibitory mechanism. The stability dynamics and biophysical analysis of both sites highlighted the stable binding of IVM within NLS-Minor site by establishing and maintaining more hydrophobic contacts with crucial residues, required for IMP-α1 inhibition which were not observed in NLS-major site. Altogether, these results recommended the worth of IVM as a possible drug to limit the SARS-CoV-2 viral load and consequently reduces its progression.Communicated by Ramaswamy H. Sarma.

A neoclerodane orthoester and other new neoclerodane diterpenoids from Teucrium yemense chemistry and effect on secretion of insulin.

Teucrium yemense, a medicinal plant commonly grown in Saudi Arabia and Yemen, is traditionally used to treat infections, kidney diseases, rheumatism, and diabetes. Extraction of the dried aerial parts of the plant with methanol, followed by further extraction with butanol and chromatography, gave twenty novel neoclerodanes. Their structures, relative configurations and some conformations were determined by MS and 1-D and 2-D NMR techniques. Most were fairly conventional but one contained an unusual stable orthoester, one had its (C-16)-(C-13)-(C-14)-(C-15) (tetrahydro)furan unit present as a succinic anhydride and one had a rearranged carbon skeleton resulting from ring-contraction to give a central octahydroindene bicyclic core, rather than the usual decalin. Mechanisms are proposed for the biosynthetic formation of the orthoester and for the ring-contraction. Four novel neoclerodanes increased the glucose-triggered release of insulin from isolated murine pancreatic islets by more than the standard drug tolbutamide, showing that they are potential leads for the development of new anti-diabetic drugs.