Computational Virtual Screening Research Articles

Renal toxicity of Aconitum plants? A study based on a new mass spectrometry scanning strategy and computer virtual screening.

Radix Aconiti Lateralis (Fuzi), a mono-herbal preparation of Aconitum herbs in the genus Aconitum, is commonly used in traditional Chinese medicine (TCM) to treat critical illnesses. The curative effect of Fuzi is remarkable. However, the toxic effects of Fuzi are still a key clinical focus, and the substances inducing nephrotoxicity are still unclear. Therefore, this study proposes a research model combining "in vitro and in vivo component mining-virtual multi-target screening-active component prediction-literature verification" to screen potential nephrotoxic substances rapidly. The UHPLC-Q-Exactive-Orbitrap MS analysis method was used for the correlation analysis of Fuzi's in vitro-in vivo chemical substance groups. On this basis, the key targets of nephrotoxicity were screened by combining online disease databases and a protein-protein interaction (PPI) network. The computer screening technique was used to verify the binding mode and affinity of Fuzi's components with nephrotoxic targets. Finally, the potential material basis of Fuzi-induced nephrotoxicity was screened. Eighty-one Fuzi components were identified. Among them, 35 components were absorbed into the blood. Based on the network biology method, 21 important chemical components and three potential key targets were screened. Computer virtual screening revealed that mesaconine, benzoylaconine, aconitine, deoxyaconitine, hypaconitine, benzoylhypaconine, benzoylmesaconine, and hypaconitine may be potential nephrotoxic substances of Fuzi. Fuzi may interact with multiple components and targets in the process of inducing nephrotoxicity. In the future, experiments can be designed to explore further. This study provides a reference for screening Fuzi nephrotoxic components and has certain significance for the safe use of Fuzi.

Using network pharmacology to discover potential drugs for hypertrophic scars.

Hypertrophic scar is a disease of abnormal skin fibrosis caused by excessive fibroblast proliferation, and existing drugs cannot achieve satisfactory therapeutic effects. This study aimed to explore the molecular pathogenesis of hypertrophic scars and screen effective drugs for hypertrophic scars. Existing human hypertrophic scar RNA sequencing data were utilized to search for hypertrophic scar-related gene modules and key genes through weighted gene co-expression network analysis (WGCNA). Candidate compounds were screened in a compound library. Potential drugs were screened by molecular docking and verified in human hypertrophic scar fibroblasts and a mouse mechanical force hypertrophic scar model. WGCNA showed that hypertrophic scar-associated gene modules influence focal adhesion, transforming growth factor β (TGF-β) signaling pathway, and other biological pathways. Integrin β1 (ITGB1) is the hub protein. Among the candidate compounds obtained by computer virtual screening and molecular docking, crizotinib, sorafenib, and SU11274 can inhibit the proliferation and migration of human hypertrophic scar fibroblasts and pro-fibrotic gene expression. Crizotinib had the best effect on hypertrophic scar attenuation in mouse models. At the same time, mouse ITGB1 small interfering RNA (siRNA) can also inhibit mouse scar hyperplasia. ITGB1 and TGF-β signaling pathways are important for hypertrophic scar formation. Crizotinib could serve as a potential drug for hypertrophic scars.

A close-up shot of protein-protein docking, from experiment to theory and reverse with the PROTAC performers

PROTACs (Proteolysis Targeting Chimeras), heterobifunctional molecules, exhibit selectivity in degrading target proteins through E3 ubiquitin ligases. Designing effective PROTACs requires a deep understanding of the intricate binding interactions in the ternary complex (POI/PROTAC/E3 ligase), crucial for efficient target protein degradation. To address this challenge, we introduce a novel computational virtual screening method that considers essential amino acid interactions between the protein of interest and the chosen E3 ligase. This approach enhances accuracy and reliability, facilitating the strategic development of potent PROTACs. Utilizing a crystallized model of the VHL:PROTAC:SMARCA2BD ternary complex (PDB: 7Z6L), we assessed the effectiveness of our method. Our study reveals that increasing the number of essential restraints between the two proteins reduces the generated docking poses, leading to closer alignment with the experimental ternary complex. Specifically, utilizing three restraints showed the closest resemblance to the published complex, highlighting crucial interactions such as an H-bond between A:Gln 89 and B:Asn 67, along with two hydrophobic interactions: A:Gly 22 with B:Arg 69 and A:Glu 37 with B:Pro 99. This resulted in a significant decrease in the mean RMSD value from 31.8 and 31.0 Å to 24.4 Å, respectively. This underscores the importance of incorporating multiple essential restraints to enhance docking accuracy. Building on this progress, we introduce a systematic approach to design potential PROTACs between the Estrogen receptor and the E3 ligase, utilizing bridging intermediates with 4, 6, or 7 carbon atoms. By providing a more accurate and efficient means of identifying optimal PROTAC candidates, this approach has the potential to accelerate the development of targeted therapies and reduce the time and costs associated with drug discovery. Communicated by Ramaswamy H. Sarma

In Silico Identification of Inhibitory Compounds for SARS-Cov-2 Papain-Like Protease.

Virtual screening with high-performance computers is a powerful and cost-effective technique in drug discovery. A chemical database is searched to find candidate compounds firmly bound to a target protein, judging from the binding poses and/or binding scores. The severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) infectious disease has spread worldwide for the last three years, causing severe slumps in economic and social activities. SARS-Cov-2 has two viral proteases: 3-chymotrypsin-like (3CL) and papain-like (PL) protease. While approved drugs have already been released for the 3CL protease, no approved agent is available for PL protease. In this work, we carried out in silico screening for the PL protease inhibitors, combining docking simulation and molecular mechanics calculation. Docking simulations were applied to 8,820 molecules in a chemical database of approved and investigational compounds. Based on the binding poses generated by the docking simulations, molecular mechanics calculations were performed to optimize the binding structures and to obtain the binding scores. Based on the binding scores, 57 compounds were selected for in vitro assay of the inhibitory activity. Five inhibitory compounds were identified from the in vitro measurement. The predicted binding structures of the identified five compounds were examined, and the significant interaction between the individual compound and the protease catalytic site was clarified. This work demonstrates that computational virtual screening by combining docking simulation with molecular mechanics calculation is effective for searching candidate compounds in drug discovery.

Exploring natural compound, Panicutine as leucine-rich repeat kinase 2 inhibitor against Parkinson’s disease: a structure-guided approach

Leucine-rich repeat kinase 2 (LRRK2) is a promising drug target for the therapeutic management of Parkinson’s disease (PD) and other neurodegenerative disorders. LRRK2 inhibitors have the potential to modulate neuroinflammation, reduce alpha-synuclein aggregation and improve motor symptoms in PD patients. Although LRRK2 inhibitors are still in the early stages of clinical development, the identification of potent and selective inhibitors through structure-guided approaches provides a promising avenue for the development of effective therapies for PD and other neurodegenerative disorders. In this study, natural compounds from the IMPPAT database were screened using a state-of-the-art computational virtual screening approach to identify potential inhibitors of LRRK2. We carried out a docking screening on a library of natural compounds and identified a few compounds with strong binding affinity, docking score and specificity towards LRRK2 as the top hits. These hits were then subjected to further analysis based on multiple parameters for the Pan-assay interference compounds and their physicochemical and pharmacokinetics evaluation followed by a detailed interaction analysis. After careful evaluation, one natural compound, Panicutine, was identified as a promising candidate for LRRK2 due to its significant affinity and specificity towards the LRRK2 binding pocket. Additionally, it exhibited drug-like properties with blood–brain barrier permeability as determined by ADMET properties. To gain a deeper understanding of the stability and conformational changes of the LRRK2–ligand complex, MD simulations were conducted for 100 nanoseconds under explicit solvent conditions followed by principal component analysis and free energy dynamics. The simulation results demonstrated that the LRRK2–Panicutine complex remained stable throughout the simulation trajectories. Based on these findings, it is concluded that Panicutine has the potential to act as a LRRK2 inhibitor against PD and other neurodegenerative disorders. Communicated by Ramaswamy H. Sarma

Discovery and Activity Evaluation of the Inhibitory Effect of Four Kinds of Traditional Chinese Medicine Extracts on the CYP3A4 Enzyme.

Traditional Chinese medicines that have inhibitory effects on the CYP3A4 enzymes were screened and their inhibitory effects were verified with in vitro bioassay. The computer virtual screening methods, including the CYP3A4 enzyme pharmacophore model and the molecular docking method, were used to rapidly screen the potential CYP3A4 inhibitors in the Traditional Chinese Medicine Database (TCMD), and then in vitro experiments were conducted to validate the computational data. A total of 413 chemical components in TCMD that have potential inhibitory effects on the CYP3A4 enzyme were screened, and four kinds of traditional Chinese medicines (Abrus precatorius, Andrographis paniculata, Angelica pubescens f. biserrata and Lithospermum erythrorhizon) contained the most potential CYP3A4 inhibitors; The results of the in vitro experiments showed that these four traditional Chinese medicine extracts all had certain degrees of inhibition on the CYP3A4 enzyme, with IC50 values of 5.15, 14.97, 15.2, and 24.21 μg/ml, respectively. The extracts of Abrus precatorius, Andrographis paniculata, Angelica pubescens f. biserrata and Lithospermum erythrorhizon had certain inhibitory effects on the CYP3A4 enzyme, and attention should be paid to the possible adverse reactions when they were used in combination with the CYP3A4 enzyme-substrate drugs. A combination of computational approaches might be a useful tool to identify potential inhibitors of the CYP3A4 enzyme from traditional Chinese medicine.

Discovery of novel and highly potential inhibitors of glycogen synthase kinase 3-beta (GSK-3β) through structure-based pharmacophore modeling, virtual computational screening, docking and in silico ADMET analysis

The protein Glycogen Synthase Kinase 3-Beta (GSK-3β), is a promising therapeutic target for treating various diseases such as neurodegenerative disorders, diabetes, inflammation and cancer. This study aims to investigate the potential of compounds targeting inflammation or carbohydrate metabolism to selectively inhibit GSK3β by binding to its ATP site. To achieve this goal, we filtered a database of 49367 molecules involved in carbohydrate metabolism or targeting inflammation using various computational analyses, including pharmacophore modeling, molecular docking, dynamic simulation, prime MM-GBSA calculation, and in silico ADME studies. We generated a pharmacophore model (hypo S: AADDHRR) using two different crystallographic complexes of GSK3β and evaluated the model’s performance in identifying hits using various parameters, including EF, GH, ROC, AUC and BEDROC. Subsequently, we performed various dockings (HTVS, SP, XP and IFD) for the retrieved hits and found that, 5 out of the top 10 ranked compounds had the scaffold of pyrazolidine 3,5-dione, which has never been reported to inhibit kinases. We also conducted ADMET studies to and concluded that compound N6 exhibited the best pharmacokinetic profile passing the blood-brain barrier, possessing high lipophilicity and a high coefficient of skin permeability in the intestines, along with good bioavailability and low toxicity risk assessment. Dynamic simulation were also performed indicating that compounds N6 derived from pyrazolidine 3,5-dione demonstrated better binding potential for GSK3β during the simulation period. Therefore, we propose that compounds derived from pyrazolidine-3,5-dione, which modulate the activity of lysosomal alpha-glucosidase could serve as a novel scaffold for the selective inhibition of GSK-3β. Communicated by Ramaswamy H. Sarma

MOViDA: multiomics visible drug activity prediction with a biologically informed neural network model.

The process of drug development is inherently complex, marked by extended intervals from the inception of a pharmaceutical agent to its eventual launch in the market. Additionally, each phase in this process is associated with a significant failure rate, amplifying the inherent challenges of this task. Computational virtual screening powered by machine learning algorithms has emerged as a promising approach for predicting therapeutic efficacy. However, the complex relationships between the features learned by these algorithms can be challenging to decipher. We have engineered an artificial neural network model designed specifically for predicting drug sensitivity. This model utilizes a biologically informed visible neural network, thereby enhancing its interpretability. The trained model allows for an in-depth exploration of the biological pathways integral to prediction and the chemical attributes of drugs that impact sensitivity. Our model harnesses multiomics data derived from a different tumor tissue sources, as well as molecular descriptors that encapsulate the properties of drugs. We extended the model to predict drug synergy, resulting in favorable outcomes while retaining interpretability. Given the imbalanced nature of publicly available drug screening datasets, our model demonstrated superior performance to state-of-the-art visible machine learning algorithms. MOViDA is implemented in Python using PyTorch library and freely available for download at https://github.com/Luigi-Ferraro/MOViDA. Training data, RIS score and drug features are archived on Zenodo https://doi.org/10.5281/zenodo.8180380.

Effects of Computer Virtual Screening and Treatment Based on Pharmacophore BRD4-Targeted Small-Molecule Inhibitors on NSE and CA19-9 Levels in Patients with Liver Cancer

Objective: Neuron-specific enolase (NSE) and carbohydrate antigen 19-9 (CA19-9) levels were compared before and after treatment with brd4-targeting small-molecule inhibitors based on pharmacophores and computer technology to investigate an efficient virtual screening method for bromodomain protein 4 (brd4)-targeted small-molecule inhibitors. The purpose of this research was to examine the efficacy of pharmacophore screening and BRD4 targeting by small-molecule inhibitors in the management of liver cancer. Methods: Subjects included 106 individuals with hepatocellular cancer who were hospitalised between July 2019 and December 2021. Using a receiver operating characteristic (ROC) curve, we assessed the clinical relevance and utility of pharmacophore-based virtual screening for brd4-targeted small-molecule inhibitors. The levels of the tumour markers NSE and CA19-9 were analysed for changes using computational methods. Results: The pharmacophore model was built using the HipHop technique, and its results were validated and optimised utilising computational technology analysis. Twenty-four small-molecule classes were covered by the pharmacophore, and the number of mismatched pharmacodynamic characteristics was 0. The first eight pharmacophores each had two clusters of hydrophobic atoms, a hydrogen bond receptor, and an aromatic ring at its core. There were no changes to the pharmacodynamic characteristics, although their locations varied. False-positive and true-positive rates are depicted on the horizontal and vertical axes, respectively, in the figure below; the other two pharmacophore groups contained a hydrophobic cluster, a hydrogen bond receptor, and the centre of an aromatic ring. The ROC curve was quite consistent, as indicated by an AUC value close to 1, which was between 0 and 1. For pharmacophores 07 and 06, the area under the curve (AUC) was 0.949 and 0.955, respectively, very near to 1. Both the hydrophobic cluster and the hydrogen bond receptor are required for a pharmacophore to exist. The examination of pharmacodynamic definitions revealed a positive correlation between database reliability and element type (P <0.05). Compared to pre-healing levels, post-healing CA19-9 and NSE levels were statistically considerably reduced (P < 0.05). Conclusions: The assessment approach was highly accurate and specific, and the pharmacophore-based virtual screening method and computer technology for brd4-targeting small-molecule inhibitors were of great benefit in the regulation of the cell cycle and the process of cell proliferation. Effective treatment modalities can be created to increase levels of tumour markers NSE and CA19-9, and pharmacophore and computational-based small-molecule inhibitors of brd4 can be employed as crucial virtual screening criteria for the evaluation of liver cancer.

Virtual Screening for the identification of potential inhibitors for Vp35 protein of Marburg Virus

Marburg virus (MARV) causes Marburg hemorrhagic disease (MHD) or Marburg hemorrhagic fever (MHF) in humans as well as in Non-Human Primates with a high mortality rate. It is a category A, risk group 4, an extremely dangerous pathogen that can also be used as a bioweapon. To date, no licensed medication against MARV infection is available. The current study attempts to design putative antiviral drugs or vaccine candidates against vp35, a polymerase cofactor protein of Marburg virus, using a computational virtual screening approach. From the zinc database 1600 compounds were selected for docking purpose. Top 20 molecules obtained via virtual screening approach were considered as potential inhibitors against the polymerase cofactor VP35 of MARV, based on Lipinski’s rule of five, binding affinity between -6.1 to -5.6 kcal/mol, a Root Mean Square Deviation (RMSD) value of zero, strong hydrogen bonding with vp35 and proper Absorption, Distribution, Metabolism and Excretion (ADME), hence can inhibit VP35. The features of these drugs can be further analyzed on the basis of in-vivo and in-vitro analysis in order to recommend any of these drugs for trials in humans and NHPs against Marburg hemorrhagic fever.

In silico screening, ADMET analysis and MD simulations of phytochemicals of Onosma bracteata Wall. as SARS CoV-2 inhibitors.

Being attracted with their cardiotonic, antidiabetic, cough relieving activity, treatment of fever, absorbent, anti-asthmatic, etc. activities reported in ancient Ayurvedic literature, phytochemicals of Onosma bracteata wall should be evaluated for their activity against SARS-CoV-2 virus. The main objective of this study is to identify a hit molecule for the inhibition of entry, replication, and protein synthesis of SARS CoV-2 virus into the host. To achieve given objective, computational virtual screening of phytochemicals of Onosma bracteata wall has been performed against three main viral targets: spike, RdRp, and Mpro. Further, the analysis of Lipinski's Ro5 and their estimation of ADMET profiles were performed using computational tools. The MD simulations studies of top hits against each viral target have also been performed for 20ns to ensure their stability. The analysis of results revealed that Pulmonarioside C (9) and other plant compounds showed better binding affinity towards targets than existing antiviral compounds, making them probable lead compounds against SARS-CoV-2. Structural modifications and studies through in silico analysis provided the founding stone for the establishment of SARS CoV-2 inhibitory potential of phytoconstitutents of Onosma bracteata wall.

Computational Screening of Inhibitory Compounds for SARS-Cov-2 3CL Protease with a Database Consisting of Approved and Investigational Chemicals.

Computational screening is one of the fundamental techniques in drug discovery. Each compound in a chemical database is bound to the target protein in virtual, and candidate compounds are selected from the binding scores. In this work, we carried out combinational computation of docking simulation to generate binding poses and molecular mechanics calculation to estimate binding scores. The coronavirus infectious disease has spread worldwide, and effective chemotherapy is strongly required. The viral 3-chymotrypsin-like (3CL) protease is a good target of low molecular-weight inhibitors. Hence, computational screening was performed to search for inhibitory compounds acting on the 3CL protease. As a preliminary assessment of the performance of this approach, we used 51 compounds for which inhibitory activity had already been confirmed. Docking simulations and molecular mechanics calculations were performed to evaluate binding scores. The preliminary evaluation suggested that our approach successfully selected the inhibitory compounds identified by the experiments. The same approach was applied to 8820 compounds in a database consisting of approved and investigational chemicals. Hence, docking simulations, molecular mechanics calculations, and re-evaluation of binding scores including solvation effects were performed, and the top 200 poses were selected as candidates for experimental assays. Consequently, 25 compounds were chosen for in vitro measurement of the enzymatic inhibitory activity. From the enzymatic assay, 5 compounds were identified to have inhibitory activities against the 3CL protease. The present work demonstrated the feasibility of a combination of docking simulation and molecular mechanics calculation for practical use in computational virtual screening.

Design and Identification of a Novel Antiviral Affinity Peptide against Fowl Adenovirus Serotype 4 (FAdV-4) by Targeting Fiber2 Protein.

Outbreaks of hydropericardium hepatitis syndrome caused by fowl adenovirus serotype 4 (FAdV-4) with a novel genotype have been reported in China since 2015, with significant economic losses to the poultry industry. Fiber2 is one of the important structural proteins on FAdV-4 virions. In this study, the C-terminal knob domain of the FAdV-4 Fiber2 protein was expressed and purified, and its trimer structure (PDB ID: 7W83) was determined for the first time. A series of affinity peptides targeting the knob domain of the Fiber2 protein were designed and synthesized on the basis of the crystal structure using computer virtual screening technology. A total of eight peptides were screened using an immunoperoxidase monolayer assay and RT-qPCR, and they exhibited strong binding affinities to the knob domain of the FAdV-4 Fiber2 protein in a surface plasmon resonance assay. Treatment with peptide number 15 (P15; WWHEKE) at different concentrations (10, 25, and 50 μM) significantly reduced the expression level of the Fiber2 protein and the viral titer during FAdV-4 infection. P15 was found to be an optimal peptide with antiviral activity against FAdV-4 in vitro with no cytotoxic effect on LMH cells up to 200 μM. This study led to the identification of a class of affinity peptides designed using computer virtual screening technology that targeted the knob domain of the FAdV-4 Fiber2 protein and may be developed as a novel potential and effective antiviral strategy in the prevention and control of FAdV-4.

Carboxymuconolactone decarboxylase is a prospective molecular target for multi-drug resistant Acinetobacter baumannii-computational modeling, molecular docking and dynamic simulation studies

Multidrug-resistant Acinetobacter baumannii (MDRAb), a priority-I pathogen declared by the World Health Organization, became a potential healthcare concern worldwide with a high mortality rate. Thus, the identification of putative molecular targets and potential lead molecules is an important concern in healthcare. The present study aimed to screen a prospective molecular target and effectual binders for the drug discovery of MDRAb by computational virtual screening approach. Based on the functional role, γ-carboxymuconolactone decarboxylase (CMD) was prioritized as the target and its three-dimensional (3D) structure was computationally modeled. Based on the availability of the 3D structure, twenty-five herbal molecules were selected by database search, and their drug-likeliness, pharmacokinetic, and toxicity features were predicted. The effectual binding of the selected molecules towards CMD was predicted by molecular docking. The stability of the best-docked complexes was predicted by molecular dynamics (MD) simulation for 100 ns and binding energy calculations were carried out by molecular mechanics generalized Born and surface area solvation (MM/GBSA) method. Out of twenty-five molecules screened, hirsutine (an indole alkaloid of Uncaria rhynchophylla) and thymoquinone (a phytochemical of Nigella sativa) were qualified for drug likeliness, pharmacokinetic, and toxicity features and demonstrated significant effectual binding to CMD when compared with the binding of co-crystallized inhibitor and CMD (control). The docked complexes of hirsutine and thymoquinone, and CMD were stabilized by the binding energies of −8. 30 and −8. 46 kcal/mol respectively. These molecules were qualified in terms of ideal drug likeliness, ADME, and toxicity properties. MD simulation studies showed that the ligand-protein complexes were stable throughout the simulation. The binding free energies of the complexes by MMGBSA were estimated to be −42.08157745 kcal/mol and −36.58618242 kcal/mol for hirsutine and thymoquinone respectively when compared with the calculated binding free energy of the control (−28.75032666 kcal/mol). This study concluded that hirsutine and thymoquinone can act as potential lead molecules against CMD and the present hypothesis can be scaled up to develop potential inhibitors against MDRAb.

Targeting mTOR/YY1 signaling pathway by quercetin through CYP7A1-mediated cholesterol-to-bile acids conversion alleviated type 2 diabetes mellitus induced hepatic lipid accumulation

BackgroundHepatic lipid accumulation was a major promoter for the further development of non-alcoholic fatty liver disease (NAFLD) in type 2 diabetes (T2DM). mTOR/YY1 signaling pathway regulated many metabolic processes in different organs, and played an important role in hepatic lipid metabolism. Thus, targeting mTOR/YY1 signaling pathway might be a novel therapeutic strategy of T2DM-associated NALFD. PurposeTo investigate the effects and the mechanism of quercetin against T2DM-associated NAFLD. Study design and methodsThe combine abilities of 24 flavonoid compounds with mTOR were detected by computer virtual screening (VS) and molecular modeling. mTOR/YY1 signaling pathway was examined in the liver of db/db mice, and high glucose (HG) and free fatty acid (FFA) co-cultured HepG2 cells. YY1 overexpression lentivirus vector and mTOR specific inhibitor rapamycin were used to further identify the indispensable role of mTOR/YY1 signaling pathway in quercetin's amelioration effect of hepatic lipid accumulation in vitro. Clinical studies, luciferase assay and chromatin immunoprecipitation (ChIP) assay were all carried out to investigate the potential mechanisms by which quercetin exerted its amelioration effect of hepatic lipid accumulation. ResultsQuercetin had the strongest ability to combine with mTOR and could competitively occupy its binding pocked. Along with the alleviated hepatic injury by quercetin, mTOR/YY1 signaling pathway was down-regulated in vivo and in vitro. However, the alleviation effect of quercetin against hepatic lipid accumulation was inhibited by YY1 overexpression in vitro. Mechanistically, the down-regulated nuclear YY1 induced by quercetin directly bound to CYP7A1 promoter and activated its transcription, resulting in the restoration of cholesterol homeostasis via the conversion of cholesterol-to-bile acids (BAs). ConclusionThe hepatoprotective effect of quercetin on T2DM-associated NAFLD was linked to the restoration of cholesterol homeostasis by the conversion of cholesterol-to-BAs via down-regulating mTOR/YY1 signaling pathway, leading to the increased CYP7A1 activity.

Ligational behavior of bidentate nitrogen–oxygen donor 8‐quinolinolazodye toward Ni2+ and Zn2+ ions: Preparation, spectral, thermal, experimental, theoretical, and docking studies

The 5‐(4‐aryl azo)‐8‐hydroxyquinolines (L1–L3) and their metal complexes with Ni2+ and Zn2+ have been produced. Various spectroscopic techniques have been employed to analyze the ligand and complexes. The structures of the prepared compounds have been confirmed by Fourier transform infrared (FT‐IR), proton nuclear magnetic resonance (1H NMR), molar conductance, magnetic measurements, thermal gravimetric and differential thermal analyses (TG and DTA), and electronic transition. The FT‐IR spectra showed that the ligands are coordinated to the metal ions in a bidentate manner with donor sites of the azomethine‐N and phenolic‐OH. The FT‐IR and UV–Visible spectra were compared with the calculated results and showed a good agreement. The mass spectra concluded that the ligands' molecular weights and the calculated estimated m/z values match well. The complexes contain coordinated and hydrated water as confirmed by the TG results. The complexes are tetrahedral, trigonal bipyramid, and octahedral geometrical structures and act as non‐electrolytes in dimethylformamide (DMF) solvent. Using density functional theory (DFT) at the B3LYP level of theory and the 6‐311G** basis set for the C, H, N, Cl, and O atoms and the LANL2DZ basis set for the Ni and Zn atoms. Natural bond orbital (NBO) analysis was used to compute and describe the natural charge population and precise electronic configuration. The small energy gap between HOMO and LUMO energies suggests that charge transfer occurs within Ni2+ and Zn2+ complexes. The first‐order hyperpolarizability (β) of the complexes and the anisotropy of polarizability (α) values show promising optical properties. The electronic transitions of the prepared complexes were computed by time‐dependent density functional theory (TD‐DFT/PCM) with the B3LYP method using a 6‐31G** basis set. The ethanol polarizable continuum model (PCM) was used to simulate the solvent effect. Utilizing a computer virtual screening technique through molecular docking, the anticipation of binding of 8‐quinolinolazodye derivatives and their complexes with human CORONA virus protein (PDB ID: 5epw) was done.

Gimap5 promoted RSV degradation through interaction with M6PR.

Respiratory syncytial virus (RSV) is one of the main pathogens of viral pneumonia and bronchiolitis in infants and young children and life-threatening diseases among infants and young children. GTPases of the immune-associated protein family (GIMAP) are new family members of immune-associated GTPases. In recent years, much attention has been paid to the function of the GIMAP family in coping with infection and stress. Gimap5 is a member of the GIMAP family, which may be correlated with anti-infectious immunity. RT-qPCR, Western blot, and indirect immunofluorescence (IFA) were used to detect the expression of Gimap5, M6PR and IGF1R(the major RSV receptor). Transmission electron microscopy (TEM) was used to detect the degradation of RSV in Gimap5-overexpressed or -silent cell lines. Computer virtual screening was used to screen small molecule compounds targeting Gimap5 and the anti-RSV effects were explored through in vivo and in vitro experiments. GIMAP5 and M6PR were significantly downregulated after RSV infection. Gimap5 accelerated RSV degradation in lysosomes by interacting with M6PR, and further prevented RSV invasion by downregulating the expression of RSV surface receptor IGF1R. Three small molecule compounds targeting Gimap5 were confirmed to be the agonists of Gimap5. The three compounds effectively inhibited RSV infection and RSV-induced complications. Gimap5 promotes the degradation of RSV and its receptor through interacting with M6PR. Gimap5 agonists can effectively reduce RSV infection and RSV-induced complication in vivo and in vitro, which provides a new choice for the treatment of RSV.

Identification of FDA-approved drugs against SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) through computational virtual screening

The SARS-CoV-2 coronavirus is responsible for the COVID-19 outbreak, which overwhelmed millions of people worldwide; hence, there is an urgency to identify appropriate antiviral drugs. This study focuses on screening compounds that inhibit RNA-dependent RNA-polymerase (RdRp) essential for RNA synthesis required for replication of positive-strand RNA viruses. Computational screening against RdRp using Food and Drug Administration (FDA)-approved drugs identified ten prominent compounds with binding energies of more than − 10.00 kcal/mol, each a potential inhibitor of RdRp. These compounds’ binding energy is comparable to known RdRp inhibitors remdesivir (IC50 = 10.09 μM, SI = 4.96) and molnupiravir (EC50 = 0.67 − 2.66 µM) and 0.32–2.03 µM). Remdesivir and molnupiravir have been tested in clinical trial and remain authorized for emergency use in the treatment of COVID-19. In docking simulations, selected compounds are bound to the substrate-binding pocket of RdRp and showed hydrophobic and hydrogen bond interaction. For molecular dynamics simulation, capmatinib, pralsetinib, ponatinib, and tedizolid phosphate were selected from the initial ten candidate compounds. MD simulation indicated that these compounds are stable at 50-ns MD simulation when bound to RdRp protein. The screen hit compounds, remdesivir, molnupiravir, and GS-441524, are bound in the substrate binding pocket with good binding-free energy. As a consequence, capmatinib, pralsetinib, ponatinib, and tedizolid phosphate are potential new inhibitors of RdRp protein with potential of limiting COVID-19 infection by blocking RNA synthesis.

In silico approach identified benzoylguanidines as SARS-CoV-2 main protease (Mpro) potential inhibitors

The coronavirus disease-2019 (COVID-19) pandemic, caused by the novel coronavirus severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), became the highest public health crisis nowadays. Although the use of approved vaccines for emergency immunization and the reuse of FDA-approved drugs remains at the forefront, the search for new, more selective, and potent drug candidates from synthetic compounds is also a viable alternative to combat this viral disease. In this context, the present study employed a computational virtual screening approach based on molecular docking and molecular dynamics (MD) simulation to identify possible inhibitors for SARS-CoV-2 Mpro (main protease), an important molecular target required for the maturation of the various polyproteins involved in viral replication. The virtual screening approach selected four potential inhibitors against SARS-CoV-2 Mpro. In addition, MD simulation studies revealed changes in the positions of the ligands during the simulations compared to the complex obtained in the molecular docking studies, showing the benzoylguanidines LMed-110 and LMed-136 have a higher affinity for the active site compared to the other structures that tended to leave the active site. Besides, there was a better understanding of the formation and stability of the existing H-bonds in the formed complexes and the energetic contributions to the stability of the target-ligand molecular complexes. Finally, the in silico prediction of the ADME profile suggested that LMed-136 has drug-like characteristics and good pharmacokinetic properties. Therefore, from the present study, it can be suggested that these structures can inhibit SARS-CoV-2 Mpro. Nevertheless, further studies are needed in vitro assays to investigate the antiviral properties of these structures against SARS-CoV-2.

Novel natural inhibitors targeting KRAS G12C by computational study.

Ideal leading and nominee compounds with inhibiting effects on KRAS G12C were selected from the ZINC database, laying a cornerstone for the progress of anticancer drugs. A variety of computational virtual screening methods were utilized to screen possible inhibitors of KRAS G12C. LibDock was utilized to estimate 17 930 compounds and the top 20 were nominated for additional study, which was absorption, distribution, metabolism, and excretion and harmfulness prediction. Molecule docking was employed to prove the binding connection between certain ligands and KRAS G12C. Natural novel compounds ZINC000012494057 and ZINC000003789195 were selected to bind stably with KRAS G12C. In addition, they had lower scores in Ames mutagenicity, rodent carcinogenicity, cytochrome P450 2D6(CYP2D6) tolerance, and non-developmental toxicity potential. Molecular dynamic simulations demonstrate that the combination of ZINC000012494057 and ZINC000003789195 with KRAS G12C has more favorable potential energy, which provides conditions for their stable existence in the natural environment. Natural compounds ZINC000012494057 and ZINC000003789195 were identified as KRAS G12C potential inhibitors. These two compounds have been verified to have enormous importance for the progress of anticancer medicines.