Identification of Papain-Like Protease inhibitors of SARS CoV-2 through HTVS, Molecular docking, MMGBSA and Molecular dynamics approach

Exploring the sedative properties of natural molecules from hop cones (Humulus lupulus) as promising natural anxiolytics through GABA receptors and the human serotonin transporter

This research work aimed to identify the main components that are responsible for the sedative properties of hop cones and allocate their targets. This investigation was performed through molecular docking, molecular dynamic simulations, root mean square fluctuation (RMSF) analysis, and DFT calculation techniques. The tested compounds from Humulus lupulus were compared to diazepam and paroxetine. Molecular docking showed that two-thirds of the compounds had a good affinity to gamma-aminobutyric acid (GABA), outperforming diazepam, while only three surpassed paroxetine on the SERT. Compounds 3,5-dihydroxy-4,6,6-tris(3-methylbut-2-en-1-yl)-2-(3-methylbutanoyl)cyclohexa-2,4-dien-1-one (5) and (S,E)-8-(3,7-dimethylocta-2,6-dien-1-yl)-5,7-dihydroxy-2-(4-hydroxyphenyl)chromen-4-one (15) showed stable binding and favorable energy parameters, indicating their potential for targeting GABA receptors and the SERT. This study provides a basis for future clinical research on these promising compounds.

3D-QSAR, Scaffold Hopping, Virtual Screening, and Molecular Dynamics Simulations of Pyridin-2-one as mIDH1 Inhibitors

Isocitrate dehydrogenase 1 (IDH1) is a necessary enzyme for cellular respiration in the tricarboxylic acid cycle. Mutant isocitrate dehydrogenase 1 (mIDH1) has been detected overexpressed in a variety of cancers. mIDH1 inhibitor ivosidenib (AG-120) was only approved by the Food and Drug Administration (FDA) for marketing, nevertheless, a range of resistance has been frequently reported. In this study, several mIDH1 inhibitors with the common backbone pyridin-2-one were explored using the three-dimensional structure-activity relationship (3D-QSAR), scaffold hopping, absorption, distribution, metabolism, excretion (ADME) prediction, and molecular dynamics (MD) simulations. Comparative molecular field analysis (CoMFA, R2 = 0.980, Q2 = 0.765) and comparative molecular similarity index analysis (CoMSIA, R2 = 0.997, Q2 = 0.770) were used to build 3D-QSAR models, which yielded notably decent predictive ability. A series of novel structures was designed through scaffold hopping. The predicted pIC50 values of C3, C6, and C9 were higher in the model of 3D-QSAR. Additionally, MD simulations culminated in the identification of potent mIDH1 inhibitors, exhibiting strong binding interactions, while the analyzed parameters were free energy landscape (FEL), radius of gyration (Rg), solvent accessible surface area (SASA), and polar surface area (PSA). Binding free energy demonstrated that C2 exhibited the highest binding free energy with IDH1, which was -93.25 ± 5.20 kcal/mol. This research offers theoretical guidance for the rational design of novel mIDH1 inhibitors.

Coupling of SARS-CoV-2 to Aβ Amyloid Fibrils

The COVID-19 infection has been more problematic for individuals with certain health predispositions. Coronaviruses could also interfere with neural diseases if the viruses succeed in entering the brain. Therefore, it might be of principal interest to examine a possible coupling of coronaviruses and amyloid fibrils. Here, molecular dynamics simulations were used to investigate direct coupling of SARS-CoV-2 and Aβ fibrils, which play a central role in neural diseases. The simulations revealed several stable binding configurations and their dynamics of Aβ42 fibrils attached to spike proteins of the Omicron and Alpha variants of SARS-CoV-2.

Antiviral fibrils of self-assembled peptides with tunable compositions

The lasting threat of viral pandemics necessitates the development of tailorable first-response antivirals with specific but adaptive architectures for treatment of novel viral infections. Here, such an antiviral platform has been developed based on a mixture of hetero-peptides self-assembled into functionalized β-sheets capable of specific multivalent binding to viral protein complexes. One domain of each hetero-peptide is designed to specifically bind to certain viral proteins, while another domain self-assembles into fibrils with epitope binding characteristics determined by the types of peptides and their molar fractions. The self-assembled fibrils maintain enhanced binding to viral protein complexes and retain high resilience to viral mutations. This method is experimentally and computationally tested using short peptides that specifically bind to Spike proteins of SARS-CoV-2. This platform is efficacious, inexpensive, and stable with excellent tolerability.

The identification of cianidanol as a selective estrogen receptor beta agonist and evaluation of its neuroprotective effects on Parkinson's disease models

AIM

The present study aims to identify selective estrogen receptor beta (ERβ) agonists and to evaluate the neuroprotective mechanism in Parkinson's disease (PD) models.

MAIN METHODS

In-silico studies were carried out using Maestro and GROMACS. Neuroprotective activity and apoptosis were evaluated using cytotoxicity assay and flow cytometry respectively. Gene expression studies were carried out by reverse transcription polymerase chain reaction. Motor and cognitive functions were assessed by actophotometer, rotarod, catalepsy, and elevated plus maze. The neuronal population in the substantia nigra and striatum of rats was assessed by hematoxylin and eosin staining.

KEY FINDINGS

Cianidanol was identified as a selective ERβ agonist through virtual screening. The cianidanol-ERβ complex is stable during the 200 ns simulation and was able to retain the interactions with key amino acid residues. Cianidanol (25 μM) prevents neuronal toxicity and apoptosis induced by rotenone in differentiated SH-SY5Y cells. Additionally, cianidanol (25 μM) increases the expression of ERβ, cathepsin D, and Nrf2 transcripts. The neuroprotective effects of cianidanol (25 μM) were reversed in the presence of a selective ERβ antagonist. In this study, we found that selective activation of ERβ could decrease the transcription of α-synuclein gene. Additionally, cianidanol (10, 20, 30 mg/kg, oral) improves the motor and cognitive deficit in rats induced by rotenone.

SIGNIFICANCE

Cianidanol shows neuroprotective action in PD models and has the potential to serve as a novel therapeutic agent for the treatment of PD.

Exploring the therapeutic mechanisms of Gleditsiae Spina acting on pancreatic cancer via network pharmacology, molecular docking and molecular dynamics simulation

Pancreatic cancer is one of the most aggressive tumors and also has a low survival rate. The dried spines of Gleditsia sinensis Lam are known as "Gleditsiae Spina" and they mostly contain flavonoids, phenolic acids, terpenoids, steroids, and other chemical components. In this study, the potential active components and molecular mechanisms of Gleditsiae Spina for treating pancreatic cancer were systematically revealed by network pharmacology, molecular docking and molecular dynamics simulations (MDs). RAC-alpha serine/threonine-protein kinase (AKT1), cellular tumor antigen p53 (TP53), tumor necrosis factor α (TNFα), interleukin-6 (IL6) and vascular endothelial growth factor A (VEGFA) were common targets of Gleditsiae Spina, human cytomegalovirus infection signaling pathway, AGE-RAGE signaling pathway in diabetic complications, and MAPK signaling pathway were critical pathways of fisetin, eriodyctiol, kaempferol and quercetin in the treatment of pancreatic cancer. Molecular dynamics simulations (MDs) results showed that eriodyctiol and kaempferol have long-term stable hydrogen bonds and high binding free energy for TP53 (-23.64 ± 0.03 kcal mol-1 and -30.54 ± 0.02 kcal mol-1, respectively). Collectively, our findings identify active components and potential targets in Gleditsiae Spina for the treatment of pancreatic cancer, which may help to explore leading compounds and potential drugs for pancreatic cancer.

Endogenous piRNAs Can Interact with the Omicron Variant of the SARS-CoV-2 Genome

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the COVID-19 pandemic, can still infect populations in many countries around the globe. The Omicron strain is the most mutated variant of SARS-CoV-2. The high transmissibility of the strain and its ability to evade immunity necessitate a priority study of its properties in order to quickly create effective means of preventing its spread. The current research aimed to examine the in silico interaction between PIWI-interacting RNAs (piRNAs) and the SARS-CoV-2 genome (gRNA) to identify endogenous piRNAs and propose synthetic piRNAs with strong antiviral activity for drug development. This study used validated bioinformatic approaches regarding the interaction of more than eight million piRNAs with the SARS-CoV-2 genome. The piRNAs’ binding sites (BSs) in the 5′UTR were located with overlapping nucleotide sequences termed clusters of BSs. Several BSs clusters have been found in the nsp3, nsp7, RNA-dependent RNA polymerase, endoRNAse, S surface glycoprotein, ORF7a, and nucleocapsid. Sixteen synthetic piRNAs that interact with gRNA have been proposed with free binding energy ranging from −170 kJ/mol to −175 kJ/mol, which can be used to create drugs that suppress the reproduction of SARS-CoV-2.

High-Throughput Screening for the Potential Inhibitors of SARS-CoV-2 with Essential Dynamic Behavior

Global health security has been challenged by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. Due to the lengthy process of generating vaccinations, it is vital to reposition currently available drugs in order to relieve anti-epidemic tensions and accelerate the development of therapies for Coronavirus Disease 2019 (COVID-19), the public threat caused by SARS-CoV-2. High throughput screening techniques have established their roles in the evaluation of already available medications and the search for novel potential agents with desirable chemical space and more cost-effectiveness. Here, we present the architectural aspects of highthroughput screening for SARS-CoV-2 inhibitors, especially three generations of virtual screening methodologies with structural dynamics: ligand-based screening, receptor-based screening, and machine learning (ML)-based scoring functions (SFs). By outlining the benefits and drawbacks, we hope that researchers will be motivated to adopt these methods in the development of novel anti- SARS-CoV-2 agents.

Virtual screening of substances used in the treatment of SARS-CoV-2 infection and analysis of compounds with known action on structurally similar proteins from other viruses

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is considered the etiological agent of the disease that caused the COVID-19 pandemic, and for which there is currently no effective treatment. This pandemic has shown that the rapid identification of therapeutic compounds is critical (when a new virus with high transmissibility occurs) to prevent or reduce as much as possible the loss of human lives. To meet the urgent need for drugs, many strategies were applied for the discovery, respectively the identification of potential therapies / drugs for SARS-CoV-2. Molecular docking and virtual screening are two of the in silico tools/techniques that provided the identification of few SARS-CoV-2 inhibitors, removing ineffective or less effective drugs and thus preventing the loss of resources such as time and additional costs. The main target of this review is to provide a comprehensive overview of how in-silico tools have been used in the crisis management of anti-SARS-CoV-2 drugs, especially in virtual screening of substances used in the treatment of SARS-CoV-2 infection and analysis of compounds with known action on structurally similar proteins from other viruses; also, completions were added to the way in which these methods came to meet the requirements of biomedical research in the field. Moreover, the importance and impact of the topic approached for researchers was highlighted by conducting an extensive bibliometric analysis.