The Discovery of Potential SARS-CoV-2 Natural Inhibitors among 4924 African Metabolites Targeting the Papain-like Protease: A Multi-Phase In Silico Approach.

Four compounds, hippacine, 4,2'-dihydroxy-4'-methoxychalcone, 2',5'-dihydroxy-4-methoxychalcone, and wighteone, were selected from 4924 African natural metabolites as potential inhibitors against SARS-CoV-2 papain-like protease (PLpro, PDB ID: 3E9S). A multi-phased in silico approach was employed to select the most similar metabolites to the co-crystallized ligand (TTT) of the PLpro through molecular fingerprints and structural similarity studies. Followingly, to examine the binding of the selected metabolites with the PLpro (molecular docking. Further, to confirm this binding through molecular dynamics simulations. Finally, in silico ADMET and toxicity studies were carried out to prefer the most convenient compounds and their drug-likeness. The obtained results could be a weapon in the battle against COVID-19 via more in vitro and in vivo studies.

Discovery of Potential SARS-CoV-2 Papain-like Protease Natural Inhibitors Employing a Multi-Phase In Silico Approach.

As an extension of our research against COVID-19, a multiphase in silico approach was applied in the selection of the three most common inhibitors (Glycyrrhizoflavone (76), Arctigenin (94), and Thiangazole (298)) against papain-like protease, PLpro (PDB ID: 4OW0), among 310 metabolites of natural origin. All compounds of the exam set were reported as antivirals. The structural similarity between the examined compound set and S88, the co-crystallized ligand of PLpro, was examined through structural similarity and fingerprint studies. The two experiments pointed to Brevicollin (28), Cryptopleurine (41), Columbamine (46), Palmatine (47), Glycyrrhizoflavone (76), Licochalcone A (87), Arctigenin (94), Termilignan (98), Anolignan B (99), 4,5-dihydroxy-6″-deoxybromotopsentin (192), Dercitin (193), Tryptanthrin (200), 6-Cyano-5-methoxy-12-methylindolo [2, 3A] carbazole (211), Thiangazole (298), and Phenoxan (300). The binding ability against PLpro was screened through molecular docking, disclosing the favorable binding modes of six metabolites. ADMET studies expected molecules 28, 76, 94, 200, and 298 as the most favorable metabolites. Then, molecules 76, 94, and 298 were chosen through in silico toxicity studies. Finally, DFT studies were carried out on glycyrrhizoflavone (76) and indicated a high level of similarity in the molecular orbital analysis. The obtained data can be used in further in vitro and in vivo studies to examine and confirm the inhibitory effect of the filtered metabolites against PLpro and SARS-CoV-2.

A Machine Learning Solution to Mitigate Co-Tier Interference and Improve QoE for Video Delivery in 5G HetNets

The exponential growth of rich media services across the globe has led to a massive increase in data traffic. The recent COVID-19 pandemic has also contributed to this surge as user traffic patterns have witnessed a sharp growth in demand for rich media services, particularly video conferencing (e.g., Zoom, Skype, Teams) and entertainment (e.g., Netflix, Hulu, Amazon). This has put a significant pressure on the current Heterogeneous Network (HetNet) environments, impacting end users' Quality of Experience (QoE). One of the promising solutions to deal with this issue is the introduction of 5th Generation (5G) networks within HetNets and the deployment of small cells (i.e., femtocells) to shift the load from the traditional macrocells. Yet, the big challenge with this approach is the co-tier interference that can occur between different femtocell users. To mitigate this problem, we propose a Machine Learning Interference Classification and Offloading Scheme (MLICOS) that classifies users' traffic based on the level of experienced co-tier interference and offloads the most affected traffic to nearby femtocells, with the ultimate goal of improving the users' QoE. MLICOS performance was evaluated using various QoE metrics, including Peak signal-to-noise ratio (PSNR), Structural Similarity Index Measure (SSIM), and Video Multi method Assessment Fusion (VMAF), and was compared to Proportional Fair (PF) scheduling algorithm, Variable Radius and Proportional Fair scheduling (VR+PF) algorithm, and a Cognitive Approach (CA). Simulation results show that MLICOS generates the highest PSNR, SSIM, and VMAF compared to the other schemes, therefore providing high user QoE. © 2022 IEEE.

Isolation and In Silico Inhibitory Potential against SARS-CoV-2 RNA Polymerase of the Rare Kaempferol 3-O-(6″-O-acetyl)-Glucoside from Calligonum tetrapterum.

The phytochemical constituents of Calligonum tetrapterum Jaub. & Spach (Family Polygonaceae) were studied for the first time. The study resulted in the isolation of the rare flavonol glycoside, kaempferol 3-O-(6″-O-acetyl)-glucoside,(K3G-A). The potential inhibitive activity of K3G-A toward SARS-CoV-2 was investigated utilizing several in silico approaches. First, molecular fingerprints and structural similarity experiments were carried out for K3G-A against nine co-crystallized ligands of nine proteins of SARS-CoV-2 to reveal if there is a structural similarity with any of them. The conducted studies showed the high similarity of K3G-A and remdesivir, the co-crystallized ligand of SARS-CoV-2 RNA-dependent RNA polymerase (PDB ID: 7BV2), RdRp. To validate these findings, a DFT study was conducted and confirmed the proposed similarity on the electronic and orbital levels. The binding of K3G-A against RdRp was confirmed through molecular docking studies exhibiting a binding energy of -27.43 kcal/mol, which was higher than that of remdesivir. Moreover, the RdRp-K3G-A complex was subjected to several MD studies at 100 ns that authenticated the accurate mode of binding and the correct dynamic behavior. Finally, in silico ADMET and toxicity evaluation of K3G-A was conducted and denoted the safety and the drug-likeness of K3G-A. In addition to K3G-A, two other metabolites were isolated and identified to be kaempferol (K) and ß-sitosterol (ß-S).

A Multistage In Silico Study of Natural Potential Inhibitors Targeting SARS-CoV-2 Main Protease.

Among a group of 310 natural antiviral natural metabolites, our team identified three compounds as the most potent natural inhibitors against the SARS-CoV-2 main protease (PDB ID: 5R84), Mpro. The identified compounds are sattazolin and caprolactin A and B. A validated multistage in silico study was conducted using several techniques. First, the molecular structures of the selected metabolites were compared with that of GWS, the co-crystallized ligand of Mpro, in a structural similarity study. The aim of this study was to determine the thirty most similar metabolites (10%) that may bind to the Mpro similar to GWS. Then, molecular docking against Mpro and pharmacophore studies led to the choice of five metabolites that exhibited good binding modes against the Mpro and good fit values against the generated pharmacophore model. Among them, three metabolites were chosen according to ADMET studies. The most promising Mpro inhibitor was determined by toxicity and DFT studies to be caprolactin A (292). Finally, molecular dynamics (MD) simulation studies were performed for caprolactin A to confirm the obtained results and understand the thermodynamic characteristics of the binding. It is hoped that the accomplished results could represent a positive step in the battle against COVID-19 through further in vitro and in vivo studies on the selected compounds.

Multi-Step In Silico Discovery of Natural Drugs against COVID-19 Targeting Main Protease.

In continuation of our antecedent work against COVID-19, three natural compounds, namely, Luteoside C (130), Kahalalide E (184), and Streptovaricin B (278) were determined as the most promising SARS-CoV-2 main protease (Mpro) inhibitors among 310 naturally originated antiviral compounds. This was performed via a multi-step in silico method. At first, a molecular structure similarity study was done with PRD_002214, the co-crystallized ligand of Mpro (PDB ID: 6LU7), and favored thirty compounds. Subsequently, the fingerprint study performed with respect to PRD_002214 resulted in the election of sixteen compounds (7, 128, 130, 156, 157, 158, 180, 184, 203, 204, 210, 237, 264, 276, 277, and 278). Then, results of molecular docking versus Mpro PDB ID: 6LU7 favored eight compounds (128, 130, 156, 180, 184, 203, 204, and 278) based on their binding affinities. Then, in silico toxicity studies were performed for the promising compounds and revealed that all of them have good toxicity profiles. Finally, molecular dynamic (MD) simulation experiments were carried out for compounds 130, 184, and 278, which exhibited the best binding modes against Mpro. MD tests revealed that luteoside C (130) has the greatest potential to inhibit SARS-CoV-2 main protease.

Joint approach based quality assessment scheme for compressed and distorted images

The Digital era is improving day by day. We can easily send and receive multimedia data, but it is challenging to know that the data is of actual quality or degraded and compressed. A similar problem arises in the medical imaging domain;it is too tedious to determine whether the image has a particular quality level or not to modify it further. So here we represent one specific method that is termed as “Joint”- Image Quality Estimation Approach as it is a combination of reference-based and no-reference-based Image Quality assessment methods;due to this fact, we termed it “Joint” approach. In some cases, the reference-based image quality assessment methods cannot predict the exact values because we don't know that the reference image that is considered to find the quality of a test image is an actual one or previously compressed. So, this will create a situation where we get the wrong IQA value for the test image. The method proposed by us can overcome this problem. First, we decide the quality of the reference image by using No-reference-based models. Then, we check the final IQA value for a test image with the reference-based models. We created a database of 72 chest images of COVID-19 infected patients and its four-level compressed images for the experiment. Results that are shown in this work are very effective and elaborated with proper justifications.

In Silico Exploration of Potential Natural Inhibitors against SARS-Cov-2 nsp10.

In continuation of our previous effort, different in silico selection methods were applied to 310 naturally isolated metabolites that exhibited antiviral potentialities before. The applied selection methods aimed to pick the most relevant inhibitor of SARS-CoV-2 nsp10. At first, a structural similarity study against the co-crystallized ligand, S-Adenosyl Methionine (SAM), of SARS-CoV-2 nonstructural protein (nsp10) (PDB ID: 6W4H) was carried out. The similarity analysis culled 30 candidates. Secondly, a fingerprint study against SAM preferred compounds 44, 48, 85, 102, 105, 182, 220, 221, 282, 284, 285, 301, and 302. The docking studies picked 48, 182, 220, 221, and 284. While the ADMET analysis expected the likeness of the five candidates to be drugs, the toxicity study preferred compounds 48 and 182. Finally, a density-functional theory (DFT) study suggested vidarabine (182) to be the most relevant SARS-Cov-2 nsp10 inhibitor.

Superimposition of Viral Protein Structures: A Means to Decipher the Phylogenies of Viruses.

Superimposition of protein structures is key in unravelling structural homology across proteins whose sequence similarity is lost. Structural comparison provides insights into protein function and evolution. Here, we review some of the original findings and thoughts that have led to the current established structure-based phylogeny of viruses: starting from the original observation that the major capsid proteins of plant and animal viruses possess similar folds, to the idea that each virus has an innate "self". This latter idea fueled the conceptualization of the PRD1-adenovirus lineage whose members possess a major capsid protein (innate "self") with a double jelly roll fold. Based on this approach, long-range viral evolutionary relationships can be detected allowing the virosphere to be classified in four structure-based lineages. However, this process is not without its challenges or limitations. As an example of these hurdles, we finally touch on the difficulty of establishing structural "self" traits for enveloped viruses showcasing the coronaviruses but also the power of structure-based analysis in the understanding of emerging viruses.