High-Throughput Screening and Quantum Mechanics for Identifying Potent Inhibitors Against Mac1 Domain of SARS-CoV-2 Nsp3

Toxicological study on ibuprofen and selenium in freshwater mussel Lamellidens marginalis and exploring the microbial cytochrome through modelling and quantum mechanics approaches for its toxicity degradation in contaminated environment

Toxicological stress in aquatic organisms is caused by the discharge of hundreds of toxic pollutants and contaminants among which the current study concentrates on the toxic effect of non-steroidal anti-inflammatory drug ibuprofen (IBF) and the trace element selenium (Se). In this study, IBF and Se toxicity on freshwater mussel Lamellidens marginalis was studied for 14 days, and in silico predictions for their degradation were made using Molecular modelling and Quantum Mechanical approaches. The degrading propensity of cytochrome c oxidase proteins from Trametes verticillatus and Thauera selenatis (Turkey tail fungi and Gram-negative bacteria) is examined into atom level. The results of molecular modelling study indicate that ionic interactions occur in the T. selenatis-HEME bound complex by Se interacting directly with HEME, and in the T. versicolor-HEME bound complex by IBF bound to a nearby region of HEME. Experimental and theoretical findings suggest that, the toxicological effects of Se and IBF pollution can be reduced by bioremediation with special emphasis on T. versicolor, and T. selenatis, which can effectively interact with Se and IBF present in the environment and degrade them. Besides, this is the first time in freshwater mussel L. marginalis that ibuprofen and selenium toxicity have been studied utilizing both experimental and computational methodologies for their bioremediation study.

Structure-Based High-Throughput Virtual Screening and Molecular Dynamics Simulation for the Discovery of Novel SARS-CoV-2 NSP3 Mac1 Domain Inhibitors

Since the emergence of SARS-CoV-2, many genetic variations within its genome have been identified, but only a few mutations have been found in nonstructural proteins (NSPs). Among this class of viral proteins, NSP3 is a multidomain protein with 16 different domains, and its largest domain is known as the macrodomain or Mac1 domain. In this study, we present a virtual screening campaign in which we computationally evaluated the NCI anticancer library against the NSP3 Mac1 domain, using Molegro Virtual Docker. The top hits with the best MolDock and Re-Rank scores were selected. The physicochemical analysis and drug-like potential of the top hits were analyzed using the SwissADME data server. The binding stability and affinity of the top NSC compounds against the NSP3 Mac1 domain were analyzed using molecular dynamics (MD) simulation, using Desmond software, and their interaction energies were analyzed using the MM/GBSA method. In particular, by applying subsequent computational filters, we identified 10 compounds as possible NSP3 Mac1 domain inhibitors. Among them, after the assessment of binding energies (ΔGbind) on the whole MD trajectories, we identified the four most interesting compounds that acted as strong binders of the NSP3 Mac1 domain (NSC-358078, NSC-287067, NSC-123472, and NSC-142843), and, remarkably, it could be further characterized for developing innovative antivirals against SARS-CoV-2.

An Update on the Current State of SARS-CoV-2 Mac1 Inhibitors

Non-structural protein 3 (nsp3) from all coronaviruses (CoVs) contains a conserved macrodomain, known as Mac1, that has been proposed as a potential therapeutic target for CoVs due to its critical role in viral pathogenesis. Mac1 is an ADP-ribose binding protein and ADP-ribosylhydrolase that promotes replication and blocks IFN responses, though the precise mechanisms it uses to carry out these functions remain unknown. Over the past 3 years following the onset of COVID-19, several groups have used high-throughput screening with multiple assays and chemical modifications to create unique chemical inhibitors of the SARS-CoV-2 Mac1 protein. Here, we summarize the current efforts to identify selective and potent inhibitors of SARS-CoV-2 Mac1.

Structure-Based Virtual Screening, Molecular Docking, Molecular Dynamics Simulation of EGFR for the Clinical Treatment of Glioblastoma

Glioblastoma (GBM) is a WHO Grade IV tumor with poor visibility, a high risk of comorbidity, and exhibit limited treatment options. Resurfacing from second-rate glioma was originally classified as either mandatory or optional. Recent interest in personalized medicine has motivated research toward biomarker stratification-based individualized illness therapy. GBM biomarkers have been investigated for their potential utility in prognostic stratification, driving the development of targeted therapy and customizing therapeutic treatment. Due to the availability of a specific EGFRvIII mutational variation with a clear function in glioma-genesis, recent research suggests that EGFR has the potential to be a prognostic factor in GBM, while others have shown no clinical link between EGFR and survival. The pre-existing pharmaceutical lapatinib (PubChem ID: 208,908) with a higher affinity score is used for virtual screening. As a result, the current study revealed a newly screened chemical (PubChem CID: 59,671,768) with a higher affinity than the previously known molecule. When the two compounds are compared, the former has the lowest re-rank score. The time-resolved features of a virtually screened chemical and an established compound were investigated using molecular dynamics simulation. Both compounds are equivalent, according to the ADMET study. This report implies that the virtual screened chemical could be a promising Glioblastoma therapy.

Identification of the SARS-unique domain of SARS-CoV-2 as an antiviral target

SARS-CoV-2 nsp3 is essential for viral replication and host responses. The SARS-unique domain (SUD) of nsp3 exerts its function through binding to viral and host proteins and RNAs. Herein, we show that SARS-CoV-2 SUD is highly flexible in solution. The intramolecular disulfide bond of SARS-CoV SUD is absent in SARS-CoV-2 SUD. Incorporating this bond in SARS-CoV-2 SUD allowed crystal structure determination to 1.35 Å resolution. However, introducing this bond in SARS-CoV-2 genome was lethal for the virus. Using biolayer interferometry, we screened compounds directly binding to SARS-CoV-2 SUD and identified theaflavin 3,3'-digallate (TF3) as a potent binder, Kd 2.8 µM. TF3 disrupted the SUD-guanine quadruplex interactions and exhibited anti-SARS-CoV-2 activity in Vero E6-TMPRSS2 cells with an EC50 of 5.9 µM and CC50 of 98.5 µM. In this work, we provide evidence that SARS-CoV-2 SUD harbors druggable sites for antiviral development.

Glyphosate potentiates insulin resistance in skeletal muscle through the modulation of IRS-1/PI3K/Akt mediated mechanisms: An in vivo and in silico analysis

Herbicides have been linked to a higher risk of developing diabetes. Certain herbicides also operate as environmental toxins. Glyphosate is a popular and extremely effective herbicide for weed control in grain crops that inhibits the shikimate pathway. It has been shown to negatively influence endocrine function. Few studies have demonstrated that glyphosate exposure results in hyperglycemic and insulin resistance; but the molecular mechanism underlying the diabetogenic potential of glyphosate on skeletal muscle, a primary organ that includes insulin-mediated glucose disposal, is unknown. In this study, we aimed to evaluate the impact of glyphosate on the detrimental changes in the insulin metabolic signaling in the gastrocnemius muscle. In vivo results showed that glyphosate exposure caused hyperglycemia, dyslipidemia, increased glycosylated hemoglobin (HbA1c), liver function, kidney function profile, and oxidative stress markers in a dose-dependent fashion. Conversely, hemoglobin and antioxidant enzymes were significantly reduced in glyphosate-induced animals indicating its toxicity is linked to induce insulin resistance. The histopathology of the gastrocnemius muscle and RT-PCR analysis of insulin signaling molecules revealed glyphosate-induced alteration in the expression of IR, IRS-1, PI3K, Akt, β-arrestin-2, and GLUT4 mRNA. Lastly, molecular docking and dynamics simulations confirmed that glyphosate showed a high binding affinity with target molecules such as Akt, IRS-1, c-Src, β-arrestin-2, PI3K, and GLUT4. The current work provides experimental proof that glyphosate exposure has a deleterious effect on the IRS-1/PI3K/Akt signaling pathways, which in turn causes the skeletal muscle to become insulin resistant and eventually develop type 2 diabetes mellitus.

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.

Intrinsically disordered proteins in viral pathogenesis and infections

Disordered proteins serve a crucial part in many biological processes that go beyond the capabilities of ordered proteins. A large number of virus-encoded proteins have extremely condensed proteomes and genomes, which results in highly disordered proteins. The presence of these IDPs allows them to rapidly adapt to changes in their biological environment and play a significant role in viral replication and down-regulation of host defense mechanisms. Since viruses undergo rapid evolution and have a high rate of mutation and accumulation in their proteome, IDPs' insights into viruses are critical for understanding how viruses hijack cells and cause disease. There are many conformational changes that IDPs can adopt in order to interact with different protein partners and thus stabilize the particular fold and withstand high mutation rates. This chapter explains the molecular mechanism behind viral IDPs, as well as the significance of recent research in the field of IDPs, with the goal of gaining a deeper comprehension of the essential roles and functions played by viral proteins.

The Conserved Macrodomain Is a Potential Therapeutic Target for Coronaviruses and Alphaviruses

Emerging and re-emerging viral diseases pose continuous public health threats, and effective control requires a combination of non-pharmacologic interventions, treatment with antivirals, and prevention with vaccines. The COVID-19 pandemic has demonstrated that the world was least prepared to provide effective treatments. This lack of preparedness has been due, in large part, to a lack of investment in developing a diverse portfolio of antiviral agents, particularly those ready to combat viruses of pandemic potential. Here, we focus on a drug target called macrodomain that is critical for the replication and pathogenesis of alphaviruses and coronaviruses. Some mutations in alphavirus and coronaviral macrodomains are not tolerated for virus replication. In addition, the coronavirus macrodomain suppresses host interferon responses. Therefore, macrodomain inhibitors have the potential to block virus replication and restore the host’s protective interferon response. Viral macrodomains offer an attractive antiviral target for developing direct acting antivirals because they are highly conserved and have a structurally well-defined (druggable) binding pocket. Given that this target is distinct from the existing RNA polymerase and protease targets, a macrodomain inhibitor may complement current approaches, pre-empt the threat of resistance and offer opportunities to develop combination therapies for combating COVID-19 and future viral threats.

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.

Structural Understanding of SARS-CoV-2 Drug Targets, Active Site Contour Map Analysis and COVID-19 Therapeutics

The most iconic word of the year 2020 is 'COVID-19', the shortened name for coronavirus disease 2019. The pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is responsible for multiple worldwide lockdowns, an economic crisis, and a substantial increase in hospitalizations for viral pneumonia along with respiratory failure and multiorgan dysfunctions. Recently, the first few vaccines were approved by World Health Organization (WHO) and can eventually save millions of lives. Even though, few emergency use drugs like Remdesivir and several other repurposed drugs, still there is no approved drug for COVID-19. The coronaviral encoded proteins involved in host-cell entry, replication, and host-cell invading mechanism are potentially therapeutic targets. This perspective review provides the molecular overview of SARS-CoV-2 life cycle for summarizing potential drug targets, structural insights, active site contour map analyses of those selected SARS-CoV-2 protein targets for drug discovery, immunology, and pathogenesis.

Comprehensive analysis of SARS-COV-2 drug targets and pharmacological aspects in treating the COVID-19

Corona viruses are enveloped, single-stranded RNA (Ribonucleic acid) viruses and they cause pandemic diseases having a devastating effect on both human healthcare and the global economy. To date, six corona viruses have been identified as pathogenic organisms which are significantly responsible for the infection and also cause severe respiratory diseases. Among them, the novel SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) caused a major outbreak of corona virus diseases 2019 (COVID-19). Coronaviridae family members can affects both humans and animals. In human, corona viruses cause severe acute respiratory syndrome with mild to severe outcomes. Several structural and genomics have been investigated, and the genome encodes about 28 proteins most of them with unknown function though it shares remarkable sequence identity with other proteins. There is no potent and licensed vaccine against SARS-CoV-2 and several trials are underway to investigate the possible therapeutic agents against viral infection. However, some of the antiviral drugs that have been investigated against SARS-CoV-2 are under clinical trials. In the current review we comparatively emphasize the emergence and pathogenicity of the SARS-CoV-2 and their infection and discuss the various putative drug targets of both viral and host receptors for developing effective vaccines and therapeutic combinations to overcome the viral outbreak.

Interrogation of Bacillus anthracis SrtA active site loop forming open/close lid conformations through extensive MD simulations for understanding binding selectivity of SrtA inhibitors

Bacillus anthracis is a gram positive, deadly spore forming bacteria causing anthrax and these bacteria having the complex mechanism in the cell wall envelope, which can adopt the changes in environmental conditions. In this, the membrane bound cell wall proteins are said to progressive drug target for the inhibition of Bacillus anthracis. Among the cell wall proteins, the SrtA is one of the important mechanistic protein, which mediate the ligation with LPXTG motif by forming the amide bonds. The SrtA plays the vital role in cell signalling, cell wall formation, and biofilm formations. Inhibition of SrtA leads to rupture of the cell wall and biofilm formation, and that leads to inhibition of Bacillus anthracis and thus, SrtA is core important enzyme to study the inhibition mechanism. In this study, we have examined 28 compounds, which have the inhibitory activity against the Bacillus anthracis SrtA for developing the 3D-QSAR and also, compounds binding selectivity with both open and closed SrtA conformations, obtained from 100 ns of MD simulations. The binding site loop deviate in forming the open and closed gate mechanism is investigated to understand the inhibitory profile of reported compounds, and results show the closed state active site conformations are required for ligand binding specificity. Overall, the present study may offer an opportunity for better understanding of the mechanism of action and can be aided to further designing of a novel and highly potent SrtA inhibitors.

Structure insights of SARS-CoV-2 open state envelope protein and inhibiting through active phytochemical of ayurvedic medicinal plants from Withania somnifera

Coronaviruses have been causing pandemic situations across the globe for the past two decades and the focus is on identifying suitable novel targets for antivirals and vaccine development. SARS-CoV-2 encodes a small hydrophobic envelope (E) protein that mediates envelope formation, budding, replication, and release of progeny viruses from the host. Through this study, the SARS-CoV-2 E protein is studied for its open and closed state and focused in identifying antiviral herbs used in traditional medicine practices for COVID-19 infections. In this study using computational tools, we docked the shortlisted phytochemicals with the envelope protein of the SARS-CoV-2 virus and the results hint that these compounds interact with the pore-lining residues. The molecular level understanding of the open state is considered and the active inhibitors from the phytochemicals of Ayurvedic medicinal plants from Withania somnifera. We have thus identified a potential phytochemical compound that directly binds with the pore region of the E protein and thereby blocks its channel activity. Blocking the ion channel activity of E protein is directly related to the inhibition of virus replication. The study shows encouraging results on the usage of these phytochemicals in the treatment/management of SARS-CoV-2 infection.

Microsecond MD Simulation and Multiple-Conformation Virtual Screening to Identify Potential Anti-COVID-19 Inhibitors Against SARS-CoV-2 Main Protease

The recent pandemic outbreak of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), raised global health and economic concerns. Phylogenetically, SARS-CoV-2 is closely related to SARS-CoV, and both encode the enzyme main protease (M^pro/3CL^pro), which can be a potential target inhibiting viral replication. Through this work, we have compiled the structural aspects of M^pro conformational changes, with molecular modeling and 1-μs MD simulations. Long-scale MD simulation resolves the mechanism role of crucial amino acids involved in protein stability, followed by ensemble docking which provides potential compounds from the Traditional Chinese Medicine (TCM) database. These lead compounds directly interact with active site residues (His41, Gly143, and Cys145) of M^pro, which plays a crucial role in the enzymatic activity. Through the binding mode analysis in the S1, S1′, S2, and S4 binding subsites, screened compounds may be functional for the distortion of the oxyanion hole in the reaction mechanism, and it may lead to the inhibition of M^pro in SARS-CoV-2. The hit compounds are naturally occurring compounds; they provide a sustainable and readily available option for medical treatment in humans infected by SARS-CoV-2. Henceforth, extensive analysis through molecular modeling approaches explained that the proposed molecules might be promising SARS-CoV-2 inhibitors for the inhibition of COVID-19, subjected to experimental validation.

Noise robust automatic heartbeat classification system using support vector machine and conditional spectral moment

Heartbeat classification is central to the detection of the arrhythmia. For the effective heartbeat classification, the noise-robust features are very significant. In this work, we have proposed a noise-robust support vector machine (SVM) based heartbeat classifier. The proposed classifier utilizes a novel noise-robust morphological feature which is based on the conditional spectral moment (CSM) of the heartbeat. In addition to the proposed CSM feature, we have also employed the existing RR interval, the wavelets, and the higher-order statistics (HOS) based temporal and morphological feature sets. The noise-robustness test of the proposed CSM and all the studied feature sets is performed for the SVM based heartbeat classifier. Further, we have studied the significance of combining these temporal and morphological features on the final classification performance. For this purpose, the individual SVMs were trained for each of the feature set. The final classification is based on the ensemble of these individual SVMs. Various combining scheme such as sum, majority, and product rules are employed to ensemble the result of the individually trained SVMs. The experimental results show the noise-robustness of the proposed CSM feature. The proposed classifier gives improved overall performance compared to the existing heartbeat classification systems.