Mutational analysis in international isolates and drug repurposing against SARS-CoV-2 spike protein: molecular docking and simulation approach

An in-silico investigation and network pharmacology based approach to explore the anti-breast-cancer potential of Tecteria coadunata (Wall.) C. Chr

Uncontrolled cell proliferation is a common definition of cancer. After lung carcinoma, breast neoplasm is the second-most prevalent kind of cancer. The majority of breast cancer cells and healthy breast cells both have receptors for circulating oestrogen and progesterone. In order to promote the development and division of cancer cells, oestrogen and progesterone bind to the receptors and may collaborate with growth factors (such as oncogenes and mutant tumour suppressor genes). As per the literature, Tecteria coadunata (Wall.) C. Chr. has anticancer, antioxidant and anti-inflammatory potential. After the hydroalcoholic extraction of this rhizome, total of 200 phytochemicals were retrieved from HR-LCMS analysis. In this current study, Network pharmacology was carried out to explore the rationale of Tecteria coadunata (Wall.) C. Chr. by using different database using Cytoscape software. The network depicted the interaction of Bioactives with their targets and their association with several disease, especially breast cancer. Tecteria coadunata (Wall.) C. Chr. has offered new relationship with variety of genes and its applications in different types of breast cancers. Further Gene Ontology was carried out and it showed key targets were TP53, BRCA2, PGR and CHEK 2. Further Signalling pathways were also enriched. Flex-X software was used for molecular docking studies, and it verified that Dopaxanthin, Dantrolene and Orotidin shows the highest binding affinities with key targets. Additionally, Pharmacokinetic analysis revealed that all top three lead compounds which follows the Lipinski Rule (Rule of three) without interrupting the conditions of bioavailability with minimal toxicity.Communicated by Ramaswamy H. Sarma.

DRaW: prediction of COVID-19 antivirals by deep learning-an objection on using matrix factorization

BACKGROUND

Due to the high resource consumption of introducing a new drug, drug repurposing plays an essential role in drug discovery. To do this, researchers examine the current drug-target interaction (DTI) to predict new interactions for the approved drugs. Matrix factorization methods have much attention and utilization in DTIs. However, they suffer from some drawbacks.

METHODS

We explain why matrix factorization is not the best for DTI prediction. Then, we propose a deep learning model (DRaW) to predict DTIs without having input data leakage. We compare our model with several matrix factorization methods and a deep model on three COVID-19 datasets. In addition, to ensure the validation of DRaW, we evaluate it on benchmark datasets. Furthermore, as an external validation, we conduct a docking study on the COVID-19 recommended drugs.

RESULTS

In all cases, the results confirm that DRaW outperforms matrix factorization and deep models. The docking results approve the top-ranked recommended drugs for COVID-19.

CONCLUSIONS

In this paper, we show that it may not be the best choice to use matrix factorization in the DTI prediction. Matrix factorization methods suffer from some intrinsic issues, e.g., sparsity in the domain of bioinformatics applications and fixed-unchanged size of the matrix-related paradigm. Therefore, we propose an alternative method (DRaW) that uses feature vectors rather than matrix factorization and demonstrates better performance than other famous methods on three COVID-19 and four benchmark datasets.

Genomic landscape of the emerging XDR Salmonella Typhi for mining druggable targets clpP, hisH, folP and gpmI and screening of novel TCM inhibitors, molecular docking and simulation analyses

Typhoid fever is transmitted by ingestion of polluted water, contaminated food, and stool of typhoid-infected individuals, mostly in developing countries with poor hygienic environments. To find novel therapeutic targets and inhibitors, We employed a subtractive genomics strategy towards Salmonella Typhi and the complete genomes of eight strains were primarily subjected to the EDGAR tool to predict the core genome (n = 3207). Human non-homology (n = 2450) was followed by essential genes identification (n = 37). The STRING database predicted maximum protein-protein interactions, followed by cellular localization. The virulent/immunogenic ability of predicted genes were checked to differentiate drug and vaccine targets. Furthermore, the 3D models of the identified putative proteins encoded by the respective genes were constructed and subjected to druggability analyses where only "highly druggable" proteins were selected for molecular docking and simulation analyses. The putative targets ATP-dependent CLP protease proteolytic subunit, Imidazole glycerol phosphate synthase hisH, 7,8-dihydropteroate synthase folP and 2,3-bisphosphoglycerate-independent phosphoglycerate mutase gpmI were screened against a drug-like library (n = 12,000) and top hits were selected based on H-bonds, RMSD and energy scores. Finally, the ADMET properties for novel inhibitors ZINC19340748, ZINC09319798, ZINC00494142, ZINC32918650 were optimized followed by binding free energy (MM/PBSA) calculation for ligand-receptor complexes. The findings of this work are expected to aid in expediting the identification of novel protein targets and inhibitors in combating typhoid Salmonellosis, in addition to the already existing therapies.

In silico Screening of Potential SARS-CoV-2 Main Protease Inhibitors from Thymus schimperi

Background

COVID-19 is still instigating significant social and economic chaos worldwide; however, there is no approved antiviral drug yet. Here, we used in silico analysis to screen potential SARS-CoV-2 main protease (M^pro) inhibitors extracted from the essential oil of Thymus schimperi which could contribute to the discovery of potent anti-SARS-CoV-2 phytochemicals.

Methods

The absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of compounds were determined through SwissADME and ProToxII servers. AutoDock tools were used for molecular docking analysis studies, while Chimera, DS studio, and LigPlot were used for post-docking studies. Molecular dynamic simulations were performed for 200 ns under constant pressure.

Results

All compounds exhibited a bioavailability score of ≥0.55 entailing that at least 55% of the drugs can be absorbed unchanged. Only five (9%), nine (16%) and two (3.6%) of the compounds showed active hepatotoxicity, carcinogenicity, and immunotoxicity, respectively. Except for flourazophore P, which showed a little mutagenicity, all other compounds did not show mutagenic properties. On the other hand, only pinene beta was found to have a little cytotoxicity. Five compounds demonstrated effective binding to the catalytic dyad of the SARS-CoV-2 M^pro substrate binding pocket, while two of them (geranylisobutanoate and 3-octane) are found to be the best hits that formed hydrogen bonds with Glu¹⁶⁶ and Ser¹⁴⁴ of SARS-CoV-2 M^pro.

Conclusion

Based on our in silico analysis, top hits from Thymus schimperi may serve as potential anti-SARS-CoV-2 compounds. Further in vitro and in vivo studies are recommended to characterize these compounds for clinical applications.

In silico analysis of the substitution mutations and evolutionary trends of the SARS-CoV-2 structural proteins in Asia

OBJECTIVES

To address a highly mutable pathogen, mutations must be evaluated. SARS-CoV-2 involves changing infectivity, mortality, and treatment and vaccination susceptibility resulting from mutations.

MATERIALS AND METHODS

We investigated the Asian and worldwide samples of amino-acid sequences (AASs) for envelope (E), membrane (M), nucleocapsid (N), and spike (S) proteins from the announcement of the new coronavirus 2019 (COVID-19) up to January 2022. Sequence alignment to the Wuhan-2019 virus permits tracking mutations in Asian and global samples. Furthermore, we explored the evolutionary tendencies of structural protein mutations and compared the results between Asia and the globe.

RESULTS

The mutation analyses indicated that 5.81%, 70.63%, 26.59%, and 3.36% of Asian S, E, M, and N samples did not display any mutation. Additionally, the most relative mutations among the S, E, M, and N AASs occurred in the regions of 508 to 635 AA, 7 to 14 AA, 66 to 88 AA, and 164 to 205 AA in both Asian and total samples. D614G, T9I, I82T, and R203M were inferred as the most frequent mutations in S, E, M, and N AASs. Timeline research showed that substitution mutation in the location of 614 among Asian and total S AASs was detected from January 2020.

CONCLUSION

N protein was the most non-conserved protein, and the most prevalent mutations in S, E, M, and N AASs were D614G, T9I, I82T, and R203M. Screening structural protein mutations is a robust approach for developing drugs, vaccines, and more specific diagnostic tools.

Spike protein of SARS-CoV-2 variants: a brief review and practical implications

The scientific community has been alarmed by the possible immunological evasion, higher infectivity, and severity of disease caused by the newest variants of SARS-CoV-2. The spike protein has an important role in the cellular invasion of viruses and is the target of several vaccines and therapeutic resources, such as monoclonal antibodies. In addition, some of the most relevant mutations in the different variants are on the spike (S) protein gene sequence that leads to structural alterations in the predicted protein, thus causing concern about the protection mediated by vaccines against these new strains. The present review highlights the most recent knowledge about COVID-19 and vaccines, emphasizing the different spike protein structures of SARS-CoV-2 and updating the reader about the emerging viral variants and their classifications, the more common viral mutations described and their distribution in Brazil. It also compiles a table with the most recent knowledge about all of the Omicron spike mutations.

In silico Drug Repurposing of Anticancer Drug 5-FU and Analogues Against SARS-CoV-2 Main Protease: Molecular Docking, Molecular Dynamics Simulation, Pharmacokinetics and Chemical Reactivity Studies

Background

Since the last COVID-19 outbreak, several approaches have been given a try to quickly tackle this global calamity. One of the well-established strategies is the drug repurposing, which consists in finding new therapeutic uses for approved drugs. Following the same paradigm, we report in the present study, an investigation of the potential inhibitory activity of 5-FU and nineteen of its analogues against the SARS-CoV-2 main protease (3CLpro).

Material and Methods

Molecular docking calculations were performed to investigate the binding affinity of the ligands within the active site of 3CLpro. The best binding candidates were further considered for molecular dynamics simulations for 100 ns to gain a time-resolved understanding of the behavior of the guest-host complexes. Furthermore, the profile of druggability of the best binding ligands was assessed based on ADMET predictions. Finally, their chemical reactivity was elucidated using different reactivity descriptors, namely the molecular electrostatic potential (MEP), Fukui functions and frontier molecular orbitals.

Results and Discussion

From the calculations performed, four candidates (compounds 14, 15, 16 and 18) show promising results with respect to the binding affinity to the target protease, 3CLpro, the therapeutic profile of druggability and safety. These compounds are maintained inside the active site of 3CLpro thanks to a variety of noncovalent interactions, especially hydrogen bonds, involving important amino acids such as GLU166, HIS163, GLY143, ASN142, HIS172, CYS145. Molecular dynamics simulations suggest that the four ligands are well trapped within the active site of the protein over a time gap of 100 ns, ligand 18 being the most retained.

Conclusion

In line with the findings reported herein, we recommend that further in-vitro and in-vivo investigations are carried out to shed light on the possible mechanism of pharmacological action of the proposed ligands.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Can polyoxometalates (POMs) prevent of coronavirus 2019-nCoV cell entry? Interaction of POMs with TMPRSS2 and spike receptor domain complexed with ACE2 (ACE2-RBD): Virtual screening approaches

The unexpected appearance and global spread of COVID-19 create significant difficulties for healthcare systems and present an unusual challenge for the fast discovery of medicines to combat this fatal disease. Screening metallodrugs libraries from the medicinal inorganic chemistry society may expand the studied ‘chemical space’ and improve the probability of discovering effective anti-COVID drugs, including polyoxometalates. POMs are an oxygen-rich family of inorganic cluster systems that have previously been tested for antiviral action against different types of viruses. Human angiotensin-converting enzyme 2 (ACE2), human transmembrane protease serine 2 (TMPRSS2), and the SARS-CoV-2 spike glycoprotein are required for host cell-mediated viral entrance. Targeting these proteins demonstrates potential possibilities for preventing infections and transmissions in the initial stage. As a result, POMs with known antiviral effects were investigated for this purpose using molecular docking and dynamic simulations. This research shows that POMs can prevent SARS CoV-2 from entering cells by blocking TMPRSS2, which SARS-CoV-2 uses for spike glycoprotein priming. They may also engage with ACE2 and the spike glycoprotein and disrupt their binding by blocking the active sites. We think that a thorough investigation of POMs as possible anti-COVID-19 drugs will provide significant opportunities.

COVID-19: comprehensive review on mutations and current vaccines

Viral outbreaks had been a threat for the human race for a long time. Several epidemics and pandemics have been reported in the past with serious consequences on human health and subsequent social and economic aspects. According to WHO, viral infections continue to be a major health concern globally. Novel coronavirus, SARS-CoV-2 (Severe acute respiratory syndrome coronavirus-2) causes the most recent infectious pandemic disease, COVID-19 (Coronavirus disease-19). As of now, there were 249 million infections of COVID-19 worldwide with a high mortality of more than 5 million deaths reported; and the number of new additional cases is drastically increasing. Development of therapies to treat the infected cases and prophylactic agents including vaccines that are effective towards different variants are crucial to curtail the COVID-19 pandemic. Owing to the fact that there is a high mortality and morbidity rate along with the risk of virus causing further epidemic outbursts, development of additional effective therapeutic and preventive strategies are highly warranted. Prevention, early detection and treatment will reduce the spread of COVID-19 pandemic. The present review highlights the novel mutations and therapeutic updates associated with coronaviruses along with the clinical manifestations-diagnosis, clinical management and, prophylactic and therapeutic strategies of COVID-19 infection.