Electrochemical Biosensor for the Determination of Specific Antibodies against SARS-CoV-2 Spike Protein.

In this article, we report the development of an electrochemical biosensor for the determination of the SARS-CoV-2 spike protein (rS). A gold disc electrode was electrochemically modified to form the nanocrystalline gold structure on the surface. Then, it was further altered by a self-assembling monolayer based on a mixture of two alkane thiols: 11-mercaptoundecanoic acid (11-MUA) and 6-mercapto-1-hexanol (6-MCOH) (SAMmix). After activating carboxyl groups using a N-(3-dimethylaminopropyl)-N'-ethyl-carbodiimide hydrochloride and N-hydroxysuccinimide mixture, the rS protein was covalently immobilized on the top of the SAMmix. This electrode was used to design an electrochemical sensor suitable for determining antibodies against the SARS-CoV-2 rS protein (anti-rS). We assessed the association between the immobilized rS protein and the anti-rS antibody present in the blood serum of a SARS-CoV-2 infected person using three electrochemical methods: cyclic voltammetry, differential pulse voltammetry, and potential pulsed amperometry. The results demonstrated that differential pulse voltammetry and potential pulsed amperometry measurements displayed similar sensitivity. In contrast, the measurements performed by cyclic voltammetry suggest that this method is the most sensitive out of the three methods applied in this research.

Repurposing Thalidomide, Its Analogue And Apremilast For Possible Antiviral In Situation Of Severe Covid Cytokine Syndrome.

BACKGROUND: COVID-19, caused by SARS-corona virus-2, is a global wide expanded public health risk at a bizarre level. In this current situation, COVID-19 became a serious emerging pandemic. Choosing drug reusing is a crucial step in identifying the new uses of old established drugs. To achieve a significant and healthy way of treatment in COVID patients within a short duration, drug repurposing is a novel method. OBJECTIVE: The present study concentrated on the molecular docking of thalidomide and its analogues and Apremilast against Coronavirus infectious symptoms, evaluated on virus proteins (Spike Protein, 3cl Protease, Nucleocapsids). METHODS: The present study explores the possibility of repurposing thalidomide for the treatment of SARS-COV-2 infection by assessing and confirming with docking affinity scores of thalidomide & its analogues and Apremilast, with spike protein, 3cl protease, and nucleocapsids. RESULTS: From the study results, thalidomide, pomalidomide, lenalidomide, and Apremilast exhibited better binding affinity to N Protein (4KXJ), Protease (4WY3) and Spike Protein (5WRG). In comparison of targets, N Protein - 4KXJ is the best for the four ligands. It is finalized that all four ligands (Thalidomide - -8.6, Pomalidomide - -8.8, Lenalidomide,and - -8.2,and Apremilast - -8.1) have good docking scores with the target N Protein. CONCLUSION: The present study shows confirmation that thalidomide and its analogues and apremilast as a better fit for treating high risk patients of COVID -19 viral infection which are supposed to promote beneficial effects for both respiratory illnesses like COVID-19 symptoms as well as improve the pathological state of condition.

Molecularly imprinted polypyrrole based sensor for the detection of SARS-CoV-2 spike glycoprotein.

This study describes the application of a polypyrrole-based sensor for the determination of SARS-CoV-2-S spike glycoprotein. The SARS-CoV-2-S spike glycoprotein is a spike protein of the coronavirus SARS-CoV-2 that recently caused the worldwide spread of COVID-19 disease. This study is dedicated to the development of an electrochemical determination method based on the application of molecularly imprinted polymer technology. The electrochemical sensor was designed by molecular imprinting of polypyrrole (Ppy) with SARS-CoV-2-S spike glycoprotein (MIP-Ppy). The electrochemical sensors with MIP-Ppy and with polypyrrole without imprints (NIP-Ppy) layers were electrochemically deposited on a platinum electrode surface by a sequence of potential pulses. The performance of polymer layers was evaluated by pulsed amperometric detection. According to the obtained results, a sensor based on MIP-Ppy is more sensitive to the SARS-CoV-2-S spike glycoprotein than a sensor based on NIP-Ppy. Also, the results demonstrate that the MIP-Ppy layer is more selectively interacting with SARS-CoV-2-S glycoprotein than with bovine serum albumin. This proves that molecularly imprinted MIP-Ppy-based sensors can be applied for the detection of SARS-CoV-2 virus proteins.

In silico Drug Repurposing for the Identification of Antimalarial Drugs as Candidate Inhibitors of SARS-CoV-2

Background: Coronavirus disease (COVID-19) is a severe acute respiratory condition that has affected millions of people worldwide, indicating a global health emergency. Despite the deteriorating trends of COVID-19, no drugs are validated to have substantial efficacy in the potential treatment of COVID-19 patients in large-scale trials. Methods: This study aimed at identifying potential antimalarial candidate molecules for the treatment of COVID and evaluating the possible mechanism of action by in silico screening method. In silico screening studies on various antimalarial compounds, like amodiaquine, chloroquine, hydroxychloroquine, mefloquine, primaquine, and atovaquone, were conducted using PyRx and AutoDoc 1.5.6 tools against ACE 2 receptor, 3CL protease, hemagglutinin esterase, spike protein of SARS HR1 motif, and papain-like protease virus proteins. Results: Based on PyRx results, mefloquine and atovaquone were found to have higher docking affinity scores against virus proteins compared to other antimalarial compounds. Screening report of atovaquone exhibited affirmative inhibition constant for spike protein of SARS HR1 motif, 3CL protease, and papain-like protease. Conclusion: In silico analysis reported atovaquone as a promising candidate for COVID 19 therapy.

Improvement of SARS-CoV-2 macromolecule conformation by algorithmic structural prediction

A fast way to reconstruct the three-dimensional molecular conformation of SARS-CoV-2 virus proteins is addressed in this article, involving the most worrying variant discovered in patients from Brazil, the lineage B.1.1.28/P.1. The proposed methodology is based on the sequencing of virus proteins and that, through the incorporation of mutations in silico, which are then computationally reconstructed using an enumerative feasibility algorithm validated by the Ramachandran diagram and structural alignment, in addition to the subsequent study of structural stability through classical molecular dynamics. From the resulting structure to the ACE2-RBD complex, the valid solution presented 97.06% of the residues in the most favorable region while the reference crystallographic structure presented 95.0%, a difference therefore very small and revealing the great consistency of the developed algorithm. Another important result was the low RMSD alignment between the best solution by the BP algorithm and the reference structure, where we obtained 0.483Å. Finally, the molecular dynamics indicated greater structural stability in the ACE2-RBD interaction with the P.1 strain, which could be a plausible explanation for convergent evolution that provides an increase in the interaction affinity with the ACE2 receptor. ©2021 IEEE

The Role of Justicia adhatoda as Prophylaxis for COVID-19 - Analysis Based on Docking.

Coronavirus causes severe harm to the health of both humans as well as animals, creating a major global health problem affecting millions of populations. Considering the situational emergency of identifying novel targeted therapy, we have chosen the herbal compound Adhatoda Justicia/ vasica, which is a high potent olden vital compound having a key role in various respiratory conditions with multiple beneficial uses. Adhatoda is promoted and supported by the Ministry of AYUSH for symptomatic management of respiratory ailments in the case of COVID 19. In this study, we focused on the efficacy of Adathoda primary active alkaloid, vasicine against coronavirus infectious symptoms, evaluated by in silico screening studies on virus proteins ACE 2 Receptor, 3CL protease and Spike protein SARS HR1 motif using PyRx tool and AutoDoc 1.5.6. Based on PyRx results, Vasicine with ACE 2 Receptor showed a higher docking affinity score -7.1 K/cal respectively when compared to other virus proteins. AutoDoc 1.5.6 screening study report showed that vasicine promotes good inhibitory constant 486.54 mM on 3CL protease more than others. Results reveal that vasicine could be a potential target for the treatment of COVID 19. This study adds strong evidence to the claim by the advisory released by AYUSH. Based on the results with the available literature, Adhatoda could be a drug helpful in relieving symptoms in non-- COVID cases in those who were quarantined or in lockdown pace, thereby reducing pandemic panic in confirmed asymptomatic or mild cases. For usage in moderate to severe cases, this could be an add-on therapy with existing modern medical therapy.

Identification of Novel Protein Sequencing SARS CoV-2 Coronavirus Using Machine Learning

The World Health Organization (WHO) declared Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection as a global pandemic in March 2020 causing COVID-19 (Coronavirus Disease-19). Till date, more than 173 million people have been infected worldwide, whereas more than 3.7 million deaths have already been reported caused by COVID-19. Protein-to-protein (PPI) interaction plays an important role in the cellular process of SARS-CoV-2 virus infection in the human body. Although the recent emergence of SARS-CoV-2 has prompted a push for deeper understanding of SARS-CoV-2 and development of effective treatment. However, understanding SARS-CoV-2 is even more critical. It was previously discovered that the proteome of the virus was known, and thus it was possible to derive some of the protein structures by experimentation and others by model-based prediction approaches. The results are later verified by experiments. Considerable research attention has been directed toward DEE deploy features extraction algorithm, amino acid composition AAC and pseudo amino acid composition PseAAC algorithms. We have proposed AdaBoost classification models and compared them with other two machine learning classifiers, such as K-Nearest Neighbor and Random Forest. This paper is not intended to be a comprehensive evaluation of AdaBoost, K-Nearest Neighbor, and Random Forest, rather we have used these models to create an ensemble classifier with excellent performance metrics such as accuracy, precision, specificity, recall, and F1 score. Based on the ensemble model, 1326 total human target proteins are predicted to be potential SARS-CoV-2 viral proteins.

Understanding Individual SARS-CoV-2 Proteins for Targeted Drug Development against COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the COVID-19 pandemic, responsible for millions of deaths globally. Even with effective vaccines, SARS-CoV-2 will likely maintain a hold in the human population through gaps in efficacy, percent vaccinated, and arising new strains. Therefore, understanding how SARS-CoV-2 causes widespread tissue damage and the development of targeted pharmacological treatments will be critical in fighting this virus and preparing for future outbreaks. Herein, we summarize the progress made thus far by using in vitro or in vivo models to investigate individual SARS-CoV-2 proteins and their pathogenic mechanisms. We have grouped the SARS-CoV-2 proteins into three categories: host entry, self-acting, and host interacting. This review focuses on the self-acting and host-interacting SARS-CoV-2 proteins and summarizes current knowledge on how these proteins promote virus replication and disrupt host systems, as well as drugs that target the virus and virus interacting host proteins. Encouragingly, many of these drugs are currently in clinical trials for the treatment of COVID-19. Future coronavirus outbreaks will most likely be caused by new virus strains that evade vaccine protection through mutations in entry proteins. Therefore, study of individual self-acting and host-interacting SARS-CoV-2 proteins for targeted therapeutic interventions is not only essential for fighting COVID-19 but also valuable against future coronavirus outbreaks.

Tuning intrinsic disorder predictors for virus proteins.

Many virus-encoded proteins have intrinsically disordered regions that lack a stable, folded three-dimensional structure. These disordered proteins often play important functional roles in virus replication, such as down-regulating host defense mechanisms. With the widespread availability of next-generation sequencing, the number of new virus genomes with predicted open reading frames is rapidly outpacing our capacity for directly characterizing protein structures through crystallography. Hence, computational methods for structural prediction play an important role. A large number of predictors focus on the problem of classifying residues into ordered and disordered regions, and these methods tend to be validated on a diverse training set of proteins from eukaryotes, prokaryotes, and viruses. In this study, we investigate whether some predictors outperform others in the context of virus proteins and compared our findings with data from non-viral proteins. We evaluate the prediction accuracy of 21 methods, many of which are only available as web applications, on a curated set of 126 proteins encoded by viruses. Furthermore, we apply a random forest classifier to these predictor outputs. Based on cross-validation experiments, this ensemble approach confers a substantial improvement in accuracy, e.g., a mean 36 per cent gain in Matthews correlation coefficient. Lastly, we apply the random forest predictor to severe acute respiratory syndrome coronavirus 2 ORF6, an accessory gene that encodes a short (61 AA) and moderately disordered protein that inhibits the host innate immune response. We show that disorder prediction methods perform differently for viral and non-viral proteins, and that an ensemble approach can yield more robust and accurate predictions.