Can SARS-CoV-2 Accumulate Mutations in the S-Protein to Increase Pathogenicity?

The evaluation of SARS-CoV-2 mutations at the early stage of the pandemic in Istanbul population

BACKGROUND

Determination of SARS-CoV-2 variant is significant to prevent the spreads of COVID-19 disease.

METHODS

We aimed to evaluate the variants of SARS-CoV-2 rate in positive patients in Kanuni Sultan Suleyman Training and Research Hospital (KSS-TRH), Istanbul, Türkiye between 1st January and 30th November 2021 by using RT-PCR method.

RESULTS

Herein, 825,169 patients were evaluated (male:58.53% and female:41.47%) whether COVID-19 positive or not [( +):21.3% and (-):78.7%] and 175,367 patient was described as positive (53.2%-female and 46.8%-male) by RT-PCR. COVID-19 positive rate is observed highest in the 6-15- and 66-75-year age range. The frequencies were obtained as SARS-CoV-2 positive (without mutation of B.1.1.7 [B.1.1.7 (U.K), E484K, L452R, B.1.351 (S. Africa/Brazil) spike mutations] as 66.1% (n: 115,899), B.1.1.7 Variant as 23.2% (n:40,686), Delta mutation (L452R) variant as 9.8% (n:17,182), B.1.351 variant as 0.8% (n:1370) and E484K as 0.1% (n: 230). In April 2021, general SARS-CoV-2 and B.1.1.7 variant were dominantly observed. Up to July 2021, B.1.617.2 (Delta variant/ Indian variant) and E484K has been not observed. B.1.351 variant of SARS-CoV-2 has been started in February 2021 at the rarest ratio and March 2021 is the top point. September 2021 is the pick point of E484K. African/Brazil variant of SARS-CoV-2 has been started in February 2021 at the rarest ratio and March 2021 is the top point. September 2021 is the pick point of E484K. When the gender type is compared within the variants, women were found to be more prevalent in all varieties.

CONCLUSIONS

The meaning of these mutations is very important to understand the transmission capacity of the COVID-19 disease, pandemic episode, and diagnosis of the virus with mutation types. Understanding the variant type is important for monitoring herd immunity and the spread of the disease.

Intolerance of uncertainty and internet addiction among college students in China post-pandemic era: the mediating role of future anxiety

Due to the repeated changes in the COVID-19 pandemic, we live in an era of various uncertainties that raise future anxiety and behavioral addiction problems. According to the Protection Motivation Theory (PMT), the present study attempted to explore the impact of COVID-19 intolerance of uncertainty (COVID-19 IU) on internet addiction (IA) among college students and the mediating role of future anxiety (FA) by constructing a mediating model. A questionnaire survey was conducted on 679 Chinese college students and PROCESS 3.5 was utilized to test the hypotheses. The results indicated that the COVID-19 IU was significantly positively correlated with IA and FA, and FA was significantly positively correlated with IA. COVID-19 IU had a significant positive predictive effect on IA; FA played a complementary partial mediating role between COVID-19 IU and IA. The results supported the PMT, which not only enriched our understanding of FA under uncertain life circumstances, but also deepened our understanding of the potential mechanisms of the effects of IA. Finally, discussions and suggestions were presented based on the results.

Wuhan Sequence-Based Recombinant Antigens Expressed in E. coli Elicit Antibodies Capable of Binding with Omicron S-Protein

The development of cross-reactive vaccines is one of the central aims of modern vaccinology. Continuous mutation and the emergence of new SARS-CoV-2 variants and subvariants create the problem of universal coronavirus vaccine design. Previously, the authors devised three recombinant coronavirus antigens, which were based on the sequence collected in 2019 (the Wuhan variant) and produced in an E. coli bacterial expression system. The present work has shown, for the first time, that these recombinant antigens induce the production of antibodies that clearly interact with produced in CHO full-length S-protein of the Omicron variant. The immunogenicity of these recombinant antigens was studied in formulations with different adjuvants: Freund's adjuvant, Al(OH)3 and an adjuvant based on spherical particles (SPs), which are structurally modified plant virus. All adjuvanted formulations effectively stimulated Omicron-specific IgG production in mice. These universal coronavirus antigens could be considered the main component for the further development of broad-spectrum coronavirus vaccines for the prevention of SARS-CoV-2 infection. The present work also provides evidence that the synthetic biology approach is a promising strategy for the development of highly cross-reactive vaccines. Moreover, it is important to note that the bacterial expression system might be appropriate for the production of antigenically active universal antigens.

Unravelling the diagnostic methodologies for SARS-CoV-2; the Indispensable need for developing point-of-care testing

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-caused COVID-19 pandemic that continues to be a global menace and since its emergence in the late 2019, SARS-CoV-2 has been vigorously spreading throughout the globe putting the whole world into a multidimensional calamity. The suitable diagnosis strategies are on the front line of the battle against preventing the spread of infections. Since the clinical manifestation of COVID-19 is shared between various diseases, detection of the unique impacts of the pathogen on the host along with the diagnosis of the virus itself should be addressed. Employing the most suitable approaches to specifically, sensitively and effectively recognize the infected cases may be a real game changer in controlling the outbreak and the crisis management. In that matter, point-of-care assays (POC) appears to be the potential option, due to sensitivity, specificity, affordable, and availability. Here we brief the most recent findings about the virus, its variants, and the conventional methods that have been used for its detection, along with the POC strategies that have been applied to the virus diagnosis and the developing technologies which can accelerate the diagnosis procedure yet maintain its efficiency.

Equal Maintenance of Anti-SARS-CoV-2 Antibody Levels Induced by Heterologous and Homologous Regimens of the BNT162b2, ChAdOx1, CoronaVac and Ad26.COV2.S Vaccines: A Longitudinal Study Up to the 4th Dose of Booster

Several technological approaches have been used to develop vaccines against COVID-19, including those based on inactivated viruses, viral vectors, and mRNA. This study aimed to monitor the maintenance of anti-SARS-CoV-2 antibodies in individuals from Brazil according to the primary vaccination regimen, as follows: BNT162b2 (group 1; 22) and ChAdOx1 (group 2; 18). Everyone received BNT162b2 in the first booster while in the second booster CoronaVac, Ad26.COV2.S, or BNT162b2. Blood samples were collected from 2021 to 2023 to analyze specific RBD (ELISA) and neutralizing antibodies (PRNT50). We observed a progressive increase in anti-RBD and neutralizing antibodies in each subsequent dose, remaining at high titers until the end of follow-up. Group 1 had higher anti-RBD antibody titers than group 2 after beginning the primary regimen, with significant differences after the 2nd and 3rd doses. Group 2 showed a more expressive increase after the first booster with BNT162B2 (heterologous booster). Group 2 also presented high levels of neutralizing antibodies against the Gamma and Delta variants until five months after the second booster. In conclusion, the circulating levels of anti-RBD and neutralizing antibodies against the two variants of SARS-CoV-2 were durable even five months after the 4th dose, suggesting that periodic booster vaccinations (homologous or heterologous) induced long-lasting immunity.

Long-term monitoring of SARS-CoV-2 variants in wastewater using a coordinated workflow of droplet digital PCR and nanopore sequencing

Quantitative polymerase chain reaction (PCR) and genome sequencing are important methods for wastewater surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The reverse transcription-droplet digital PCR (RT-ddPCR) is a highly sensitive method for quantifying SARS-CoV-2 RNA in wastewater samples to track the trends of viral activity levels but cannot identify new variants. It also takes time to develop new PCR-based assays targeting variants of interest. Whole genome sequencing (WGS) can be used to monitor known and new SARS-CoV-2 variants, but it is generally not quantitative. Several short-read sequencing techniques can be expensive and might experience delayed turnaround times when outsourced due to inadequate in-house resources. Recently, a portable nanopore sequencing system offers an affordable and real-time method for sequencing SARS-CoV-2 variants in wastewater. This technology has the potential to enable swift response to disease outbreaks without relying on clinical sequencing results. In addressing concerns related to rapid turnaround time and accurate variant analysis, both RT-ddPCR and nanopore sequencing methods were employed to monitor the emergence of SARS-CoV-2 variants in wastewater. This surveillance was conducted at 23 sewer maintenance hole sites and five wastewater treatment plants in Michigan from 2020 to 2022. In 2020, the wastewater samples were dominated by the parental variants (20A, 20C and 20 G), followed by 20I (Alpha, B.1.1.7) in early 2021 and the Delta variant of concern (VOC) in late 2021. For the year 2022, Omicron variants dominated. Nanopore sequencing has the potential to validate suspected variant cases that were initially undetermined by RT-ddPCR assays. The concordance rate between nanopore sequencing and RT-ddPCR assays in identifying SARS-CoV-2 variants to the clade-level was 76.9%. Notably, instances of disagreement between the two methods were most prominent in the identification of the parental and Omicron variants. We also showed that sequencing wastewater samples with SARS-CoV-2 N gene concentrations of >104 GC/100 ml as measured by RT-ddPCR improve genome recovery and coverage depth using MinION device. RT-ddPCR was better at detecting key spike protein mutations A67V, del69-70, K417N, L452R, N501Y, N679K, and R408S (p-value <0.05) as compared to nanopore sequencing. It is suggested that RT-ddPCR and nanopore sequencing should be coordinated in wastewater surveillance where RT-ddPCR can be used as a preliminary quantification method and nanopore sequencing as the confirmatory method for the detection of variants or identification of new variants. The RT-ddPCR and nanopore sequencing methods reported here can be adopted as a reliable in-house analysis of SARS-CoV-2 in wastewater for rapid community level surveillance and public health response.

Proteomics-based mass spectrometry profiling of SARS-CoV-2 infection from human nasopharyngeal samples

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the on-going global pandemic of coronavirus disease 2019 (COVID-19) that continues to pose a significant threat to public health worldwide. SARS-CoV-2 encodes four structural proteins namely membrane, nucleocapsid, spike, and envelope proteins that play essential roles in viral entry, fusion, and attachment to the host cell. Extensively glycosylated spike protein efficiently binds to the host angiotensin-converting enzyme 2 initiating viral entry and pathogenesis. Reverse transcriptase polymerase chain reaction on nasopharyngeal swab is the preferred method of sample collection and viral detection because it is a rapid, specific, and high-throughput technique. Alternate strategies such as proteomics and glycoproteomics-based mass spectrometry enable a more detailed and holistic view of the viral proteins and host-pathogen interactions and help in detection of potential disease markers. In this review, we highlight the use of mass spectrometry methods to profile the SARS-CoV-2 proteome from clinical nasopharyngeal swab samples. We also highlight the necessity for a comprehensive glycoproteomics mapping of SARS-CoV-2 from biological complex matrices to identify potential COVID-19 markers.

Molecular detection and characterization of SARS-CoV-2 in wastewater in Thailand during 2020-2022

BACKGROUND

SARS-CoV-2 has been detected in feces of infected individuals and in wastewater in many countries, which indicates that wastewater may be used to monitor contamination of the virus in community. However, information about the presence of SARS-CoV-2 in different types of environmental water and their genetic characterization are still limited. The purpose of this study was to investigate the presence of SARS-CoV-2 contaminating in environmental water in Thailand.

METHODS

We collected 600 water samples from 10 different sampling sites in Chiang Mai city, Thailand twice a month from July 2020 to December 2022. The SARS-CoV-2 RNA was detected by real-time RT-PCR and further amplified for ORF1a and S genes to investigate their genetic relationship to the reference strains by phylogenetic analysis.

RESULTS

SARS-CoV-2 was detected at 0.17% in the wastewater sample collected in the vicinity of fresh market where the outbreak of COVID-19 cases were simultaneously reported. The detected SARS-CoV-2 strain (W323/21) had nucleotide and amino acid sequences identical to SARS-CoV-2 Delta variant. Amino acid sequence alignment of spike protein revealed that the W323/21 strain carried a mutation of D950N as it was demonstrated in Delta variant reference strains.

CONCLUSIONS

The findings indicated that SARS-CoV-2 Delta variant was detected in wastewater in Chiang Mai, Thailand during the outbreak of COVID-19, suggesting a circulation of the virus in environmental water and in the community during the outbreak.

Delineating the Structure-Dynamics-Binding Differences among BA.1, BA.4/5, and BF.7 SARS-CoV-2 Variants through Atomistic Simulations: Correlation with Structural and Epidemiological Features

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an RNA virus possessing a spike (S) protein that facilitates the entry of the virus into human cells. The emergence of highly transmissible and fit SARS-CoV-2 variants has been driven by the positive selection of mutations within the S-protein. Notable among these variants are alpha, beta, gamma, delta, and omicron (BA.1), with the latter contributing to significant global health challenges and impacting populations worldwide. Recently, a novel subvariant of BA.1, named BF.7, has surfaced, purportedly exhibiting elevated transmissibility and infectivity rates. In order to comprehend and compare the transmissibility and disease progression characteristics of distinct SARS-CoV-2 variants, we performed an extensive comparative analysis utilizing all-atom molecular dynamics (MD) simulations (in triplicate) to investigate the structural, dynamic, and binding features of BA.1, BA.4/5, and BF.7. Our simulation findings, energetic analysis, and assessment of physicochemical properties collectively illuminate the dominance of the BA.1 variant over the others, a trend that is further substantiated by the sustained global prevalence of BA.1 relative to BA.4/5 and BF.7. Additionally, our simulation results align well with the reported cryoelectron microscopy (cryo-EM) structural data and epidemiological characteristics obtained from the Global Initiative on Sharing All Influenza Data (GISAID). This study presents a comprehensive comparative elucidation of the critical structural, dynamic, and binding attributes of these variants, providing insights into the predominance of BA.1 and its propensity to continuously generate numerous novel subvariants.

A novel mutation-proof, next-generation vaccine to fight against upcoming SARS-CoV-2 variants and subvariants, designed through AI enabled approaches and tools, along with the machine learning based immune simulation: A vaccine breakthrough

Emerging SARS-CoV-2 variants and subvariants are great concerns for their significant mutations, which are also responsible for vaccine escape. Therefore, the study was undertaken to develop a mutation-proof, next-generation vaccine to protect against all upcoming SARS-CoV-2 variants. We used advanced computational and bioinformatics approaches to develop a multi-epitopic vaccine, especially the AI model for mutation selection and machine learning (ML) strategies for immune simulation. AI-enabled and the top-ranked antigenic selection approaches were used to select nine mutations from 835 RBD mutations. We selected twelve common antigenic B cell and T cell epitopes (CTL and HTL) containing the nine RBD mutations and joined them with the adjuvants, PADRE sequence, and suitable linkers. The constructs' binding affinity was confirmed through docking with TLR4/MD2 complex and showed significant binding free energy (-96.67 kcal mol^-1) with positive binding affinity. Similarly, the calculated eigenvalue (2.428517e-05) from the NMA of the complex reveals proper molecular motion and superior residues' flexibility. Immune simulation shows that the candidate can induce a robust immune response. The designed mutation-proof, multi-epitopic vaccine could be a remarkable candidate for upcoming SARS-CoV-2 variants and subvariants. The study method might guide researchers in developing AI-ML and immunoinformatics-based vaccines for infectious disease.

In silico identification and molecular dynamic simulations of derivatives of 6,6-dimethyl-3-azabicyclo[3.1.0]hexane-2-carboxamide against main protease 3CLpro of SARS-CoV-2 viral infection

CONTEXT

The unavailability of target-specific antiviral drugs for SARS-CoV-2 viral infection kindled the motivation to virtually design derivatives of 6,6-dimethyl-3-azabicyclo[3.1.0]hexane-2-carboxamide as potential antiviral inhibitors against the concerned virus. The molecular docking and molecular dynamic results revealed that the reported derivatives have a potential to act as antiviral drug against SARS-CoV-2. The reported hit compounds can be considered for in vitro and in vivo analyses.

METHODS

Fragment-based drug designing was used to model the derivatives. Furthermore, DFT simulations were carried out using B3LYP/6-311G** basis set. Docking simulations were performed by using a combination of empirical free energy force field with a Lamarckian genetic algorithm under AutoDock 4.2. By the application of AMBER14 force field and SPCE water model, molecular dynamic simulations and MM-PBSA were calculated for 100 ns.

Identification of SARS-CoV-2 RNA dependent RNA polymerase inhibitors using pharmacophore modelling, molecular docking and molecular dynamics simulation approaches

The RNA-dependent RNA polymerase (RdRp) is one of the crucial enzymes in severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2) catalysing the replication of RNA, therefore acts as a potential target for antiviral drug design. The fixation of a ligand in the active site of RdRp may alter the SARS-CoV-2 life cycle. Present work aimed at identifying novel inhibitors of the SARS-CoV-2 RdRp enzyme by performing pharmacophore-based virtual screening, molecular docking and molecular dynamics simulation (MDS). Initially, the pharmacophore model of SARS-CoV-2 RdRp was constructed and the resulting model was used to screen compounds from ChEMBL, ZINC and PubChem databases. During the investigation, 180 compounds were screened using the above model and subjected to molecular docking with RdRp. Two compounds viz. ChEMBL1276156 and PubChem135548348 showed a strong binding affinity with RdRp than its standard inhibitor, Remdesivir. Toxicity prediction of these two compounds reveals their non-toxic nature. These compounds were further subjected to MDS for 100 ns to check their stability after binding with RdRp. The MDS of RdRp-ChEMBL1276156 and RdRp-PubChem135548348 complexes show enhanced stability in comparison to the RdRp-Remdesivir complex. The average interaction energy calculated after 100 ns of MDS was -146.56 and -172.68 KJ mol^-1 for RdRp-CHEMBL1276156 complex and RdRp-PubChem135548348 complex, respectively, while -59.90 KJ mol^-1 for RdRp-Remdesivir complex. The current investigation reveals that these two compounds are potent inhibitors of SARS-CoV-2 RdRp and they could be tested in the experimental condition to evaluate their efficacy against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

A Computer Simulation of SARS-CoV-2 Mutation Spectra for Empirical Data Characterization and Analysis

It is very important to compute the mutation spectra, and simulate the intra-host mutation processes by sequencing data, which is not only for the understanding of SARS-CoV-2 genetic mechanism, but also for epidemic prediction, vaccine, and drug design. However, the current intra-host mutation analysis algorithms are not only inaccurate, but also the simulation methods are unable to quickly and precisely predict new SARS-CoV-2 variants generated from the accumulation of mutations. Therefore, this study proposes a novel accurate strand-specific SARS-CoV-2 intra-host mutation spectra computation method, develops an efficient and fast SARS-CoV-2 intra-host mutation simulation method based on mutation spectra, and establishes an online analysis and visualization platform. Our main results include: (1) There is a significant variability in the SARS-CoV-2 intra-host mutation spectra across different lineages, with the major mutations from G- > A, G- > C, G- > U on the positive-sense strand and C- > U, C- > G, C- > A on the negative-sense strand; (2) our mutation simulation reveals the simulation sequence starts to deviate from the base content percentage of Alpha-CoV/Delta-CoV after approximately 620 mutation steps; (3) 2019-NCSS provides an easy-to-use and visualized online platform for SARS-Cov-2 online analysis and mutation simulation.

Screening of potential bio-molecules from Moringa olifera against SARS-CoV-2 main protease using computational approaches

COVID-19 caused by SARS-CoV-2 is responsible for the deaths of millions of people worldwide. It is having devastating effects on the people of all countries. In this regard, the phytochemicals of medicinal plants could be explored to prevent this disease. M. oleifera is a miracle plant with antibacterial, antiviral, and antioxidant properties because of its high content of flavonoids, glucosides and glucosinolates. Therefore, we constructed a library of 294 phytochemicals of M. oleifera and filtered it through the FAF-Drugs4. Further, molecular docking studies of filtered phytochemicals were performed with Mpro enzyme to investigate the binding interactions. Drug likeness properties, ADMET prediction were analyzed to determine the therapeutic aspect of these compounds. Based on the binding energy score of the top 4 compounds, the results indicate that Vicenin-2 has the highest binding affinity (-8.6 kcal mol^-1) as compared to the reference molecule (-8.4 kcal mol^-1). ADMET result reveals that all top four compounds have minimal toxic effects and good absorption. Further, 500 ns molecular dynamics simulation of the top four compounds showed that Kaempferol-3-O-rutinoside and Vitexin have good stability with Mpro. These two compounds were then subjected for MMPBSA (last 50 ns) calculation to analyze the protein-ligand stability and dynamic behavior. Kaempferol-3-O-rutinoside and Vitexin showed very good binding free energy i.e. -40.136 kJ mol^-1 and -26.784 kJ mol^-1, respectively. Promising outcomes from MD simulations evidence the worth of these compounds for future drug development to combat coronavirus disease.Communicated by Ramaswamy H. Sarma.

Hotspot residues and resistance mutations in the nirmatrelvir-binding site of SARS-CoV-2 main protease: Design, identification, and correlation with globally circulating viral genomes

Shortly after the onset of the COVID-19 pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has acquired numerous variations in its intracellular proteins to adapt quickly, become more infectious, and ultimately develop drug resistance by mutating certain hotspot residues. To keep the emerging variants at bay, including Omicron and subvariants, FDA has approved the antiviral nirmatrelvir for mild-to-moderate and high-risk COVID-19 cases. Like other viruses, SARS-CoV-2 could acquire mutations in its main protease (Mpro) to adapt and develop resistance against nirmatrelvir. Employing a unique high-throughput protein design technique, the hotspot residues, and signatures of adaptation of Mpro having the highest probability of mutating and rendering nirmatrelvir ineffective were identified. Our results show that ∼40% of the designed mutations in Mpro already exist in the globally circulating SARS-CoV-2 lineages and several predicted mutations. Moreover, several high-frequency, designed mutations were found to be in corroboration with the experimentally reported nirmatrelvir-resistant mutants and are naturally occurring. Our work on the targeted design of the nirmatrelvir-binding site offers a comprehensive picture of potential hotspot sites and resistance mutations in Mpro and is thus crucial in comprehending viral adaptation, robust antiviral design, and surveillance of evolving Mpro variations.

Herbo-mineral formulation, Divya-Swasari-Vati averts SARS-CoV-2 pseudovirus entry into human alveolar epithelial cells by interfering with spike protein-ACE 2 interaction and IL-6/TNF-α /NF-κB signaling

The herbo-mineral formulation, Divya-Swasari-Vati (DSV), is a well-known Ayurvedic medication for respiratory ailments. In a recent pre-clinical study, DSV rescued humanized zebrafish from SARS-CoV-2 S-protein-induced pathologies. This merited for an independent evaluation of DSV as a SARS-CoV-2 entry inhibitor in the human host cell and its effectiveness in ameliorating associated cytokine production. The ELISA-based protein-protein interaction study showed that DSV inhibited the interactions of recombinant human ACE 2 with three different variants of S proteins, namely, Smut 1 (the first reported variant), Smut 2 (W436R variant) and Smut 3 (D614G variant). Entry of recombinant vesicular stomatitis SARS-CoV-2 (VSVppSARS-2S) pseudovirus, having firefly luciferase and EGFP reporters, was assessed through luciferase assay and fluorescent microscopy. DSV exhibited dose-dependent inhibition of VSVppSARS-2S pseudovirus entry into human lung epithelial A549 cells and also suppressed elevated levels of secreted pro-inflammatory cytokines such as interleukin-6 (IL-6), interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α) induced by viral infection mimicking Poly I:C-, S-protein- and VSVppSARS-2S pseudovirus. In human immune cells, DSV also moderated TNF-α-mediated NF-κB induction, in a dose-dependent manner. The observed anti-viral effect of DSV against SARS-CoV-2 is attributable to the presence of different metabolites Summarily, the observations from this study biochemically demonstrated that DSV interfered with the interaction between SARS-CoV-2 S-protein and human ACE 2 receptor which consequently, inhibited viral entry into the host cells and concomitant induction of inflammatory response.

Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has led to a global pandemic. Deep learning (DL) technology and molecular dynamics (MD) simulation are two mainstream computational approaches to investigate the geometric, chemical and structural features of protein and guide the relevant drug design. Despite a large amount of research papers focusing on drug design for SARS-COV-2 using DL architectures, it remains unclear how the binding energy of the protein-protein/ligand complex dynamically evolves which is also vital for drug development. In addition, traditional deep neural networks usually have obvious deficiencies in predicting the interaction sites as protein conformation changes. In this review, we introduce the latest progresses of the DL and DL-based MD simulation approaches in structure-based drug design (SBDD) for SARS-CoV-2 which could address the problems of protein structure and binding prediction, drug virtual screening, molecular docking and complex evolution. Furthermore, the current challenges and future directions of DL-based MD simulation for SBDD are also discussed.

Monoclonal antibodies: a remedial approach to prevent SARS-CoV-2 infection

SARS-CoV-2, the newly emerged virus of the Coronaviridae family is causing havoc worldwide. The novel coronavirus 2019 was first reported in Wuhan, China marked as the third highly infectious pathogenic virus of the twenty-first century. The typical manifestations of COVID-19 include cough, sore throat, fever, fatigue, loss of sense of taste and difficulties in breathing. Large numbers of SARS-CoV-2 infected patients have mild to moderate symptoms, however severe and life-threatening cases occur in about 5-10% of infections with an approximately 2% mortality rate. For the treatment of SARS-CoV-2, the use of neutralizing monoclonal antibodies (mAbs) could be one approach. The receptor binding domain (RBD) and N-terminal domain (NTD) situated on the peak of the spike protein (S-Protein) of SARS-CoV-2 are immunogenic in nature, therefore, can be targeted by neutralizing monoclonal antibodies. Several bioinformatics approaches highlight the identification of novel SARS-CoV-2 epitopes which can be targeted for the development of COVID-19 therapeutics. Here we present a summary of neutralizing mAbs isolated from COVID-19 infected patients which are anticipated to be a better therapeutic alternative against SARS-CoV-2. However, provided the vast escalation of the disease worldwide affecting people from all strata, affording expensive mAb therapy will not be feasible. Hence other strategies are also being employed to find suitable vaccine candidates and antivirals against SARS-CoV-2 that can be made easily available to the population.

A comprehensive protein design protocol to identify resistance mutations and signatures of adaptation in pathogens

Most pathogens mutate and evolve over time to escape immune and drug pressure. To achieve this, they alter specific hotspot residues in their intracellular proteins to render the targeted drug(s) ineffective and develop resistance. Such hotspot residues may be located as a cluster or uniformly as a signature of adaptation in a protein. Identifying the hotspots and signatures is extremely important to comprehensively understand the disease pathogenesis and rapidly develop next-generation therapeutics. As experimental methods are time-consuming and often cumbersome, there is a need to develop efficient computational protocols and adequately utilize them. To address this issue, we present a unique computational protein design protocol that identifies hotspot residues, resistance mutations and signatures of adaptation in a pathogen’s protein against a bound drug. Using the protocol, the binding affinity between the designed mutants and drug is computed quickly, which offers predictions for comparison with biophysical experiments. The applicability and accuracy of the protocol are shown using case studies of a few protein–drug complexes. As a validation, resistance mutations in severe acute respiratory syndrome coronavirus 2 main protease (Mpro) against narlaprevir (an inhibitor of hepatitis C NS3/4A serine protease) are identified. Notably, a detailed methodology and description of the working principles of the protocol are presented. In conclusion, our protocol will assist in providing a first-hand explanation of adaptation, hotspot-residue variations and surveillance of evolving resistance mutations in a pathogenic protein.

High-throughput design of symmetrical dimeric SARS-CoV-2 main protease: structural and physical insights into hotspots for adaptation and therapeutics

Dimerization of SARS-CoV-2 main protease (M^pro) is a prerequisite for its processing activity. With >2000 mutations already reported in M^pro, SARS-CoV-2 may accumulate mutations in the M^pro dimeric interface to stabilize it further. We employed high-throughput protein design strategies to design the symmetrical dimeric interface of M^pro (300 000 designs) to identify mutational hotspots that render the M^pro more stable. We found that ∼22% of designed mutations that yield stable M^pro dimers already exist in SARS-CoV-2 genomes and are currently circulating. Our multi-parametric analyses highlight potential M^pro mutations that SARS-CoV-2 may develop, providing a foundation for assessing viral adaptation and mutational surveillance.

SARS-CoV-2: Evolution and Emergence of New Viral Variants

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiological agent responsible for the coronavirus disease 2019 (COVID-19). The high rate of mutation of this virus is associated with a quick emergence of new viral variants that have been rapidly spreading worldwide. Several mutations have been documented in the receptor-binding domain (RBD) of the viral spike protein that increases the interaction between SARS-CoV-2 and its cellular receptor, the angiotensin-converting enzyme 2 (ACE2). Mutations in the spike can increase the viral spread rate, disease severity, and the ability of the virus to evade either the immune protective responses, monoclonal antibody treatments, or the efficacy of current licensed vaccines. This review aimed to highlight the functional virus classification used by the World Health Organization (WHO), Phylogenetic Assignment of Named Global Outbreak (PANGO), Global Initiative on Sharing All Influenza Data (GISAID), and Nextstrain, an open-source project to harness the scientific and public health potential of pathogen genome data, the chronological emergence of viral variants of concern (VOCs) and variants of interest (VOIs), the major findings related to the rate of spread, and the mutations in the spike protein that are involved in the evasion of the host immune responses elicited by prior SARS-CoV-2 infections and by the protection induced by vaccination.

Assessment of activity of chalcone compounds as inhibitors of 3-chymotrypsin like protease (3CLPro) of SARS-CoV-2: in silico study

The COVID-19 is still pandemic due to emerging of various variant of concern of SARS-CoV2. Hence, it is devastating the world, causing significant economic as well as social chaos. This needs great effort to search and develop effective alternatives along with vaccination. Therefore, to continue drug discovery endeavors, we used chalcone derivatives to find an effective drug candidate against SARS-CoV2. Chalcone is a common simple scaffold that exists in many diets as well as in traditional medicine. Natural as well as synthetic chalcones have shown numerous interesting biological activities and are also effective in fighting various diseases. Hence, various computational methods were applied to find out potential inhibitors of 3CL^Pro using a library of 3000 compounds of chalcones. Firstly, the screening by structure-based pharmacophore model yielded 84 hits that were subjected to molecular docking. The top 10 docked compounds were characterized for stability by using 100 ns molecular dynamic (MD) simulation approach. Further, the binding free energy calculation by MMPBSA showed that four compounds bind to 3CL^Pro enzyme with high affinity, i.e., − 87.962 (kJ/mol), − 66.125 (kJ/mol), − 59.589 (kJ/mol), and − 66.728 (kJ/mol), respectively. Since chalcone is a common simple scaffold that is present in many diets as well as in traditional medicine, we suggest that screened compounds may emerge as promising drug candidates for SARS-CoV-2. These compounds may be investigated in vitro to evaluate the efficacy against SARS-CoV-2.

Emergence of unique SARS-CoV-2 ORF10 variants and their impact on protein structure and function

The devastating impact of the ongoing coronavirus disease 2019 (COVID-19) on public health, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has made targeting the COVID-19 pandemic a top priority in medical research and pharmaceutical development. Surveillance of SARS-CoV-2 mutations is essential for the comprehension of SARS-CoV-2 variant diversity and their impact on virulence and pathogenicity. The SARS-CoV-2 open reading frame 10 (ORF10) protein interacts with multiple human proteins CUL2, ELOB, ELOC, MAP7D1, PPT1, RBX1, THTPA, TIMM8B, and ZYG11B expressed in lung tissue. Mutations and co-occurring mutations in the emerging SARS-CoV-2 ORF10 variants are expected to impact the severity of the virus and its associated consequences. In this article, we highlight 128 single mutations and 35 co-occurring mutations in the unique SARS-CoV-2 ORF10 variants. The possible predicted effects of these mutations and co-occurring mutations on the secondary structure of ORF10 variants and host protein interactomes are presented. The findings highlight the possible effects of mutations and co-occurring mutations on the emerging 140 ORF10 unique variants from secondary structure and intrinsic protein disorder perspectives.

Peptides and peptidomimetics as therapeutic agents for Covid-19

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) Covid-19 pandemic has caused high morbidity and mortality rates worldwide. Virus entry into cells can be blocked using several strategies, including inhibition of protein-protein interactions (PPIs) between the viral spike glycoprotein and cellular receptors, as well as blocking of spike protein conformational changes that are required for cleavage/activation and fusogenicity. The spike-mediated viral attachment and entry into cells via fusion of the viral envelope with cellular membranes involve PPIs mediated by short peptide fragments exhibiting particular secondary structures. Thus, peptides that can inhibit these PPIs may be used as potential antiviral agents preventing virus entry and spread. This review is focused on peptides and peptidomimetics as PPI modulators and protease inhibitors against SARS-CoV-2.

DFT and MD simulation investigation of favipiravir as an emerging antiviral option against viral protease (3CLpro) of SARS-CoV-2

As per date, around 20 million COVID-19 cases reported from across the globe due to a tiny 125 nm sized virus: SARS-CoV-2 which has created a pandemic and left an unforgettable impact on our world. Besides vaccine, medical community is in a race to identify an effective drug, which can fight against this disease effectively. Favipiravir (F) has recently attracted too much attention as an effective repurposed drug against COVID-19. In the present study, the pertinency of F has been tested as an antiviral option against viral protease (3CLpro) of SARS-CoV-2 with the help of density functional theory (DFT) and MD Simulation. Different electronic properties of F such as atomic charges, molecular electrostatic properties (MEP), chemical reactivity and absorption analysis have been studied by DFT. In order to understand the interaction and stability of inhibitor F against viral protease, molecular docking and MD simulation have been performed. Various output like interaction energies, number of intermolecular hydrogen bonding, binding energy etc. have established the elucidate role of F for the management of CoV-2 virus for which there is no approved therapies till now. Our findings highlighted the need to further evaluate F as a potential antiviral against SARS-CoV-2.

Emerging SARS-CoV-2 Variants: Genetic Variability and Clinical Implications

The sudden rise in COVID-19 cases in 2020 and the incessant emergence of fast-spreading variants have created an alarming situation worldwide. Besides the continuous advancements in the design and development of vaccines to combat this deadly pandemic, new variants are frequently reported, possessing mutations that rapidly outcompeted an existing population of circulating variants. As concerns grow about the effects of mutations on the efficacy of vaccines, increased transmissibility, immune escape, and diagnostic failures are few other apprehensions liable for more deadly waves of COVID-19. Although the phenomenon of antigenic drift in new variants of SARS-CoV-2 is still not validated, it is conceived that the virus is acquiring new mutations as a fitness advantage for rapid transmission or to overcome immunological resistance of the host cell. Considerable evolution of SARS-CoV-2 has been observed since its first appearance in 2019, and despite the progress in sequencing efforts to characterize the mutations, their impacts in many variants have not been analyzed. The present article provides a substantial review of literature explaining the emerging variants of SARS-CoV-2 circulating globally, key mutations in viral genome, and the possible impacts of these new mutations on prevention and therapeutic strategies currently administered to combat this pandemic. Rising infections, mortalities, and hospitalizations can possibly be tackled through mass vaccination, social distancing, better management of available healthcare infrastructure, and by prioritizing genome sequencing for better serosurveillance studies and community tracking.

COVID-19 challenges: From SARS-CoV-2 infection to effective point-of-care diagnosis by electrochemical biosensing platforms

In January 2020, the World Health Organization (WHO) identified a new zoonotic virus, SARS-CoV-2, responsible for causing the COVID-19 (coronavirus disease 2019). Since then, there has been a collaborative trend between the scientific community and industry. Multidisciplinary research networks try to understand the whole SARS-CoV-2 pathophysiology and its relationship with the different grades of severity presented by COVID-19. The scientific community has gathered all the data in the quickly developed vaccines that offer a protective effect for all variants of the virus and promote new diagnostic alternatives able to have a high standard of efficiency, added to shorter response analysis time and portability. The industry enters in the context of accelerating the path taken by science until obtaining the final product. In this review, we show the principal diagnostic methods developed during the COVID-19 pandemic. However, when we observe the diagnostic tools section of an efficient infection outbreak containment report and the features required for such tools, we could observe a highlight of electrochemical biosensing platforms. Such devices present a high standard of analytical performance, are low-cost tools, easy to handle and interpret, and can be used in the most remote and low-resource regions. Therefore, probably, they are the ideal point-of-care diagnostic tools for pandemic scenarios.

Drug-based therapeutic strategies for COVID-19-infected patients and their challenges

Emerging epidemic-prone diseases have introduced numerous health and economic challenges in recent years. Given current knowledge of COVID-19, herd immunity through vaccines alone is unlikely. In addition, vaccination of the global population is an ongoing challenge. Besides, the questions regarding the prevalence and the timing of immunization are still under investigation. Therefore, medical treatment remains essential in the management of COVID-19. Herein, recent advances from beginning observations of COVID-19 outbreak to an understanding of the essential factors contributing to the spread and transmission of COVID-19 and its treatment are reviewed. Furthermore, an in-depth discussion on the epidemiological aspects, clinical symptoms and most efficient medical treatment strategies to mitigate the mortality and spread rates of COVID-19 is presented.

Development of an in silico multi-epitope vaccine against SARS-COV-2 by précised immune-informatics approaches

The coronavirus family has been infecting the human population for the past two decades, but the ongoing coronavirus called SARS-CoV-2 has posed an enigmatic challenge to global public health security. Since last year, the mutagenic quality of this virus is causing changes to its genetic material. To prevent those situations, the FDA approved some emergency vaccines but there is no assurance that these will function properly in the complex human body system. In point of view, a short but efficient effort has made in this study to develop an immune epitope-based therapy for the rapid exploitation of SARS-CoV-2 by applying in silico structural biology and advancing immune information strategies. The antigenic epitopes were screened from the Surface, Membrane, Envelope proteins of SARS-CoV-2 and passed through several immunological filters to determine the best possible one. According to this, 7CD4+, 10CD8+ and 5 B-cell epitopes were found to be prominent, antigenic, immunogenic, and most importantly, highly conserved among 128 Bangladeshi and 110 other infected countries SARS-CoV-2 variants. After that, the selected epitopes and adjuvant were linked to finalize the multi-epitope vaccine by appropriate linkers. The immune simulation disclosed that the engineered vaccine could activate both humoral and innate immune responses. For the prediction of an effective binding, molecular docking was carried out between the vaccine and immunological receptors (TLRs). Strong binding affinity and good docking scores clarified the stringency of the vaccines. Furthermore, MD simulation was performed within the highest binding affinity complex to observe the stability. Codon optimization and other physicochemical properties revealed that the vaccine would be suitable for a higher expression at cloning level. So, monitoring the overall in silico assessment, we anticipated that our engineered vaccine would be a plausible prevention against COVID-19.

SARS-CoV-2, Covid-19, and the debunking of conspiracy theories

The emergence of a novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has engaged considerable awareness and attention around the world. The associated disease, coronavirus disease 2019 (Covid-19), has now involved virtually all 200 countries. The total number of confirmed cases has been much more than in the two previous outbreaks of human coronaviruses, that is, SARS-CoV and Middle East respiratory syndrome coronavirus. In line with the outbreak escalation, false information about SARS-CoV-2 and its associated disease disseminated globally, particularly through online and social media. Believers in conspiracy theories promote misinformation that the virus is not contagious, is the result of laboratory manipulation or is created to gain profit by distributing new vaccines. The most dangerous effect of this widely disseminated misinformation is it will negatively influence the attitudes and behaviours for preventive measures to contain the outbreak. In this review, I discuss common conspiracy theories associated with SARS-CoV-2 and Covid-19 and consider how we can address and counterbalance these issues based on scientific information and studies.

Immune Profile of SARS-CoV-2 Variants of Concern

The spread of the current Sars-Cov-2 pandemics leads to the development of mutations that are constantly monitored because they could affect the efficacy of vaccines. Three recently identified mutated strains, known as variants of concern, are rapidly spreading worldwide. Here, we study possible effects of these mutations on the immune response to Sars-Cov-2 infection using NetTepi a computational method based on artificial neural networks that considers binding and stability of peptides obtained by proteasome degradation for widely represented HLA class I alleles present in human populations as well as the T-cell propensity of viral peptides that measures their immune response. Our results show variations in the number of potential highly ranked peptides ranging between 0 and 20% depending on the specific HLA allele. The results can be useful to design more specific vaccines.

Role of artificial intelligence in peptide vaccine design against RNA viruses

RNA viruses have high rate of replication and mutation that help them adapt and change according to their environmental conditions. Many viral mutants are the cause of various severe and lethal diseases. Vaccines, on the other hand have the capacity to protect us from infectious diseases by eliciting antibody or cell-mediated immune responses that are pathogen-specific. While there are a few reviews pertaining to the use of artificial intelligence (AI) for SARS-COV-2 vaccine development, none focus on peptide vaccination for RNA viruses and the important role played by AI in it. Peptide vaccine which is slowly coming to be recognized as a safe and effective vaccination strategy has the capacity to overcome the mutant escape problem which is also being currently faced by SARS-COV-2 vaccines in circulation.Here we review the present scenario of peptide vaccines which are developed using mathematical and computational statistics methods to prevent the spread of disease caused by RNA viruses. We also focus on the importance and current stage of AI and mathematical evolutionary modeling using machine learning tools in the establishment of these new peptide vaccines for the control of viral disease.

On K-Means Clustering with IVIF Datasets for Post-COVID-19 Recovery Efforts

The recovery efforts of the tourism and hospitality sector are compromised by the emergence of COVID-19 variants that can escape vaccines. Thus, maintaining non-pharmaceutical measures amidst massive vaccine rollouts is still relevant. The previous works which categorize tourist sites and restaurants according to the perceived degree of tourists’ and customers’ exposure to COVID-19 are deemed relevant for sectoral recovery. Due to the subjectivity of predetermining categories, along with the failure of capturing vagueness and uncertainty in the evaluation process, this work explores the use k-means clustering with dataset values expressed as interval-valued intuitionistic fuzzy sets. In addition, the proposed method allows for the incorporation of criteria (or attribute) weights into the dataset, often not considered in traditional k-means clustering but relevant in clustering problems with attributes having varying priorities. Two previously reported case studies were analyzed to demonstrate the proposed approach, and comparative and sensitivity analyses were performed. Results show that the priorities of the criteria in evaluating tourist sites remain the same. However, in evaluating restaurants, customers put emphasis on the physical characteristics of the restaurants. The proposed approach assigns 12, 15, and eight sites to the “low exposure”, “moderate exposure”, and “high exposure” cluster, respectively, each with distinct characteristics. On the other hand, 16 restaurants are assigned “low exposure”, 16 to “moderate exposure”, and eight to “high exposure” clusters, also with distinct characteristics. The characteristics described in the clusters offer meaningful insights for sectoral recovery efforts. Findings also show that the proposed approach is robust to small parameter changes. Although idiosyncrasies exist in the results of both case studies, considering the characteristics of the resulting clusters, tourists or customers could evaluate any tourist site or restaurant according to their perceived exposure to COVID-19.

AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations

We present a novel technique to predict binding affinity trends between two molecules from atomistic molecular dynamics simulations. The technique uses a neural network algorithm applied to a series of images encoding the distance between two molecules in time. We demonstrate that our algorithm is capable of separating with high accuracy non-hydrophobic mutations with low binding affinity from those with high binding affinity. Moreover, we show high accuracy in prediction using a small subset of the simulation, therefore requiring a much shorter simulation time. We apply our algorithm to the binding between several variants of the SARS-CoV-2 spike protein and the human receptor ACE2.

Interface-based design of the favipiravir-binding site in SARS-CoV-2 RNA-dependent RNA polymerase reveals mutations conferring resistance to chain termination

Favipiravir is a broad-spectrum inhibitor of viral RNA-dependent RNA polymerase (RdRp) currently being used to manage COVID-19. Accumulation of mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RdRp may facilitate antigenic drift, generating favipiravir resistance. Focussing on the chain-termination mechanism utilized by favipiravir, we used high-throughput interface-based protein design to generate > 100 000 designs of the favipiravir-binding site of RdRp and identify mutational hotspots. We identified several single-point mutants and designs having a sequence identity of 97%-98% with wild-type RdRp, suggesting that SARS-CoV-2 can develop favipiravir resistance with few mutations. Out of 134 mutations documented in the CoV-GLUE database, 63 specific mutations were already predicted as resistant in our calculations, thus attaining ˜ 47% correlation with the sequencing data. These findings improve our understanding of the potential signatures of adaptation in SARS-CoV-2 against favipiravir.

Telaprevir is a potential drug for repurposing against SARS-CoV-2: computational and in vitro studies

Drug repurposing is an important approach to the assignment of already approved drugs for new indications. This technique bypasses some steps in the traditional drug approval system, which saves time and lives in the case of pandemics. Direct acting antivirals (DAAs) have repeatedly repurposed from treating one virus to another. In this study, 16 FDA-approved hepatitis C virus (HCV) DAA drugs were studied to explore their activities against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) human and viral targets. Among the 16 HCV DAA drugs, telaprevir has shown the best in silico evidence to work on both indirect human targets (cathepsin L [CTSL] and human angiotensin-converting enzyme 2 [hACE2] receptor) and direct viral targets (main protease [Mpro]). Moreover, the docked poses of telaprevir inside both hACE2 and Mpro were subjected to additional molecular dynamics simulations monitored by calculating the binding free energy using MM-GBSA. In vitro analysis of telaprevir showed inhibition of SARS-CoV-2 replication in cell culture (IC50 = 11.552 μM, CC50 = 60.865 μM, and selectivity index = 5.27). Accordingly, based on the in silico studies and supported by the presented in vitro analysis, we suggest that telaprevir may be considered for therapeutic development against SARS-CoV-2.

Accelerating COVID-19 Research Using Molecular Dynamics Simulation

The COVID-19 pandemic has emerged as a global medico-socio-economic disaster. Given the lack of effective therapeutics against SARS-CoV-2, scientists are racing to disseminate suggestions for rapidly deployable therapeutic options, including drug repurposing and repositioning strategies. Molecular dynamics (MD) simulations have provided the opportunity to make rational scientific breakthroughs in a time of crisis. Advancements in these technologies in recent years have become an indispensable tool for scientists studying protein structure, function, dynamics, interactions, and drug discovery. Integrating the structural data obtained from high-resolution methods with MD simulations has helped in comprehending the process of infection and pathogenesis, as well as the SARS-CoV-2 maturation in host cells, in a short duration of time. It has also guided us to identify and prioritize drug targets and new chemical entities, and to repurpose drugs. Here, we discuss how MD simulation has been explored by the scientific community to accelerate and guide translational research on SARS-CoV-2 in the past year. We have also considered future research directions for researchers, where MD simulations can help fill the existing gaps in COVID-19 research.

Computational studies reveal Fluorine based quinolines to be potent inhibitors for proteins involved in SARS-CoV-2 assembly

World is witnessing one of the worst pandemics of this century caused by SARS-CoV-2 virus which has affected millions of individuals. Despite rapid efforts to develop vaccines and drugs for COVID-19, the disease is still not under control. Chloroquine (CQ) and Hydroxychloroquine (HCQ) are two very promising inhibitors which have shown positive effect in combating the disease in preliminary experimental studies, but their use was reduced due to severe side-effects. Here, we performed a theoretical investigation of the same by studying the binding of the molecules with SARS-COV-2 Spike protein, the complex formed by Spike and ACE2 human receptor and a human serine protease TMPRSS2 which aids in cleavage of the Spike protein to initiate the viral activation in the body. Both the molecules had shown very good docking energies in the range of -6kcal/mol. Subsequently, we did a high throughput screening for other potential quinoline candidates which could be used as inhibitors. From the large pool of ligand candidates, we shortlisted the top three ligands (binding energy -8kcal/mol). We tested the stability of the docked complexes by running Molecular Dynamics (MD) simulations where we observed the stability of the quinoline analogues with the Spike-ACE2 and TMPRSS2 nevertheless the quinolines were not stable with the Spike protein alone. Thus, although the inhibitors bond very well with the protein molecules their intrinsic binding affinity depends on the protein dynamics. Moreover, the quinolines were stable when bound to electronegative pockets of Spike-ACE2 or TMPRSS2 but not with Viral Spike protein. We also observed that a Fluoride based compound: 3-[3-(Trifluoromethyl)phenyl]quinoline helps the inhibitor to bind with both Spike-ACE2 and TMPRSS2 with equal probability. The molecular details presented in this study would be very useful for developing quinoline based drugs for COVID-19 treatment.

Therapeutic p28 peptide targets essential H1N1 influenza virus proteins: insights from docking and molecular dynamics simulations

The H1N1 influenza virus causes a severe disease that affects the human respiratory tract leading to millions of deaths every year. At present, certain vaccines and few drugs are used to control the virus during seasonal outbreaks. However, high mutation rates and genetic reassortment make it challenging to prevent and mitigate outbreaks, leading to pandemics. Thus, alternate therapies are required for its management and control. Here, we report that a bacterial protein, azurin, and its peptide derivatives p18 and p28 target critical proteins of the influenza virus in an effective manner. The molecular docking studies show that the p28 peptide could target C-PB1, NS1-ED, PB2-CBD, PB2-RBD, NP, and PA proteins. These complexes were further subjected to the simulation of molecular dynamics and binding free energy calculations. The data indicate that p28 has an unusually high affinity and forms stable complexes with the viral proteins C-PB1, PB2-CBD, PB2-RBD, and NP. We suggest that the azurin derivative p28 peptide can act as an anti-influenza agent as it can bind to multiple targets and neutralize the virus. Additional experimental studies need to be conducted to evaluate its safety and efficacy as an anti-H1N1 molecule.

A Novel Therapeutic Peptide Blocks SARS-CoV-2 Spike Protein Binding with Host Cell ACE2 Receptor

Background and Objective

Coronavirus disease 2019 is a novel disease caused by the severe acute respiratory syndrome coronavirus (SARS-CoV)-2 virus. It was first detected in December 2019 and has since been declared a pandemic causing millions of deaths worldwide. Therefore, there is an urgent need to develop effective therapeutics against coronavirus disease 2019. A critical step in the crosstalk between the virus and the host cell is the binding of the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein to the peptidase domain of the angiotensin-converting enzyme 2 (ACE2) receptor present on the surface of host cells.

Methods

An in silico approach was employed to design a 13-amino acid peptide inhibitor (13AApi) against the RBD of the SARS-CoV-2 spike protein. Its binding specificity for RBD was confirmed by molecular docking using pyDockWEB, ClusPro 2.0, and HDOCK web servers. The stability of 13AApi and the SARS-CoV-2 spike protein complex was determined by molecular dynamics simulation using the GROMACS program while the physicochemical and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of 13AApi were determined using the ExPASy tool and pkCSM server. Finally, in vitro validation of the inhibitory activity of 13AApi against the spike protein was performed by an enzyme-linked immunosorbent assay.

Results

In silico analyses indicated that the 13AApi could bind to the RBD of the SARS-CoV-2 spike protein at the ACE2 binding site with high affinity. In vitro experiments validated the in silico findings, showing that 13AApi could significantly block the RBD of the SARS-CoV-2 spike protein.

Conclusions

Blockage of binding of the SARS-CoV-2 spike protein with ACE2 in the presence of the 13AApi may prevent virus entry into host cells. Therefore, the 13AApi can be utilized as a promising therapeutic agent to combat coronavirus disease 2019.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40268-021-00357-0.

Targeted design of drug binding sites in the main protease of SARS-CoV-2 reveals potential signatures of adaptation

Several existing drugs are currently being tested worldwide to treat COVID-19 patients. Recent data indicate that SARS-CoV-2 is rapidly evolving into more transmissible variants. It is therefore highly possible that SARS-CoV-2 can accumulate adaptive mutations modulating drug susceptibility and hampering viral antigenicity. Thus, it is vital to predict potential non-synonymous mutation sites and predict the evolution of protein structural modifications leading to drug tolerance. As two FDA-approved anti-hepatitis C virus (HCV) drugs, boceprevir, and telaprevir, have been shown to effectively inhibit SARS-CoV-2 by targeting the main protease (M^pro), here we used a high-throughput interface-based protein design strategy to identify mutational hotspots and potential signatures of adaptation in these drug binding sites of M^pro. Several mutants exhibited reduced binding affinity to these drugs, out of which hotspot residues having a strong tendency to undergo positive selection were identified. The data further indicated that these anti-HCV drugs have larger footprints in the mutational landscape of M^pro and hence encompass the highest potential for positive selection and adaptation. These findings are crucial in understanding the potential structural modifications in the drug binding sites of M^pro and thus its signatures of adaptation. Furthermore, the data could provide systemic strategies for robust antiviral design and discovery against COVID-19 in the future.

Epidemiology, pathogenesis, clinical presentations, diagnosis and treatment of COVID-19: a review of current evidence

INTRODUCTION

The COVID-19 pandemic has created a public health crisis, infected millions of people, and caused a significant number of deaths. SARS-CoV-2 transmits from person to person through several routes, mainly via respiratory droplets, which makes it difficult to contain its spread into the community. Here, we provide an overview of the epidemiology, pathogenesis, clinical presentation, diagnosis, and treatment of COVID-19.

AREAS COVERED

Direct person-to-person respiratory transmission has rapidly amplified the spread of coronavirus. In the absence of any clinically proven treatment options, the current clinical management of COVID-19 includes symptom management, infection prevention and control measures, optimized supportive care, and intensive care support in severe or critical illness. Developing an effective vaccine is now a leading research priority. Some vaccines have already been approved by the regulatory authorities for the prevention of COVID-19.

EXPERT OPINION

General prevention and protection measures regarding the containment and management of the second or third waves are necessary to minimize the risk of infection. Until now, four vaccines reported variable efficacies of between 62-95%, and two of them (Pfizer/BioNTech and Moderna) received FDA emergency use authorization. Equitable access and effective distribution of these vaccines in all countries will save millions of lives.

Identification of natural compounds as potent inhibitors of SARS-CoV-2 main protease using combined docking and molecular dynamics simulations

Coronavirus disease 2019 (COVID-19) has emerged from China and globally affected the entire population through the human-to-human transmission of a newly emerged virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The genome of SARS-CoV-2 encodes several proteins that are essential for multiplication and pathogenesis. The main protease (Mpro or 3CLpro) of SARS-CoV-2 plays a central role in its pathogenesis and thus is considered as an attractive drug target for the drug design and development of small-molecule inhibitors. We have employed an extensive structure-based high-throughput virtual screening to discover potential natural compounds from the ZINC database which could inhibit the Mpro of SARS-CoV-2. Initially, the hits were selected on the basis of their physicochemical and drug-like properties. Subsequently, the PAINS filter, estimation of binding affinities using molecular docking, and interaction analyses were performed to find safe and potential inhibitors of SARS-CoV-2 Mpro. We have identified ZINC02123811 (1-(3-(2,5,9-trimethyl-7-oxo-3-phenyl-7H-furo[3,2-g]chromen-6-yl)propanoyl)piperidine-4-carboxamide), a natural compound bearing appreciable affinity, efficiency, and specificity towards the binding pocket of SARS-CoV-2 Mpro. The identified compound showed a set of drug-like properties and preferentially binds to the active site of SARS-CoV-2 Mpro. All-atom molecular dynamics (MD) simulations were performed to evaluate the conformational dynamics, stability and interaction mechanism of Mpro with ZINC02123811. MD simulation results indicated that Mpro with ZINC02123811 forms a stable complex throughout the trajectory of 100 ns. These findings suggest that ZINC02123811 may be further exploited as a promising scaffold for the development of potential inhibitors of SARS-CoV-2 Mpro to address COVID-19.

Randomized placebo-controlled pilot clinical trial on the efficacy of ayurvedic treatment regime on COVID-19 positive patients

BACKGROUND

Specific treatment for COVID-19 is still an unmet need. Outcomes of clinical trials on repurposed drugs have not been yielding success. Therefore, it is necessary to include complementary approaches of medicine against COVID-19.

PURPOSE

This study was designed to evaluate the impact of traditional Indian Ayurvedic treatment regime on asymptomatic patients with COVID-19 infection.

STUDY DESIGN

It is a placebo controlled randomized double-blind pilot clinical trial.

METHODS

The study was registered with Clinical Trial Registry-India (vide Registration No. CTRI/2020/05/025273) and conducted at the Department of Medicine in National Institute of Medical Sciences and Research, Jaipur, India. 1 g of Giloy Ghanvati (Tinospora cordifolia) and 2 g of Swasari Ras (traditional herbo-mineral formulation) and 0.5 g each of Ashwagandha (Withania somnifera) and Tulsi Ghanvati (Ocimum sanctum) were given orally to the patients in treatment group twice per day for 7 days. Medicines were given in the form of tablets and each tablet weighed 500 mg. While, Swasari Ras was administered in powdered form, 30 min before breakfasts and dinners, rest were scheduled for 30 min post-meals. Patients in the treatment group also received 4 drops of Anu taila (traditional nasal drop) in each nostril every day 1 h before breakfast. Patients in the placebo group received identical-looking tablets and drops, post randomization and double blinded assortments. RT-qPCR test was used for the detection of viral load in the nasopharyngeal and oropharyngeal swab samples of study participants during the study. Chemiluminescent immunometric assay was used to quantify serum levels of interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-α) and high sensitivity C-reactive protein (hs-CRP) on day 1 and day 7 of the study.

RESULTS

By day 3, 71.1 % and 50.0 % patients recovered in the treatment and placebo groups, respectively. Treatment group witnessed 100 % recovery by day 7, while it was 60.0 % in the placebo group. Average fold changes in serum levels of hs-CRP, IL-6 and TNF-α in treatment group were respectively, 12.4, 2.5 and 20 times lesser than those in the placebo group at day 7. There was 40 % absolute reduction in the risk of delayed recovery from infection in the treatment group.

CONCLUSIONS

Ayurvedic treatment can expedite virological clearance, help in faster recovery and concomitantly reduce the risk of viral dissemination. Reduced inflammation markers suggested less severity of SARS-CoV-2 infection in the treatment group. Moreover, there was no adverse effect observed to be associated with this treatment.

Molecular dynamics and in silico mutagenesis on the reversible inhibitor-bound SARS-CoV-2 main protease complexes reveal the role of lateral pocket in enhancing the ligand affinity

The 2019 novel coronavirus pandemic caused by SARS-CoV-2 remains a serious health threat to humans and there is an urgent need to develop therapeutics against this deadly virus. Recent scientific evidences have suggested that the main protease (Mpro) enzyme in SARS-CoV-2 can be an ideal drug target due to its crucial role in the viral replication and transcription processes. Therefore, there are ongoing research efforts to identify drug candidates against SARS-CoV-2 Mpro that resulted in hundreds of X-ray crystal structures of ligand-bound Mpro complexes in the Protein Data Bank (PDB) describing the interactions of different fragment chemotypes within different sites of the Mpro. In this work, we performed rigorous molecular dynamics (MD) simulation of 62 reversible ligand-Mpro complexes in the PDB to gain mechanistic insights about their interactions at the atomic level. Using a total of over 3 µs long MD trajectories, we characterized different pockets in the apo Mpro structure, and analyzed the dynamic interactions and binding affinity of ligands within those pockets. Our results identified the key residues that stabilize the ligands in the catalytic sites and other pockets of Mpro. Our analyses unraveled the role of a lateral pocket in the catalytic site in Mpro that is critical for enhancing the ligand binding to the enzyme. We also highlighted the important contribution from HIS163 in the lateral pocket towards ligand binding and affinity against Mpro through computational mutation analyses. Further, we revealed the effects of explicit water molecules and Mpro dimerization in the ligand association with the target. Thus, comprehensive molecular-level insights gained from this work can be useful to identify or design potent small molecule inhibitors against SARS-CoV-2 Mpro.

A computational study on active constituents of Habb-ul-aas and Tabasheer as inhibitors of SARS-CoV-2 main protease

A respiratory pandemic known as coronavirus disease-19 (COVID-19) has created havoc since it emerged from Wuhan, China. COVID-19 is caused by a newly emerged SARS coronavirus (SARS-CoV) with increased pathogenicity named severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Due to the lack of understanding of the mechanism of pathogenesis, an effective therapeutic option is unavailable. Epidemics described in Unani ancient literature include nazla-e-wabai and humma-e-wabai, and most of the symptoms of COVID-19 resemble nazla-e-wabai. Hence, in light of Unani literature, the treatment of COVID-19 can be managed with the composites prescribed in Unani medicine for nazla-e-wabai. In this study, a structure-based drug design approach was carried out to check the effectiveness of the pharmacologically active constituents of the Unani composites prescribed to treat nazla-e-wabai against SARS-CoV-2. We performed molecular docking of the active constituents of these composites against the main protease (M^pro), a potential drug target in SARS-CoV-2. Using detailed molecular docking analysis, Habb-ul-aas and Tabasheer were identified as potential inhibitors of SARS-CoV-2 M^pro. The active constituents of both these composites bind to the substrate-binding pocket of SARS-CoV-2 M^pro, forming interactions with key residues of the binding pocket. Molecular dynamics (MD) simulation suggested the binding of active constituents of Habb-ul-aas with SARS-CoV-2 M^pro with a strong affinity as compared to the constituents of Tabasheer. Thus, this study sheds light on the use of these Unani composites in COVID-19 therapeutics.Communicated by Ramaswamy H. Sarma.

Coronil, a Tri-Herbal Formulation, Attenuates Spike-Protein-Mediated SARS-CoV-2 Viral Entry into Human Alveolar Epithelial Cells and Pro-Inflammatory Cytokines Production by Inhibiting Spike Protein-ACE-2 Interaction

PURPOSE

Coronil is a tri-herbal formulation containing extracts from Withania somnifera, Tinospora cordifolia, and Ocimum sanctum. Recently, it was shown that Coronil rescued humanized zebrafish from SARS-CoV-2 induced pathologies. Based on reported computational studies on the phytochemicals present in Coronil, it could be a potential inhibitor of SARS-CoV-2 entry into the host cell and associated cytokines' production.

METHODS

Through an ELISA-based biochemical assay, effects of Coronil on interaction between ACE-2 and different mutants of viral spike (S) protein, crucial for viral invasion of host cell, were evaluated. Additionally, using recombinant pseudoviruses having SARS-CoV-2 spike (S) protein in their envelopes and firefly luciferase reporter in their genomes, effects of Coronil on virus entry into human alveolar epithelial cells were evaluated through luciferase assay. UHPLC profiled Coronil also modulated S-protein mediated production of pro-inflammatory cytokines in A549 cells, like interleukin-6 (IL-6), interleukin-1β (IL-1β), and tumor necrosis factor-α (TNF-α), as evaluated through RT-qPCR and ELISA.

RESULTS

Coronil effectively inhibited the interaction of ACE-2 not only with the wild-type S protein (SWT) but also with its currently prevalent and more infectious variant (SD614G) and another mutant (SW436R) with significantly higher affinity toward ACE-2. Treatment with Coronil significantly reduced the increased levels of IL-6, IL-1β, and TNF-α in A549 cells incubated with different S-protein variants in a dose-dependent manner. Likewise, it also prevented the SARS-CoV-2 S-protein pseudotyped vesicular stomatitis virus (VSVppSARS-2S) mediated cytokine response in these cells by reducing entry of pseudoviruses into host cells.

CONCLUSION

Coronil prevented SARS-CoV-2 S-protein mediated viral entry into A549 cells by inhibiting spike protein-ACE-2 interactions. SARS-CoV-2 S protein induced inflammatory cytokine response in these cells was also moderated by Coronil.

Unraveling the mechanism of arbidol binding and inhibition of SARS-CoV-2: Insights from atomistic simulations

The COVID-19 pandemic has spread rapidly and poses an unprecedented threat to the global economy and human health. Broad-spectrum antivirals are currently being administered to treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). China's prevention and treatment guidelines suggest the use of an anti-influenza drug, arbidol, for the clinical treatment of COVID-19. Reports indicate that arbidol could neutralize SARS-CoV-2. Monotherapy with arbidol is superior to lopinavir-ritonavir or favipiravir for treating COVID-19. In SARS-CoV-2 infection, arbidol acts by interfering with viral binding to host cells. However, the detailed mechanism by which arbidol induces the inhibition of SARS-CoV-2 is not known. Here, we present atomistic insights into the mechanism underlying membrane fusion inhibition of SARS-CoV-2 by arbidol. Molecular dynamics (MD) simulation-based analyses demonstrate that arbidol binds and stabilizes at the receptor-binding domain (RBD)/ACE2 interface with a high affinity. It forms stronger intermolecular interactions with the RBD than ACE2. Analyses of the detailed decomposition of energy components and binding affinities revealed a substantial increase in the affinity between the RBD and ACE2 in the arbidol-bound RBD/ACE2 complex, suggesting that arbidol generates favorable interactions between them. Based on our MD simulation results, we propose that the binding of arbidol induces structural rigidity in the viral glycoprotein, thus restricting the conformational rearrangements associated with membrane fusion and virus entry. Furthermore, key residues of the RBD and ACE2 that interact with arbidol were identified, opening the door for developing therapeutic strategies and higher-efficacy arbidol derivatives or lead drug candidates.

Pan-India novel coronavirus SARS-CoV-2 genomics and global diversity analysis in spike protein

The mortality rates due to COVID-19 have been found disproportionate globally and are currently being researched. India mortality rate with a population of 1.3 billion people is relatively lowest to other countries with high infection rates. Genetic composition of circulating isolates continues to be a key determinant of virulence and pathogenesis. This study aimed to analyse the extent of divergence between genomes of Indian isolates (n = 2525 as compared to reference Wuhan-1 strain and isolates from countries showing higher fatality rates including France, Italy, Belgium, and the USA. The study also analyses the impact of key mutations on interactions with angiotensin converting enzyme 2 (ACE2) and panel of neutralizing monoclonal antibodies. Using 1,44,605 spike protein sequences, global prevalence of mutations in spike protein was observed. The study suggests that SARS-CoV-2 genomes from India share consensus with global trends with respect to D614G as most prevalent mutational event (81.66% among 2525 Indian isolates). Indian isolates did not reported prevalence of N439K mutation in receptor binding motif (RBM) as compared to global isolates (0.54%). Computational docking and molecular dynamics simulation analysis of N439K mutation with respect to ACE 2 binding and reactivity with RBM targeted antibodies viz., B38, BD23, CB6, P2B-F26 and EY6A suggests that variant have relatively higher affinity with ACE 2 receptor which may support higher infectivity. The study warrants large scale monitoring of Indian isolates as SARS-CoV-2 virus is expected to evolve and mutations may appear in unpredictable way.