Lead optimization, pharmacophore development and scaffold design of protein kinase CK2 inhibitors as potential COVID-19 therapeutics.

Therapeutic agents being designed against COVID-19 have targeted either the virus directly or the host cellular machinery. A particularly attractive host target is the ubiquitous and constitutively active serine-threonine kinase, Protein kinase CK2 (CK2). CK2 enhances viral protein synthesis by inhibiting the sequestration of host translational machinery as stress granules and assists in viral egression via association with the N-protein at filopodial protrusions of the infected cell. CK2 inhibitors such as Silmitasertib have been proposed as possible therapeutic candidates in COVID-19 infections. The present study aims to optimize Silmitasertib, develop pharmacophore models and design unique scaffolds to modulate CK2. The lead optimization phase involved the generation of compounds structurally similar to Silmitasertib via bioisostere replacement followed by a multi-stage docking approach to identify drug-like candidates. Molecular dynamics (MD) simulations were performed for two promising candidates (ZINC-43206125 and PC-57664175) to estimate their binding stability and interaction. Top scoring candidates from the lead optimization phase were utilized to build ligand-based pharmacophore models. These models were then merged with structure-based pharmacophores (e-pharmacophores) to build a hybrid hypothesis. This hybrid hypothesis was validated against a decoy set and used to screen a diverse kinase inhibitors library to identify favored chemical features in the retrieved actives. These chemical features include; an anion, an aromatic ring and an H-bond acceptor. Based on the knowledge of these features; de-novo scaffold design was carried out which identified phenindiones, carboxylated steroids, macrocycles and peptides as novel scaffolds with the potential to modulate CK2.Communicated by Ramaswamy H. Sarma.

Release of microplastics from disposable face mask in tropical climate.

Outbreak of COVID 19 has caused an abrupt surge in the consumption of disposable face masks around the world. WHO has stated that wearing a face mask in public reduces the chances of being exposed to COVID 19 virus. With unchecked disposal of these used masks, a new kind of pollutant has emerged in the environment. Since these masks are generally made of polypropylene and polyurethane material, they can be considered as a potential source of microplastics (MPs) in the environment. In this study, we have evaluated the release of MPs particles from these face masks (namely from N95 and surgical masks) in deionized (DI) water and tap water over the span of 1 to 180 days. More specifically, a systematic study has been carried out to see the effect of temperature on release of MPs in water. MPs particles released in tap water (837 ± 113 particles/piece in 30 days) were significantly higher than that in DI water (564 ± 37 particles/piece in 30 days). When these masks were kept at a constant temperature of 45 °C for 30 Days, highest amount of MPs release (N95 899 ± 65 particles, Surgical 1038 ± 65 particles/piece) was observed as compared to other conditions. Most of the MPs particles released were polypropylene which were transparent and white in case of N95 while for surgical mask they were found to be of blue and white colour. With the aging of masks in water, quantity of MPs release was increased with simultaneous reduction in their size. Our study indicates that these disposable face masks are emerging to be a prominent source of MPs release in the environment and more hazardous for the tropical climate.

Natural selection plays a significant role in governing the codon usage bias in the novel SARS-CoV-2 variants of concern (VOC).

The ongoing prevailing COVID-19 pandemic caused by SARS-CoV-2 is becoming one of the major global health concerns worldwide. The SARS-CoV-2 genome encodes spike (S) glycoprotein that plays a very crucial role in viral entry into the host cell via binding of its receptor binding domain (RBD) to the host angiotensin converting enzyme 2 (ACE2) receptor. The continuously evolving SARS-CoV-2 genome results in more severe and transmissible variants characterized by the emergence of novel mutations called 'variants of concern' (VOC). The currently designated alpha, beta, gamma, delta and omicron VOC are the focus of this study due to their high transmissibility, increased virulence, and concerns for decreased effectiveness of the available vaccines. In VOC, the spike (S) gene and other non-structural protein mutations may affect the efficacies of the approved COVID-19 vaccines. To understand the diversity of SARS-CoV-2, several studies have been performed on a limited number of sequences. However, only a few studies have focused on codon usage bias (CUBs) pattern analysis of all the VOC strains. Therefore, to evaluate the evolutionary divergence of all VOC S-genes, we performed CUBs analysis on 300,354 sequences to understand the evolutionary relationship with its adaptation in different hosts, i.e., humans, bats, and pangolins. Base composition and RSCU analysis revealed the presence of 20 preferred AU-ended and 10 under-preferred GC-ended codons. In addition, CpG was found to be depleted, which may be attributable to the adaptive response by viruses to escape from the host defense process. Moreover, the ENC values revealed a higher bias in codon usage in the VOC S-gene. Further, the neutrality plot analysis demonstrated that S-genes analyzed in this study are under 83.93% influence of natural selection, suggesting its pivotal role in shaping the CUBs. The CUBs pattern of S-genes was found to be very similar among all the VOC strains. Interestingly, we observed that VOC strains followed a trend of antagonistic codon usage with respect to the human host. The identified CUBs divergence would help to understand the virus evolution and its host adaptation, thus help design novel vaccine strategies against the emerging VOC strains. To the best of our knowledge, this is the first report for identifying the evolution of CUBs pattern in all the currently identified VOC.

A machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys.

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used.

Insights into the structure and dynamics of SARS-CoV-2 spike glycoprotein double mutant L452R-E484Q.

The Receptor Binding Domain (RBD) of SARS-CoV-2, located on the S1 subunit, plays a vital role in the virus binding and its entry into the host cell through angiotensin-converting enzyme 2 (ACE2) receptor. Therefore, understanding the dynamic effects of mutants on the SARS-CoV-2 RBD is essential for discovering drugs to inhibit the virus binding and disrupt its entry into the host cells. A recent study reported a double mutant of SARS-CoV-2, L452R-E484Q, located in the RBD region. Thus, this study employed various computational algorithms and methods to understand the structural impact of both individual variants L452R, E484Q, and the double mutant L452R-E484Q on the native RBD of spike glycoprotein. The effects of the mutations on native RBD structure were predicted by in silico algorithms, which predicted changes in the protein structure and function upon the mutations. Subsequently, molecular dynamics (MD) simulations were employed to understand the conformational stability and functional changes on the RBD upon the mutations. The comparative results of MD simulation parameters displayed that the double mutant induces significant conformational changes in the spike glycoprotein RBD, which may alter its biological functions. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-022-03151-0.

Anti-Fungal Drug Anidulafungin Inhibits SARS-CoV-2 Spike-Induced Syncytia Formation by Targeting ACE2-Spike Protein Interaction.

Drug repositioning continues to be the most effective, practicable possibility to treat COVID-19 patients. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus enters target cells by binding to the ACE2 receptor via its spike (S) glycoprotein. We used molecular docking-based virtual screening approaches to categorize potential antagonists, halting ACE2-spike interactions by utilizing 450 FDA-approved chemical compounds. Three drug candidates (i.e., anidulafungin, lopinavir, and indinavir) were selected, which show high binding affinity toward the ACE2 receptor. The conformational stability of selected docked complexes was analyzed through molecular dynamics (MD) simulations. The MD simulation trajectories were assessed and monitored for ACE2 deviation, residue fluctuation, the radius of gyration, solvent accessible surface area, and free energy landscapes. The inhibitory activities of the selected compounds were eventually tested in-vitro using Vero and HEK-ACE2 cells. Interestingly, besides inhibiting SARS-CoV-2 S glycoprotein induced syncytia formation, anidulafungin and lopinavir also blocked S-pseudotyped particle entry into target cells. Altogether, anidulafungin and lopinavir are ranked the most effective among all the tested drugs against ACE2 receptor-S glycoprotein interaction. Based on these findings, we propose that anidulafungin is a novel potential drug targeting ACE2, which warrants further investigation for COVID-19 treatment.

Targeting cathepsins: A potential link between COVID-19 and associated neurological manifestations.

Many studies have shown that the lysosomal cathepsins, especially cathepsins B/L (CTSB/L) are required for SARS-CoV-2 entry into host cells. Lysosomal proteases, cathepsins are indispensable for normal health and are involved in several brain disorders occurring at different development age periods. On the other hand, it has been well known that COVID-19 infection is largely associated with several neurological disorders. Taken together these findings and given the high levels of expression of CTSB/L in the brain, we here proposed a reasonable hypothesis about the involvement of CTSB/L in the neurological manifestations linked to COVID-19. Pharmacological inhibitions of the CTSB/L could be a potential therapeutic target to block the virus entry as well as to mitigate the brain disorders. To this end, we utilized the network-based drug repurposing analyses to identify the possible drugs that can target CTSB/L. This study identifies the molecules like cyclosporine, phenytoin, and paclitaxel as potential drugs with binding ability to the CTSB/L. Further, we have performed molecular docking and all-atom molecular dynamics (MD) simulations to investigate the stability of CTSL-drug complexes. The results showed strong and stable binding of drugs with CTSL.

Identification of COVID-19 prognostic markers and therapeutic targets through meta-analysis and validation of Omics data from nasopharyngeal samples.

BACKGROUND: While our battle with the COVID-19 pandemic continues, a multitude of Omics data have been generated from patient samples in various studies. Translation of these data into clinical interventions against COVID-19 remains to be accomplished. Exploring host response to COVID-19 in the upper respiratory tract can unveil prognostic markers and therapeutic targets. METHODS: We conducted a meta-analysis of published transcriptome and proteome profiles of respiratory samples of COVID-19 patients to shortlist high confidence upregulated host factors. Subsequently, mRNA overexpression of selected genes was validated in nasal swabs from a cohort of COVID-19 positive/negative, symptomatic/asymptomatic individuals. Guided by this analysis, we sought to check for potential drug targets. An FDA-approved drug, Auranofin, was tested against SARS-CoV-2 replication in cell culture and Syrian hamster challenge model. FINDINGS: The meta-analysis and validation in the COVID-19 cohort revealed S100 family genes (S100A6, S100A8, S100A9, and S100P) as prognostic markers of severe COVID-19. Furthermore, Thioredoxin (TXN) was found to be consistently upregulated. Auranofin, which targets Thioredoxin reductase, was found to mitigate SARS-CoV-2 replication in vitro. Furthermore, oral administration of Auranofin in Syrian hamsters in therapeutic as well as prophylactic regimen reduced viral replication, IL-6 production, and inflammation in the lungs. INTERPRETATION: Elevated mRNA level of S100s in the nasal swabs indicate severe COVID-19 disease, and FDA-approved drug Auranofin mitigated SARS-CoV-2 replication in preclinical hamster model. FUNDING: This study was supported by the DBT-IISc partnership program (DBT (IED/4/2020-MED/DBT)), the Infosys Young Investigator award (YI/2019/1106), DBT-BIRAC grant (BT/CS0007/CS/02/20) and the DBT-Wellcome Trust India Alliance Intermediate Fellowship (IA/I/18/1/503613) to ST lab.

Machine Learning Assisted Prediction of Prognostic Biomarkers Associated With COVID-19, Using Clinical and Proteomics Data.

With the availability of COVID-19-related clinical data, healthcare researchers can now explore the potential of computational technologies such as artificial intelligence (AI) and machine learning (ML) to discover biomarkers for accurate detection, early diagnosis, and prognosis for the management of COVID-19. However, the identification of biomarkers associated with survival and deaths remains a major challenge for early prognosis. In the present study, we have evaluated and developed AI-based prediction algorithms for predicting a COVID-19 patient's survival or death based on a publicly available dataset consisting of clinical parameters and protein profile data of hospital-admitted COVID-19 patients. The best classification model based on clinical parameters achieved a maximum accuracy of 89.47% for predicting survival or death of COVID-19 patients, with a sensitivity and specificity of 85.71 and 92.45%, respectively. The classification model based on normalized protein expression values of 45 proteins achieved a maximum accuracy of 89.01% for predicting the survival or death, with a sensitivity and specificity of 92.68 and 86%, respectively. Interestingly, we identified 9 clinical and 45 protein-based putative biomarkers associated with the survival/death of COVID-19 patients. Based on our findings, few clinical features and proteins correlate significantly with the literature and reaffirm their role in the COVID-19 disease progression at the molecular level. The machine learning-based models developed in the present study have the potential to predict the survival chances of COVID-19 positive patients in the early stages of the disease or at the time of hospitalization. However, this has to be verified on a larger cohort of patients before it can be put to actual clinical practice. We have also developed a webserver CovidPrognosis, where clinical information can be uploaded to predict the survival chances of a COVID-19 patient. The webserver is available at http://14.139.62.220/covidprognosis/.

Insights from a Pan India Sero-Epidemiological survey (Phenome-India Cohort) for SARS-CoV2.

To understand the spread of SARS-CoV2, in August and September 2020, the Council of Scientific and Industrial Research (India) conducted a serosurvey across its constituent laboratories and centers across India. Of 10,427 volunteers, 1058 (10.14%) tested positive for SARS-CoV2 anti-nucleocapsid (anti-NC) antibodies, 95% of which had surrogate neutralization activity. Three-fourth of these recalled no symptoms. Repeat serology tests at 3 (n = 607) and 6 (n = 175) months showed stable anti-NC antibodies but declining neutralization activity. Local seropositivity was higher in densely populated cities and was inversely correlated with a 30-day change in regional test positivity rates (TPRs). Regional seropositivity above 10% was associated with declining TPR. Personal factors associated with higher odds of seropositivity were high-exposure work (odds ratio, 95% confidence interval, p value: 2.23, 1.92-2.59, <0.0001), use of public transport (1.79, 1.43-2.24, <0.0001), not smoking (1.52, 1.16-1.99, 0.0257), non-vegetarian diet (1.67, 1.41-1.99, <0.0001), and B blood group (1.36, 1.15-1.61, 0.001).

Structural stability predictions and molecular dynamics simulations of RBD and HR1 mutations associated with SARS-CoV-2 spike glycoprotein.

The COVID-19 pandemic is caused by human transmission and infection of Severe Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV-2). There is no trusted drug against the virus; hence, efforts are on discovering novel inhibitors for the virus. The entry of a SARS-CoV-2 virus particle into a host cell is initiated by its spike glycoprotein and host Angiotensin-Converting Enzyme 2 (ACE2) receptor interaction. Spike glycoprotein domains, namely, the Receptor Binding Domain (RBD) and Heptad Repeat (HR) domains, are essential for this activity. We have studied the impact of mutations such as A348T, N354D, D364Y, G476S, V483A, S494D in the RBD (319-591), and S939F, S940T, T941A, S943P (912-984) in the HR1 domains of spike glycoprotein. Summarily, we utilized the computational screening algorithms to rank the deleterious, damaging and disease-associated spike glycoprotein mutations. Subsequently, to understand the changes in conformation, flexibility and function of the spike glycoprotein mutants, Molecular Dynamics (MD) simulations were performed. The computational predictions and analysis of the MD trajectories suggest that the RBD and HR1 mutations induce significant phenotypic effects on the pre-binding spike glycoprotein structure, which are presumably consequential to its binding to the receptor and provides lead to design inhibitors against the binding.Communicated by Ramaswamy H. Sarma.

Simultaneous Inhibition of SARS-CoV-2 Entry Pathways by Cyclosporine.

The COVID-19 pandemic caused by SARS-CoV-2 represents a global public health emergency. The entry of SARS-CoV-2 into host cells requires the activation of its spike protein by host cell proteases. The serine protease, TMPRSS2, and cysteine proteases, Cathepsins B/L, activate spike protein and enable SARS-CoV-2 entry to the host cell through two completely different and independent pathways. Therefore, inhibiting either TMPRSS2 or cathepsin B/L may not sufficiently block the virus entry. We here hypothesized that simultaneous targeting of both the entry pathways would be more efficient to block the virus entry rather than targeting the entry pathways individually. To this end, we utilized the network-based drug repurposing analyses to identify the possible common drugs that can target both the entry pathways. This study, for the first time, reports the molecules like cyclosporine, calcitriol, and estradiol as candidate drugs with the binding ability to the host proteases, TMPRSS2, and cathepsin B/L. Next, we analyzed drug-gene and gene-gene interaction networks using 332 human targets of SARS-CoV-2 proteins. The network results indicate that, out of 332 human proteins, cyclosporine interacts with 216 (65%) proteins. Furthermore, we performed molecular docking and all-atom molecular dynamics (MD) simulations to explore the binding of drug with TMPRSS2 and cathepsin L. The molecular docking and MD simulation results showed strong and stable binding of cyclosporine A (CsA) with TMPRSS2 and CTSL genes. The above results indicate cyclosporine as a potential drug molecule, as apart from interacting with SARS-CoV-2 entry receptors, it also interacts with most of SARS-CoV-2 target host genes; thus it could potentially interfere with functions of SARS-CoV-2 proteins in human cells. We here also suggest that these antiviral drugs alone or in combination can simultaneously target both the entry pathways and thus can be considered as a potential treatment option for COVID-19.

Screening Malaria-box compounds to identify potential inhibitors against SARS-CoV-2 Mpro, using molecular docking and dynamics simulation studies.

Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) Main protease (Mpro) is one of the vital drug targets amongst all the coronaviruses, as the protein is indispensable for virus replication. The study aimed to identify promising lead molecules against Mpro enzyme through virtual screening of Malaria Venture (MMV) Malaria Box (MB) comprising of 400 experimentally proven compounds. The binding affinities were studied using virtual screening based molecular docking, which revealed five molecules having the highest affinity scores compared to the reference molecules. Utilizing the established 3D structure of Mpro the binding affinity conformations of the docked complexes were studied by Molecular Dynamics (MD) simulations. The MD simulation trajectories were analysed to monitor protein deviation, relative fluctuation, atomic gyration, compactness covariance, residue-residue map and free energy landscapes. Based on the present study outcome, we propose three Malaria_box (MB) compounds, namely, MB_241, MB_250 and MB_266 to be the best lead compounds against Mpro activity. The compounds may be evaluated for their inhibitory activities using experimental techniques.

Identification of Novel SARS-CoV-2 Drug Targets by Host MicroRNAs and Transcription Factors Co-regulatory Interaction Network Analysis.

Understanding the host regulatory mechanisms opposing virus infection and virulence can provide actionable insights to identify novel therapeutics against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We have used a network biology approach to elucidate the crucial factors involved in host responses involving host-microRNA (miRNA) interactions with host and virus genes using recently published experimentally verified protein-protein interaction data. We were able to identify 311 host genes to be potentially targetable by 2,197 human miRNAs. These miRNAs are known to be involved in various biological processes, such as T-cell differentiation and activation, virus replication, and immune system. Among these, the anti-viral activity of 38 miRNAs to target 148 host genes is experimentally validated. Six anti-viral miRNAs, namely, hsa-miR-1-3p, hsa-miR-17-5p, hsa-miR-199a-3p, hsa-miR-429, hsa-miR-15a-5p, and hsa-miR-20a-5p, are previously reported to be anti-viral in respiratory diseases and were found to be downregulated. The interaction network of the 2,197 human miRNAs and interacting transcription factors (TFs) enabled the identification of 51 miRNAs to interact with 77 TFs inducing activation or repression and affecting gene expression of linked genes. Further, from the gene regulatory network analysis, the top five hub genes HMOX1, DNMT1, PLAT, GDF1, and ITGB1 are found to be involved in interferon (IFN)-α2b induction, epigenetic modification, and modulation of anti-viral activity. The comparative miRNAs target identification analysis in other respiratory viruses revealed the presence of 98 unique host miRNAs targeting SARS-CoV-2 genome. Our findings identify prioritized key regulatory interactions that include miRNAs and TFs that provide opportunities for the identification of novel drug targets and development of anti-viral drugs.

Targeting SARS-CoV-2 nucleocapsid oligomerization: Insights from molecular docking and molecular dynamics simulations.

The outbreak of COVID-19 caused by SARS-CoV-2 virus continually led to infect a large population worldwide. Currently, there is no specific viral protein-targeted therapeutics. The Nucleocapsid (N) protein of the SARS-CoV-2 virus is necessary for viral RNA replication and transcription. The C-terminal domain of N protein (CTD) involves in the self-assembly of N protein into a filament that is packaged into new virions. In this study, the CTD (PDB ID: 6WJI) was targeted for the identification of possible inhibitors of oligomerization of N protein. Herein, multiple computational approaches were employed to explore the potential mechanisms of binding and inhibitor activity of five antiviral drugs toward CTD. The five anti-N drugs studied in this work are 4E1RCat, Silmitasertib, TMCB, Sapanisertib, and Rapamycin. Among the five drugs, 4E1RCat displayed highest binding affinity (-10.95 kcal/mol), followed by rapamycin (-8.91 kcal/mol), silmitasertib (-7.89 kcal/mol), TMCB (-7.05 kcal/mol), and sapanisertib (-6.14 kcal/mol). Subsequently, stability and dynamics of the protein-drug complex were examined with molecular dynamics (MD) simulations. Overall, drug binding increases the stability of the complex with maximum stability observed in the case of 4E1RCat. The CTD-drug complex systems behave differently in terms of the free energy landscape and showed differences in population distribution. Overall, the MD simulation parameters like RMSD, RMSF, Rg, hydrogen bonds analysis, PCA, FEL, and DCCM analysis indicated that 4E1RCat and TMCB complexes were more stable as compared to silmitasertib and sapanisertib and thus could act as effective drug compounds against CTD.Communicated by Ramaswamy H. Sarma.

Artificial Intelligence-Based Classification of Chest X-Ray Images into COVID-19 and Other Infectious Diseases.

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has led to global health and healthcare crisis, apart from the tremendous socioeconomic effects. One of the significant challenges in this crisis is to identify and monitor the COVID-19 patients quickly and efficiently to facilitate timely decisions for their treatment, monitoring, and management. Research efforts are on to develop less time-consuming methods to replace or to supplement RT-PCR-based methods. The present study is aimed at creating efficient deep learning models, trained with chest X-ray images, for rapid screening of COVID-19 patients. We used publicly available PA chest X-ray images of adult COVID-19 patients for the development of Artificial Intelligence (AI)-based classification models for COVID-19 and other major infectious diseases. To increase the dataset size and develop generalized models, we performed 25 different types of augmentations on the original images. Furthermore, we utilized the transfer learning approach for the training and testing of the classification models. The combination of two best-performing models (each trained on 286 images, rotated through 120° or 140° angle) displayed the highest prediction accuracy for normal, COVID-19, non-COVID-19, pneumonia, and tuberculosis images. AI-based classification models trained through the transfer learning approach can efficiently classify the chest X-ray images representing studied diseases. Our method is more efficient than previously published methods. It is one step ahead towards the implementation of AI-based methods for classification problems in biomedical imaging related to COVID-19.

Dataset of mutational analysis, miRNAs targeting SARS-CoV-2 genes and host gene expression in SARS-CoV and SARS-CoV-2 infections.

The identification of host-miRNAs targeting mutated virus genes is crucial to understand the miRNA mediated host-defense mechanism in virus infections. To understand the mechanism in COVID-19 infections, we collected genome sequences of SARS-CoV-2 with its metadata from the GISAID database (submitted till April 2020) and identified mutational changes in the sequences. The dataset consists of genes with mutation event count and entropy scores. We predicted host-miRNAs targeting the genes in the genomes and compared it with that in related viral species. We have identified 2284 miRNAs targeting MERS genomes, 2074 miRNAs targeting SARS genomes, and 1599 miRNAs targeting SARS-CoV-2 genomes, identified using the miRNA target prediction software miRanda. The host miRNAs targeting SARS-CoV-2 genes were further validated to be anti-viral miRNAs and their role in respiratory diseases through a literature survey, which helped in the identification of 42 conserved antiviral miRNAs. The data could be used to validate the anti-viral role of the predicted miRNAs and design miRNA-based therapeutics against SARS-CoV-2.

Integrative analyses of SARS-CoV-2 genomes from different geographical locations reveal unique features potentially consequential to host-virus interaction, pathogenesis and clues for novel therapies.

We have performed an integrative analysis of SARS-CoV-2 genome sequences from different countries. Apart from mutational analysis, we have predicted host antiviral miRNAs targeting virus genes, PTMs in the virus proteins and antiviral peptides. A comparison of the analyses with other coronavirus genomes has been performed, wherever possible. Our analysis confirms unique features in the SARS-CoV-2 genomes absent in other evolutionarily related coronavirus family genomes, which presumably confer unique infection, transmission and virulence capabilities to the virus. For understanding the crucial factors involved in host-virus interactions, we have performed Bioinformatics aided analysis integrated with experimental data related to other corona viruses. We have identified 42 conserved miRNAs that can potentially target SARS-CoV-2 genomes. Interestingly, out of these, 3 are previously reported to exhibit antiviral activity against other respiratory viruses. Gene expression analysis of known host antiviral factors reveals significant over-expression of IFITM3 and down regulation of cathepsins during SARS-CoV-2 infection, suggesting its active role in pathogenesis and delayed immune response. We also predicted antiviral peptides which can be used in designing peptide based drugs against SARS-CoV-2. Our analysis explores the functional impact of the virus mutations on its proteins and interaction of its genes with host antiviral mechanisms.

Insights into the structural and dynamical changes of spike glycoprotein mutations associated with SARS-CoV-2 host receptor binding

Novel Coronavirus or SARS-CoV-2 has received worldwide attention due to the COVID-19 pandemic, which originated in Wuhan, China leading to thousands of deaths to date. The SARS-CoV-2 Spike glycoprotein protein is one of the main focus of COVID-19 related research as it is a structural protein that facilitates its attachment, entry, and infection to the host cells. We have focused our work on mutations in two of the several functional domains in the virus spike glycoprotein, namely, receptor-binding domain (RBD) and heptad repeat 1 (HR1) domain. These domains are majorly responsible for the stability of spike glycoprotein and play a key role in the host cell attachment and infection. In our study, several mutations like R408I, L455Y, F486L, Q493N, Q498Y, N501T of RBD (319-591), and A930V, D936Y of HR1 (912-984) have been studied to examine its role on the spike glycoprotein native structure. Comparisons of MD simulations in the WT and mutants revealed a significant de-stabilization effect of the mutations on RBD and HR1 domains. We have investigated the impact of mapped mutations on the stability of the spike glycoprotein, before binding to the receptor, which may be consequential to its binding properties to the receptor and other ligands. Communicated by Ramaswamy H. Sarma.