Prediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapies

Deep learning for discriminating non-trivial conformational changes in molecular dynamics simulations of SARS-CoV-2 spike-ACE2

Molecular dynamics (MD) simulations produce a substantial volume of high-dimensional data, and traditional methods for analyzing these data pose significant computational demands. Advances in MD simulation analysis combined with deep learning-based approaches have led to the understanding of specific structural changes observed in MD trajectories, including those induced by mutations. In this study, we model the trajectories resulting from MD simulations of the SARS-CoV-2 spike protein-ACE2, specifically the receptor-binding domain (RBD), as interresidue distance maps, and use deep convolutional neural networks to predict the functional impact of point mutations, related to the virus's infectivity and immunogenicity. Our model was successful in predicting mutant types that increase the affinity of the S protein for human receptors and reduce its immunogenicity, both based on MD trajectories (precision = 0.718; recall = 0.800; [Formula: see text] = 0.757; MCC = 0.488; AUC = 0.800) and their centroids. In an additional analysis, we also obtained a strong positive Pearson's correlation coefficient equal to 0.776, indicating a significant relationship between the average sigmoid probability for the MD trajectories and binding free energy (BFE) changes. Furthermore, we obtained a coefficient of determination of 0.602. Our 2D-RMSD analysis also corroborated predictions for more infectious and immune-evading mutants and revealed fluctuating regions within the receptor-binding motif (RBM), especially in the [Formula: see text] loop. This region presented a significant standard deviation for mutations that enable SARS-CoV-2 to evade the immune response, with RMSD values of 5Å in the simulation. This methodology offers an efficient alternative to identify potential strains of SARS-CoV-2, which may be potentially linked to more infectious and immune-evading mutations. Using clustering and deep learning techniques, our approach leverages information from the ensemble of MD trajectories to recognize a broad spectrum of multiple conformational patterns characteristic of mutant types. This represents a strategic advantage in identifying emerging variants, bypassing the need for long MD simulations. Furthermore, the present work tends to contribute substantially to the field of computational biology and virology, particularly to accelerate the design and optimization of new therapeutic agents and vaccines, offering a proactive stance against the constantly evolving threat of COVID-19 and potential future pandemics.

Preventing future zoonosis: SARS-CoV-2 mutations enhance human-animal cross-transmission

The COVID-19 pandemic has driven substantial evolution of the SARS-CoV-2 virus, yielding subvariants that exhibit enhanced infectiousness in humans. However, this adaptive advantage may not universally extend to zoonotic transmission. In this work, we hypothesize that viral adaptations favoring animal hosts do not necessarily correlate with increased human infectivity. In addition, we consider the potential for gain-of-function mutations that could facilitate the virus's rapid evolution in humans following adaptation in animal hosts. Specifically, we identify the SARS-CoV-2 receptor-binding domain (RBD) mutations that enhance human-animal cross-transmission. To this end, we construct a multitask deep learning model, MT-TopLap trained on multiple deep mutational scanning datasets, to accurately predict the binding free energy changes upon mutation for the RBD to ACE2 of various species, including humans, cats, bats, deer, and hamsters. By analyzing these changes, we identified key RBD mutations such as Q498H in SARS-CoV-2 and R493K in the BA.2 variant that are likely to increase the potential for human-animal cross-transmission.

SARS-CoV-2 infectivity and antigenic evasion: spotlight on isolated Omicron sub-lineages

Since the SARS-CoV-2 outbreak in 2019, a diversity of viral genomic variants has emerged and spread globally due to increased transmissibility, pathogenicity, and immune evasion. By the first trimester of 2023 in Chile, as in most countries, BQ and XBB were the predominant circulating sub-lineages of Omicron. The molecular and antigenic characteristics of these variants have been mainly determined using non-authentic spike pseudoviruses, which is often described as a limitation. Additionally, few comparative studies using isolates from recent Omicron sub-lineages have been conducted. In this study, we isolated SARS-CoV-2 variants from clinical samples, including the ancestral B.1.1, Delta, Omicron BA.1, and sub-lineages of BA.2 and BA.5. We assessed their infectivity through cell culture infections and their antibody evasion using neutralization assays. We observed variations in viral plaque size, cell morphology, and cytotoxicity upon infection in Vero E6-TMPRSS2 cells for each variant compared to the ancestral B.1.1 virus. BA.2-derived sub-variants, such as XBB.1.5, showed attenuated viral replication, while BA.5-derived variants, such as BQ.1.1, exhibited replication rates similar to the ancestral SARS-CoV-2 virus. Similar trends were observed in intestinal Caco-2 cells, except for Delta. Antibody neutralization experiments using sera from individuals infected during the first COVID-19 wave (FWI) showed a consistent but moderate reduction in neutralization against Omicron sub-lineages. Interestingly, despite being less prevalent, BQ.1.1 showed a 6.1-fold greater escape from neutralization than XBB.1.5. Neutralization patterns were similar when tested against sera from individuals vaccinated with 3xBNT162b2 (PPP) or Coronavac-Coronavac-BNT162b2 (CCP) schedules. However, CCP sera showed 2.3-fold higher neutralization against XBB.1.5 than FWI and PPP sera. This study provides new insights into the differences between BA.2 and BA.5-derived variants, leading to their eventual outcompetition. Our analysis offers important evidence regarding the balance between infectivity and antigenic escape that drives the evolution of second-generation SARS-CoV-2 variants in the population.

Reduced Viral Shedding Time in High-Risk COVID-19 Patients Infected by Omicron and Treated with Paxlovid: A Real-World Study from China

Introduction

The purpose of this study was to compare the viral shedding time in patients infected with the Omicron variant during Paxlovid therapy and conventional therapy and to analyze the effects of Paxlovid on patients infected with COVID-19.

Methods

In this study, the demographic and clinical characteristics and laboratory data of 3159 patients infected with the SARS-CoV-2 Omicron variant treated at Jilin Province People's Hospital were collected and analyzed. A total of 362 patients received Paxlovid therapy, and 2797 patients received conventional therapy. After propensity score matching (PSM), 1086 patients were obtained.

Results

The difference in platelet (PLT) count between the two groups was statistically significant but within the normal range (P < 0.05). CT value revealed that the nucleic acid test results became negative more quickly in the Paxlovid therapy group. Analysis of the Paxlovid therapy group showed that IgG and IgM levels were increased after Paxlovid therapy administration.

Conclusion

The CT value of the Paxlovid therapy group became negative more quickly. This finding suggests that Paxlovid treatment after early diagnosis of the Omicron variant may achieve good therapeutic efficacy.

A review of the clinical characteristics and management of immunosuppressed patients living with HIV or solid organ transplants infected with SARS-CoV-2 omicron variants

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron strain was first detected in South Africa in November 2021. Although clinical responses to SARS-CoV-2 depend on host immunity, it remains uncertain how immunosuppression affects subsequent coronavirus disease 2019-related (COVID-19-related) incidence, severity, and mortality, especially with respect to the omicron strain. Conversely, immunosuppressants are often thought to predispose to infection. To explore the associations between host immunity and infection with SARS-CoV-2 omicron variants, here we discuss two groups of immunosuppressed patients: organ transplant recipients, who generally receive exogenous immunosuppressants, and Human Immunodeficiency Virus (HIV)-infected patients, who often have disease-related immunosuppression. In summarizing the clinical features and prognoses of HIV-infected patients and human organ transplant recipients infected with SARS-CoV-2 omicron variants, we provide new insights into the pathogenesis of omicron SARS-CoV-2 and provide a framework for the management of these patients now and in the future.

Changing epidemiology of COVID-19: potential future impact on vaccines and vaccination strategies

INTRODUCTION

COVID-19 was an unprecedented challenge worldwide; however, disease epidemiology has evolved, and COVID-19 no longer constitutes a public health emergency of international concern. Nonetheless, COVID-19 remains a global threat and uncertainties remain, including definition of the end of the pandemic and transition to endemicity, and understanding true rates of SARS-CoV-2 infection/transmission.

AREAS COVERED

Six international experts convened (April 2023) to interpret changing COVID-19 epidemiology and public health challenges. We report the panel's recommendations and knowledge gaps in COVID-19 epidemiology, SARS-CoV-2 evolution, and future vaccination strategies, informed by peer-reviewed publications, surveillance data, health authority assessments, and clinical experience.

EXPERT OPINION

High population SARS-CoV-2 immunity indicates the likely end to the pandemic's acute phase. Continued emergence of variants/sublineages that can evade the vaccine-induced antibody response are likely, but widespread immunity reduces the risk of disease severity. Continued surveillance is required to capture transition to endemicity, seasonality, and emergence of novel variants/sublineages, to inform future vaccination strategies. COVID-19 vaccination should be integrated into routine vaccination programs throughout life. Co-circulation with other respiratory viruses should be monitored to avoid a combined peak, which could overrun healthcare systems. Effective, combined vaccines and improved education may help overcome vaccine hesitancy/booster fatigue and increase vaccination uptake.

Lived experience of Iranian pre-hospital medical staff during the COVID-19 pandemic: a descriptive phenomenological study

Background

Pre-hospital medical staff faced numerous challenges during the COVID-19 pandemic. However, these challenges specific to pre-hospital services have not been thoroughly explored in Iran. This qualitative study aimed to examine the essence of pre-hospital care during the COVID-19 pandemic.

Methods

This phenomenological study was conducted from June to August 2021 in Tehran, Iran. Semi-structured interviews were conducted with pre-hospital medical staff. Data analysis was performed using Colaizzi's approach, and rigor was ensured by adhering to the consolidated criteria for qualitative reporting research.

Results

A total of 17 pre-hospital medical staff were interviewed, and five themes were extracted from the data: workload and resilience, damage, lack of control, under preparedness, and post-traumatic growth. These themes highlight the resilience demonstrated by pre-hospital medical staff, who faced an unprecedented crisis with limited preparedness and significant damage.

Conclusion

The findings of this study indicate that pre-hospital medical staff in Iran encountered challenges during the COVID-19 pandemic due to a lack of preparedness and substantial damage. Despite these adversities, the participants exhibited resilience and experienced post-traumatic growth. The study emphasizes the importance of proper planning and preparedness to enhance the resilience of emergency medical services during pandemics. Furthermore, the results underscore the need to address the challenges faced by pre-hospital medical staff and improve the quality of care provided to patients during crises such as the COVID-19 pandemic.

Computational approaches for identifying disease-causing mutations in proteins

Advancements in genome sequencing have expanded the scope of investigating mutations in proteins across different diseases. Amino acid mutations in a protein alter its structure, stability and function and some of them lead to diseases. Identification of disease-causing mutations is a challenging task and it will be helpful for designing therapeutic strategies. Hence, mutation data available in the literature have been curated and stored in several databases, which have been effectively utilized for developing computational methods to identify deleterious mutations (drivers), using sequence and structure-based properties of proteins. In this chapter, we describe the contents of specific databases that have information on disease-causing and neutral mutations followed by sequence and structure-based properties. Further, characteristic features of disease-causing mutations will be discussed along with computational methods for identifying cancer hotspot residues and disease-causing mutations in proteins.

Next-Generation Rapid and Ultrasensitive Lateral Flow Immunoassay for Detection of SARS-CoV-2 Variants

The coronavirus disease 2019 (COVID-19) pandemic highlighted the need for rapid and accurate viral detection at the point-of-care testing (POCT). Compared with nucleic acid detection, lateral flow immunoassay (LFIA) is a rapid and flexible method for POCT detection. However, the sensitivity of LFIA limits its use for early identification of patients with COVID-19. Here, an innovative surface-enhanced Raman scattering (SERS)-LFIA platform based on two-dimensional black phosphorus decorated with Ag nanoparticles as important antigen-capturing and Raman-signal-amplification unit was developed for detection of SARS-CoV-2 variants within 5-20 min. The novel SERS-LFIA platform realized a limit of detection of 0.5 pg/mL and 100 copies/mL for N protein and SARS-CoV-2, demonstrating 1000 times more sensitivity than the commercial LFIA strips. It could reliably detect seven different SARS-CoV-2 variants with cycle threshold (Ct) < 38, with sensitivity and specificity of 97 and 100%, respectively, exhibiting the same sensitivity with q-PCR. Furthermore, the detection results for 48 SARS-CoV-2-positive nasopharyngeal swabs (Ct = 19.8-38.95) and 96 negative nasopharyngeal swabs proved the reliability of the strips in clinical application. The method also had good specificity in double-blind experiments involving several other coronaviruses, respiratory viruses, and respiratory medications. The results showed that the innovative SERS-LFIA platform is expected to be the next-generation antigen detection technology. The inexpensive amplification-free assay combines the advantages of rapid low-cost POCT and highly sensitive nucleic acid detection, and it is suitable for rapid detection of SARS-CoV-2 variants and other pathogens. Thus, it could replace existing antigens and nucleic acids to some extent.

Structure, dynamics and free energy studies on the effect of point mutations on SARS-CoV-2 spike protein binding with ACE2 receptor

The ongoing COVID-19 pandemic continues to infect people worldwide, and the virus continues to evolve in significant ways which can pose challenges to the efficiency of available vaccines and therapeutic drugs and cause future pandemic. Therefore, it is important to investigate the binding and interaction of ACE2 with different RBD variants. A comparative study using all-atom MD simulations was conducted on ACE2 binding with 8 different RBD variants, including N501Y, E484K, P479S, T478I, S477N, N439K, K417N and N501Y-E484K-K417N on RBD. Based on the RMSD, RMSF, and DSSP results, overall the binding of RBD variants with ACE2 is stable, and the secondary structure of RBD and ACE2 are consistent after the point mutation. Besides that, a similar buried surface area, a consistent binding interface and a similar amount of hydrogen bonds formed between RBD and ACE2 although the exact residue pairs on the binding interface were modified. The change of binding free energy from point mutation was predicted using the free energy perturbation (FEP) method. It is found that N501Y, N439K, and K417N can strengthen the binding of RBD with ACE2, while E484K and P479S weaken the binding, and S477N and T478I have negligible effect on the binding. Point mutations modified the dynamic correlation of residues in RBD based on the dihedral angle covariance matrix calculation. Doing dynamic network analysis, a common intrinsic network community extending from the tail of RBD to central, then to the binding interface region was found, which could communicate the dynamics in the binding interface region to the tail thus to the other sections of S protein. The result can supply unique methodology and molecular insight on studying the molecular structure and dynamics of possible future pandemics and design novel drugs.

Topological deep learning based deep mutational scanning

High-throughput deep mutational scanning (DMS) experiments have significantly impacted protein engineering, drug discovery, immunology, cancer biology, and evolutionary biology by enabling the systematic understanding of protein functions. However, the mutational space associated with proteins is astronomically large, making it overwhelming for current experimental capabilities. Therefore, alternative methods for DMS are imperative. We propose a topological deep learning (TDL) paradigm to facilitate in silico DMS. We utilize a new topological data analysis (TDA) technique based on the persistent spectral theory, also known as persistent Laplacian, to capture both topological invariants and the homotopic shape evolution of data. To validate our TDL-DMS model, we use SARS-CoV-2 datasets and show excellent accuracy and reliability for binding interface mutations. This finding is significant for SARS-CoV-2 variant forecasting and designing effective antibodies and vaccines. Our proposed model is expected to have a significant impact on drug discovery, vaccine design, precision medicine, and protein engineering.

Persistent topological Laplacian analysis of SARS-CoV-2 variants

Topological data analysis (TDA) is an emerging field in mathematics and data science. Its central technique, persistent homology, has had tremendous success in many science and engineering disciplines. However, persistent homology has limitations, including its inability to handle heterogeneous information, such as multiple types of geometric objects; being qualitative rather than quantitative, e.g., counting a 5-member ring the same as a 6-member ring, and a failure to describe non-topological changes, such as homotopic changes in protein-protein binding. Persistent topological Laplacians (PTLs), such as persistent Laplacian and persistent sheaf Laplacian, were proposed to overcome the limitations of persistent homology. In this work, we examine the modeling and analysis power of PTLs in the study of the protein structures of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike receptor binding domain (RBD). First, we employ PTLs to study how the RBD mutation-induced structural changes of RBD-angiotensin-converting enzyme 2 (ACE2) binding complexes are captured in the changes of spectra of the PTLs among SARS-CoV-2 variants. Additionally, we use PTLs to analyze the binding of RBD and ACE2-induced structural changes of various SARS-CoV-2 variants. Finally, we explore the impacts of computationally generated RBD structures on a topological deep learning paradigm and predictions of deep mutational scanning datasets for the SARS-CoV-2 Omicron BA.2 variant. Our results indicate that PTLs have advantages over persistent homology in analyzing protein structural changes and provide a powerful new TDA tool for data science.

Combining MOE Bioinformatics Analysis and In Vitro Pseudovirus Neutralization Assays to Predict the Neutralizing Ability of CV30 Monoclonal Antibody on SARS-CoV-2 Variants

Combining bioinformatics and in vitro cytology assays, a predictive method was established to quickly evaluate the protective effect of immunity acquired through SARS-CoV-2 infection against variants. Bioinformatics software was first used to predict the changes in the affinity of variant antigens to the CV30 monoclonal antibody by integrating bioinformatics and cytology assays. Then, the ability of the antibody to neutralize the variant antigen was further verified, and the ability of the CV30 to neutralize the new variant strain was predicted through pseudovirus neutralization experiments. The current study has demonstrated that when the Molecular Operating Environment (MOE) predicts |ΔBFE| ≤ 3.0003, it suggests that the CV30 monoclonal antibody exhibits some affinity toward the variant strain and can potentially neutralize it. However, if |ΔBFE| ≥ 4.1539, the CV30 monoclonal antibody does not display any affinity for the variant strain and cannot neutralize it. In contrast, if 3.0003 < |ΔBFE| < 4.1539, it is necessary to conduct a series of neutralization tests promptly with the CV30 monoclonal antibody and the variant pseudovirus to obtain results and supplement the existing method, which is faster than the typical procedures. This approach allows for a rapid assessment of the protective efficacy of natural immunity gained through SARS-CoV-2 infection against variants.

Risk Assessment of the Possible Intermediate Host Role of Pigs for Coronaviruses with a Deep Learning Predictor

Swine coronaviruses (CoVs) have been found to cause infection in humans, suggesting that Suiformes might be potential intermediate hosts in CoV transmission from their natural hosts to humans. The present study aims to establish convolutional neural network (CNN) models to predict host adaptation of swine CoVs. Decomposing of each ORF1ab and Spike sequence was performed with dinucleotide composition representation (DCR) and other traits. The relationship between CoVs from different adaptive hosts was analyzed by unsupervised learning, and CNN models based on DCR of ORF1ab and Spike were built to predict the host adaptation of swine CoVs. The rationality of the models was verified with phylogenetic analysis. Unsupervised learning showed that there is a multiple host adaptation of different swine CoVs. According to the adaptation prediction of CNN models, swine acute diarrhea syndrome CoV (SADS-CoV) and porcine epidemic diarrhea virus (PEDV) are adapted to Chiroptera, swine transmissible gastroenteritis virus (TGEV) is adapted to Carnivora, porcine hemagglutinating encephalomyelitis (PHEV) might be adapted to Primate, Rodent, and Lagomorpha, and porcine deltacoronavirus (PDCoV) might be adapted to Chiroptera, Artiodactyla, and Carnivora. In summary, the DCR trait has been confirmed to be representative for the CoV genome, and the DCR-based deep learning model works well to assess the adaptation of swine CoVs to other mammals. Suiformes might be intermediate hosts for human CoVs and other mammalian CoVs. The present study provides a novel approach to assess the risk of adaptation and transmission to humans and other mammals of swine CoVs.

High dietary iodine intake may contribute to the low death rate from COVID-19 infection in Japan with activation by the lactoperoxidase system

We draw the attention of readers and governments to the death rate from coronavirus disease 2019 in Japan, continuing as a fraction of that experienced by many other developed nations. We think this is due to the activity of the powerful, protective lactoperoxidase system (LPO) which prevents serious airborne infections. The LPO system requires iodine, which is liberally provided by the typical Japanese diet but lacking in many others. One might consider the Japanese experience an incredibly large, open-label study exhibiting the preventative power of a high-iodine diet. We predict this favourable trend will continue for Japan because deadly variants of the severe, acute respiratory syndrome coronavirus 2 will be with us, forever.

Web Resources for SARS-CoV-2 Genomic Database, Annotation, Analysis and Variant Tracking

The SARS-CoV-2 genomic data continue to grow, providing valuable information for researchers and public health officials. Genomic analysis of these data sheds light on the transmission and evolution of the virus. To aid in SARS-CoV-2 genomic analysis, many web resources have been developed to store, collate, analyze, and visualize the genomic data. This review summarizes web resources used for the SARS-CoV-2 genomic epidemiology, covering data management and sharing, genomic annotation, analysis, and variant tracking. The challenges and further expectations for these web resources are also discussed. Finally, we highlight the importance and need for continued development and improvement of related web resources to effectively track the spread and understand the evolution of the virus.

Persistent topological Laplacian analysis of SARS-CoV-2 variants

Topological data analysis (TDA) is an emerging field in mathematics and data science. Its central technique, persistent homology, has had tremendous success in many science and engineering disciplines. However, persistent homology has limitations, including its inability to handle heterogeneous information, such as multiple types of geometric objects; being qualitative rather than quantitative, e.g., counting a 5-member ring the same as a 6-member ring, and a failure to describe non-topological changes, such as homotopic changes in protein-protein binding. Persistent topological Laplacians (PTLs), such as persistent Laplacian and persistent sheaf Laplacian, were proposed to overcome the limitations of persistent homology. In this work, we examine the modeling and analysis power of PTLs in the study of the protein structures of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike receptor binding domain (RBD). First, we employ PTLs to study how the RBD mutation-induced structural changes of RBD-angiotensin-converting enzyme 2 (ACE2) binding complexes are captured in the changes of spectra of the PTLs among SARS-CoV-2 variants. Additionally, we use PTLs to analyze the binding of RBD and ACE2-induced structural changes of various SARS-CoV-2 variants. Finally, we explore the impacts of computationally generated RBD structures on a topological deep learning paradigm and predictions of deep mutational scanning datasets for the SARS-CoV-2 Omicron BA.2 variant. Our results indicate that PTLs have advantages over persistent homology in analyzing protein structural changes and provide a powerful new TDA tool for data science.

Systematic Guidelines for Effective Utilization of COVID-19 Databases in Genomic, Epidemiologic, and Clinical Research

The pandemic has led to the production and accumulation of various types of data related to coronavirus disease 2019 (COVID-19). To understand the features and characteristics of COVID-19 data, we summarized representative databases and determined the data types, purpose, and utilization details of each database. In addition, we categorized COVID-19 associated databases into epidemiological data, genome and protein data, and drug and target data. We found that the data present in each of these databases have nine separate purposes (clade/variant/lineage, genome browser, protein structure, epidemiological data, visualization, data analysis tool, treatment, literature, and immunity) according to the types of data. Utilizing the databases we investigated, we created four queries as integrative analysis methods that aimed to answer important scientific questions related to COVID-19. Our queries can make effective use of multiple databases to produce valuable results that can reveal novel findings through comprehensive analysis. This allows clinical researchers, epidemiologists, and clinicians to have easy access to COVID-19 data without requiring expert knowledge in computing or data science. We expect that users will be able to reference our examples to construct their own integrative analysis methods, which will act as a basis for further scientific inquiry and data searching.

SARS-CoV-2 RdRp uses NDPs as a substrate and is able to incorporate NHC into RNA from diphosphate form molnupiravir

The coronavirus disease 2019 has been ravaging throughout the world for three years and has severely impaired both human health and the economy. The causative agent, severe acute respiratory syndrome coronavirus 2 employs the viral RNA dependent RNA polymerase (RdRp) complex for genome replication and transcription, making RdRp an appealing target for antiviral drug development. Systematic characterization of RdRp will undoubtedly aid in the development of antiviral drugs targeting RdRp. Here, our research reveals that RdRp can recognize and utilize nucleoside diphosphates as a substrate to synthesize RNA with an efficiency of about two thirds of using nucleoside triphosphates as a substrate. Nucleoside diphosphates incorporation is also template-specific and has high fidelity. Moreover, RdRp can incorporate β-d-N4-hydroxycytidine into RNA while using diphosphate form molnupiravir as a substrate. This incorporation results in genome mutation and virus death. It is also observed that diphosphate form molnupiravir is a better substrate for RdRp than the triphosphate form molnupiravir, presenting a new strategy for drug design.

A flexible age-dependent, spatially-stratified predictive model for the spread of COVID-19, accounting for multiple viral variants and vaccines

BACKGROUND

After COVID-19 vaccines received approval, vaccination campaigns were launched worldwide. Initially, these were characterized by a shortage of vaccine supply, and specific risk groups were prioritized. Once supply was guaranteed and vaccination coverage saturated, the focus shifted from risk groups to anti-vaxxers, the under-aged population, and regions of low coverage. At the same time, hopes to reach herd immunity by vaccination campaigns were put into perspective by the emergence and spread of more contagious and aggressive viral variants. Particularly, concerns were raised that not all vaccines protect against the new-emerging variants. The objective of this study is to introduce a predictive model to quantify the effect of vaccination campaigns on the spread of SARS-CoV-2 viral variants.

METHODS AND FINDINGS

The predictive model introduced here is a comprehensive extension of the one underlying the pandemic preparedness tool CovidSim 2.0 (http://covidsim.eu/). The model is age and spatially stratified, incorporates a finite (but arbitrary) number of different viral variants, and incorporates different vaccine products. The vaccines are allowed to differ in their vaccination schedule, vaccination rates, the onset of vaccination campaigns, and their effectiveness. These factors are also age and/or location dependent. Moreover, the effectiveness and the immunizing effect of vaccines are assumed to depend on the interaction of a given vaccine and viral variant. Importantly, vaccines are not assumed to immunize perfectly. Individuals can be immunized completely, only partially, or fail to be immunized against one or many viral variants. Not all individuals in the population are vaccinable. The model is formulated as a high-dimensional system of differential equations, which is implemented efficiently in the programming language Julia. As an example, the model was parameterized to reflect the epidemic situation in Germany until November 2021 and future dynamics of the epidemic under different interventions were predicted. In particular, without tightening contact reductions, a strong epidemic wave is predicted during December 2021 and January 2022. Provided the dynamics of the epidemic in Germany, in late 2021 administration of full-dose vaccination to all eligible individuals (e.g. by mandatory vaccination) would be too late to have a strong effect on reducing the number of infections in the fourth wave in Germany. However, it would reduce mortality. An emergency brake, i.e., an incidence-based stepwise lockdown, would be efficient to reduce the number of infections and mortality. Furthermore, to specifically account for mobility between regions, the model was applied to two German provinces of particular interest: Saxony, which currently has the lowest vaccine rollout in Germany and high incidence, and Schleswig-Holstein, which has high vaccine rollout and low incidence.

CONCLUSIONS

A highly sophisticated and flexible but easy-to-parameterize model for the ongoing COVID-19 pandemic is introduced. The model is capable of providing useful predictions for the COVID-19 pandemic, and hence provides a relevant tool for epidemic decision-making. The model can be adjusted to any country, and the predictions can be used to derive the demand for hospital or ICU capacities.

Knowledge, practice and attitude associated with SARS-CoV-2 Delta Variant among adults in Jordan

COVID-19 infection is a global pandemic health emergency. This contagious disease was caused by the Severe Acute Respiratory Syndrome Coronavirus‑2 (SARS‑CoV-2) which is mutating over time. In 2021, the Delta variant became the most dominant transmissible form. During the crisis, human practice and knowledge were critical in the overall efforts to encompass the outbreak. A cross-sectional, web-based approach was conducted among adults in Jordan to quantify knowledge, attitude, and practices towards SARS-CoV-2 (Delta variant). This research was carried out between 15th April and 15th of May 2021. The study questionnaire consisted of four sections including the participant's demographics, knowledge, practices and attitude. Comparative evaluation of responses was accomplished using a scoring system. Respondents who scored above the mean score (60%) on the item measured were categorized as knowledgeable, having a positive attitude, and good practices. Participants were allocated to one of the three groups; medical, non-medical and others (unemployed and housewives). Data collected was analyzed using Statistical Package for Social Sciences (SPSS) version 23.0 software. A variance test to assess the statistical difference between groups was used. Pearson's chi-squared test was applied to compare the variables and identify significant predictors. Of the participants, 308 (66%) were in the age group of 18-25yrs, 392 (84.1%) females, 120 (25.8%) employed and 346 (74.2%) unemployed. The principle source of knowledge was social media (291, 62.4%). Interestingly, participants had adequate overall knowledge. The mean knowledge score was 22.6 (± 0.19), 20.6 (± 0.19), and 21.3 (± 0.18) for the medical, the non-medical and the others group, respectively. Also, participants showed a positive attitude and good practices towards SARS-CoV-2 (Delta variant). The mean practice score for medical, the non-medical and the others groups was 7.35 (± 0.25), 7.38 (± 0.24), 7.35 (± 0.24) and the mean attitude score was 10.8 (± 0.16), 9.4 (± 0.21), 9.5 (± 0.22), respectively. The studied groups generally had good knowledge, positive attitudes and good practices about SARS-CoV-2 (Delta variant). This was expected due to the authorities' successful management of the pandemic and the high educational level of the Jordanian society, bearing in mind the economic and social impact of COVID-19 disease.

Omicron variant: Current insights and future directions

The global COVID-19 outbreak has returned with the identification of the SARS-CoV-2 Omicron variant (B.1.1.529) after appearing to be persistently spreading for the more than past two years. In comparison to prior SARS-CoV-2 variants, this new variant revealed a significant amount of mutation. This novel variety may have a greater rate of transmissibility which might impede the effectiveness of current diagnostic equipment as well as vaccination efficacy and also impede immunotherapies (Antibody / monoclonal antibody based). WHO designated B.1.1.529 as a variant of concern on November 26, 2021, identified as Omicron. The Omicron variant transmission method and severity, on the other hand, are well defined. The global spread of Omicron, which has now seized many nations, has resulted in numerous speculations regarding its origin and degree of infectivity. The following sections will go over its potential for transmission, omicron structure, and impact on COVID-19 vaccines, how it is different from delta variant and diagnostics.

Persistent Laplacian projected Omicron BA.4 and BA.5 to become new dominating variants

Due to its high transmissibility, Omicron BA.1 ousted the Delta variant to become a dominating variant in late 2021 and was replaced by more transmissible Omicron BA.2 in March 2022. An important question is which new variants will dominate in the future. Topology-based deep learning models have had tremendous success in forecasting emerging variants in the past. However, topology is insensitive to homotopic shape evolution in virus-human protein-protein binding, which is crucial to viral evolution and transmission. This challenge is tackled with persistent Laplacian, which is able to capture both the topological change and homotopic shape evolution of data. Persistent Laplacian-based deep learning models are developed to systematically evaluate variant infectivity. Our comparative analysis of Alpha, Beta, Gamma, Delta, Lambda, Mu, and Omicron BA.1, BA.1.1, BA.2, BA.2.11, BA.2.12.1, BA.3, BA.4, and BA.5 unveils that Omicron BA.2.11, BA.2.12.1, BA.3, BA.4, and BA.5 are more contagious than BA.2. In particular, BA.4 and BA.5 are about 36% more infectious than BA.2 and are projected to become new dominant variants by natural selection. Moreover, the proposed models outperform the state-of-the-art methods on three major benchmark datasets for mutation-induced protein-protein binding free energy changes. Our key projection about BA4 and BA.5's dominance made on May 1, 2022 (see arXiv:2205.00532) became a reality in late June 2022.

SARS-CoV-2 Delta Variant: Interplay between Individual Mutations and Their Allosteric Synergy

Since its first appearance in April 2021, B.1.617.2, also termed variant Delta, catalyzed one major worldwide wave dominating the second year of coronavirus disease 2019 (COVID-19) pandemic. Despite its quick disappearance worldwide, the strong virulence caused by a few point mutations remains an unsolved problem largely. Along with the other two sublineages, the Delta variant harbors an accumulation of Spike protein mutations, including the previously identified L452R, E484Q, and the newly emerged T478K on its receptor binding domain (RBD). We used molecular dynamics (MD) simulations, in combination with free energy perturbation (FEP) calculations, to examine the effects of two combinative mutation sets, L452R + E484Q and L452R + T478K. Our dynamic trajectories reveal an enhancement in binding affinity between mutated RBD and the common receptor protein angiotensin converting enzyme 2 (ACE2) through a net increase in the buried molecular surface area of the binary complex. This enhanced binding, mediated through Gln493, sets the same stage for all three sublineages due to the presence of L452R mutation. The other mutation component, E484Q or T478K, was found to impact the RBD-ACE2 binding and help the variant to evade several monoclonal antibodies (mAbs) in a distinct manner. Especially for L452R + T478K, synergies between mutations are mediated through a complex residual and water interaction network and further enhance its binding to ACE2. Taking together, this study demonstrates that new variants of SARS-CoV-2 accomplish both "attack" (infection) and "defense" (antibody neutralization escape) with the same "polished sword" (mutated Spike RBD).

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.

Molecular Mechanism of the Non-Covalent Orally Targeted SARS-CoV-2 Mpro Inhibitor S-217622 and Computational Assessment of Its Effectiveness against Mainstream Variants

Convenient and efficient therapeutic agents are urgently needed to block the continued spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here, the mechanism for the novel orally targeted SARS-CoV-2 main protease (M^pro) inhibitor S-217622 is revealed through a molecular dynamics simulation. The difference in the movement modes of the S-217622-M^pro complex and apo-M^pro suggested S-217622 could inhibit the motility intensity of M^pro, thus maintaining their stable binding. Subsequent energy calculations showed that the P2 pharmacophore possessed the highest energy contribution among the three pharmacophores of S-217622. Additionally, hot-spot residues H41, M165, C145, E166, and H163 have strong interactions with S-217622. To further investigate the resistance of S-217622 to six mainstream variants, the binding modes of S-217622 with these variants were elucidated. The subtle differences in energy compared to that of the wild type implied that the binding patterns of these systems were similar, and S-217622 still inhibited these variants. We hope this work will provide theoretical insights for optimizing novel targeted M^pro drugs.

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

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

Multi-season transmission model of Eastern Equine Encephalitis

Eastern Equine Encephalitis (EEE) is an arbovirus that, while it has been known to exist since the 1930's, recently had a spike in cases. This increased prevalence is particularly concerning due to the severity of the disease with 1 in 3 symptomatic patients dying. The cause of this peak is currently unknown but could be due to changes in climate, the virus itself, or host behavior. In this paper we propose a novel multi-season deterministic model of EEE spread and its stochastic counterpart. Models were parameterized using a dataset from the Florida Department of Health with sixteen years of sentinel chicken seroconversion rates. The different roles of the enzootic and bridge mosquito vectors were explored. As expected, enzootic mosquitoes like Culiseta melanura were more important for EEE persistence, while bridge vectors were implicated in the disease burden in humans. These models were used to explore hypothetical viral mutations and host behavior changes, including increased infectivity, vertical transmission, and host feeding preferences. Results showed that changes in the enzootic vector transmission increased cases among birds more drastically than equivalent changes in the bridge vector. Additionally, a 5% difference in the bridge vector's bird feeding preference can increase cumulative dead-end host infections more than 20-fold. Taken together, this suggests changes in many parts of the transmission cycle can augment cases in birds, but the bridge vectors feeding preference acts as a valve limiting the enzootic circulation from its impact on dead-end hosts, such as humans. Our what-if scenario analysis reveals and measures possible threats regarding EEE and relevant environmental changes and hypothetically suggests how to prevent potential damage to public health and the equine economy.

Mathematical artificial intelligence design of mutation-proof COVID-19 monoclonal antibodies

Emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have compromised existing vaccines and posed a grand challenge to coronavirus disease 2019 (COVID-19) prevention, control, and global economic recovery. For COVID-19 patients, one of the most effective COVID-19 medications is monoclonal antibody (mAb) therapies. The United States Food and Drug Administration (U.S. FDA) has given the emergency use authorization (EUA) to a few mAbs, including those from Regeneron, Eli Elly, etc. However, they are also undermined by SARS-CoV-2 mutations. It is imperative to develop effective mutation-proof mAbs for treating COVID-19 patients infected by all emerging variants and/or the original SARS-CoV-2. We carry out a deep mutational scanning to present the blueprint of such mAbs using algebraic topology and artificial intelligence (AI). To reduce the risk of clinical trial-related failure, we select five mAbs either with FDA EUA or in clinical trials as our starting point. We demonstrate that topological AI-designed mAbs are effective for variants of concerns and variants of interest designated by the World Health Organization (WHO), as well as the original SARS-CoV-2. Our topological AI methodologies have been validated by tens of thousands of deep mutational data and their predictions have been confirmed by results from tens of experimental laboratories and population-level statistics of genome isolates from hundreds of thousands of patients.

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

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

A unique antigen against SARS-CoV-2, Acinetobacter baumannii, and Pseudomonas aeruginosa

The recent outbreak of COVID-19 has increased hospital admissions, which could elevate the risk of nosocomial infections, such as A. baumannii and P. aeruginosa infections. Although effective vaccines have been developed against SARS-CoV-2, no approved treatment option is still available against antimicrobial-resistant strains of A. baumannii and P. aeruginosa. In the current study, an all-in-one antigen was designed based on an innovative, state-of-the-art strategy. In this regard, experimentally validated linear epitopes of spike protein (SARS-CoV-2), OmpA (A. baumannii), and OprF (P. aeruginosa) were selected to be harbored by mature OmpA as a scaffold. The selected epitopes were used to replace the loops and turns of the barrel domain in OmpA; OprF_311–341 replaced the most similar sequence within the OmpA, and three validated epitopes of OmpA were retained intact. The obtained antigen encompasses five antigenic peptides of spike protein, which are involved in SARS-CoV-2 pathogenicity. One of these epitopes, viz. QTQTNSPRRARSV could trigger antibodies preventing super-antigenic characteristics of spike and alleviating probable autoimmune responses. The designed antigen could raise antibodies neutralizing emerging variants of SARS-CoV-2 since at least two epitopes are consensus. In conclusion, the designed antigen is expected to raise protective antibodies against SARS-CoV-2, A. baumannii, and P. aeruginosa.

Mapping of SARS-CoV-2 spike protein evolution during first and second waves of COVID-19 infections in India

Aim: The aim of this study was to investigate the SARS-CoV-2 spike protein evolution during the first and second wave of COVID-19 infections in India. Materials & Methods: Detailed mutation analysis was done in 763 samples taken from GISAID for the ten most affected Indian states between March 2020 to August 2021. Results: The study revealed 242 mutations corresponding to 207 sites. Fifty one novel mutations emerged during the assessment period, including many with higher transmissibility and immune evasion functions. Highest number of mutations per spike protein also rose from 5 (first wave) to 13 (second wave). Conclusion: The study identified mutation-rich and no mutation regions in the spike protein. The conserved spike regions can be useful for designing future diagnostics, vaccines and therapeutics.

A Network Analysis of the Fear of COVID-19 Scale (FCV-19S): A Large-Scale Cross-Cultural Study in Iran, Bangladesh, and Norway

The rapid spread of the coronavirus disease 2019 (COVID-19) has led to high levels of fear worldwide. Given that fear is an important factor in causing psychological distress and facilitating preventive behaviors, assessing the fear of COVID-19 is important. The seven-item Fear of COVID-19 Scale (FCV-19S) is a widely used psychometric instrument to assess this fear. However, the factor structure of the FCV-19S remains unclear according to the current evidence. Therefore, the present study used a network analysis to provide further empirical evidence for the factor structure of FCV-19S. A total of 24,429 participants from Iran (n = 10,843), Bangladesh (n = 9906), and Norway (n = 3680) completed the FCV-19S in their local language. A network analysis (via regularized partial correlation networks) was applied to investigate the seven FCV-19S items. Moreover, relationships between the FCV-19S items were compared across gender (males vs. females), age groups (18−30 years, 31−50 years, and >50 years), and countries (Iran, Bangladesh, and Norway). A two-factor structure pattern was observed (three items concerning physical factors, including clammy hands, insomnia, and heart palpitations; four items concerning psychosocial factors, including being afraid, uncomfortable, afraid of dying, and anxious about COVID-19 news). Moreover, this pattern was found to be the same among men and women, across age groups and countries. The network analysis used in the present study verified the two-factor structure for the FCV-19S. Future studies may consider using the two-factor structure of FCV-19S to assess the fear of COVID-19 during the COVID-19 era.

Omicron BA.2 (B.1.1.529.2): High Potential for Becoming the Next Dominant Variant

The Omicron variant has three subvariants: BA.1 (B.1.1.529.1), BA.2 (B.1.1.529.2), and BA.3 (B.1.1.529.3). BA.2 is found to be able to alarmingly reinfect patients originally infected by Omicron BA.1. An important question is whether BA.2 or BA.3 will become a new dominating "variant of concern". Currently, no experimental data has been reported about BA.2 and BA.3. We construct a novel algebraic topology-based deep learning model to systematically evaluate BA.2's and BA.3's infectivity, vaccine breakthrough capability, and antibody resistance. Our comparative analysis of all main variants, namely, Alpha, Beta, Gamma, Delta, Lambda, Mu, BA.1, BA.2, and BA.3, unveils that BA.2 is about 1.5 and 4.2 times as contagious as BA.1 and Delta, respectively. It is also 30% and 17-fold more capable than BA.1 and Delta, respectively, to escape current vaccines. Therefore, we project that Omicron BA.2 is on a path to becoming the next dominant variant. We forecast that like Omicron BA.1, BA.2 will also seriously compromise most existing monoclonal antibodies. All key predictions have been nearly perfectly confirmed before the official publication of this work.

AutoCoV: tracking the early spread of COVID-19 in terms of the spatial and temporal patterns from embedding space by K-mer based deep learning

BACKGROUND

The widely spreading coronavirus disease (COVID-19) has three major spreading properties: pathogenic mutations, spatial, and temporal propagation patterns. We know the spread of the virus geographically and temporally in terms of statistics, i.e., the number of patients. However, we are yet to understand the spread at the level of individual patients. As of March 2021, COVID-19 is wide-spread all over the world with new genetic variants. One important question is to track the early spreading patterns of COVID-19 until the virus has got spread all over the world.

RESULTS

In this work, we proposed AutoCoV, a deep learning method with multiple loss object, that can track the early spread of COVID-19 in terms of spatial and temporal patterns until the disease is fully spread over the world in July 2020. Performances in learning spatial or temporal patterns were measured with two clustering measures and one classification measure. For annotated SARS-CoV-2 sequences from the National Center for Biotechnology Information (NCBI), AutoCoV outperformed seven baseline methods in our experiments for learning either spatial or temporal patterns. For spatial patterns, AutoCoV had at least 1.7-fold higher clustering performances and an F1 score of 88.1%. For temporal patterns, AutoCoV had at least 1.6-fold higher clustering performances and an F1 score of 76.1%. Furthermore, AutoCoV demonstrated the robustness of the embedding space with an independent dataset, Global Initiative for Sharing All Influenza Data (GISAID).

CONCLUSIONS

In summary, AutoCoV learns geographic and temporal spreading patterns successfully in experiments on NCBI and GISAID datasets and is the first of its kind that learns virus spreading patterns from the genome sequences, to the best of our knowledge. We expect that this type of embedding method will be helpful in characterizing fast-evolving pandemics.

Mathematical artificial intelligence design of mutation-proof COVID-19 monoclonal antibodies

Emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have compromised existing vaccines and posed a grand challenge to coronavirus disease 2019 (COVID-19) prevention, control, and global economic recovery. For COVID-19 patients, one of the most effective COVID-19 medications is monoclonal antibody (mAb) therapies. The United States Food and Drug Administration (U.S. FDA) has given the emergency use authorization (EUA) to a few mAbs, including those from Regeneron, Eli Elly, etc. However, they are also undermined by SARS-CoV-2 mutations. It is imperative to develop effective mutation-proof mAbs for treating COVID-19 patients infected by all emerging variants and/or the original SARS-CoV-2. We carry out a deep mutational scanning to present the blueprint of such mAbs using algebraic topology and artificial intelligence (AI). To reduce the risk of clinical trial-related failure, we select five mAbs either with FDA EUA or in clinical trials as our starting point. We demonstrate that topological AI-designed mAbs are effective to variants of concerns and variants of interest designated by the World Health Organization (WHO), as well as the original SARS-CoV-2. Our topological AI methodologies have been validated by tens of thousands of deep mutational data and their predictions have been confirmed by results from tens of experimental laboratories and population-level statistics of genome isolates from hundreds of thousands of patients.

Computational design of stapled peptide inhibitor against SARS‐CoV ‐2 receptor binding domain

Since its first detection in 2019, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2) has been the cause of millions of deaths worldwide. Despite the development and administration of different vaccines, the situation is still worrisome as the virus is constantly mutating to produce newer variants some of which are highly infectious. This raises an urgent requirement to understand the infection mechanism and thereby design therapeutic‐based treatment for COVID‐19. The gateway of the virus to the host cell is mediated by the binding of the receptor binding domain (RBD) of the virus spike protein to the angiotensin‐converting enzyme 2 (ACE2) of the human cell. Therefore, the RBD of SARS‐CoV‐2 can be used as a target to design therapeutics. The α1 helix of ACE2, which forms direct contact with the RBD surface, has been used as a template in the current study to design stapled peptide therapeutics. Using computer simulation, the mechanism and thermodynamics of the binding of six stapled peptides with RBD have been estimated. Among these, the one with two lactam stapling agents has shown binding affinity, sufficient to overcome RBD‐ACE2 binding. Analyses of the mechanistic detail reveal that a reorganization of amino acids at the RBD‐ACE2 interface produces favorable enthalpy of binding whereas conformational restriction of the free peptide reduces the loss in entropy to result higher binding affinity. The understanding of the relation of the nature of the stapling agent with their binding affinity opens up the avenue to explore stapled peptides as therapeutic against SARS‐CoV‐2.

A review of the potential of conventional and advanced membrane technology in the removal of pathogens from wastewater

Consumption of pathogenic contaminated water has claimed the lives of many people. Hence, this scenario has emphasized the urgent need for research methods to avoid, treat and eliminate harmful pathogens in wastewater. Therefore, effective water treatment has become a matter of utmost importance. Membrane technology offers purer, cleaner, and pathogen-free water through the water separation method via a permeable membrane. Advanced membrane technology such as nanocomposite membrane, membrane distillation, membrane bioreactor, and photocatalytic membrane reactor can offer synergistic effects in removing pathogen through the integration of additional functionality and filtration in a single chamber. This paper also comprehensively discussed the application, challenges, and future perspective of the advanced membrane technology as a promising alternative in battling pathogenic microbial contaminants, which will also be beneficial and valuable in managing pandemics in the future as well as protecting human health and the environment. In addition, the potential of membrane technology in battling the ongoing global pandemic of coronavirus disease 2019 (COVID-19) was also discussed briefly.

Isolation of Thioinosine and Butenolides from a Terrestrial Actinomycetes sp. GSCW-51 and Their in Silico Studies for Potential against SARS-CoV-2

In our continuous screening for bioactive microbial natural products, the culture extracts of a terrestrial Actinomycetes sp. GSCW-51 yielded two new metabolites, i. e., 5-hydroxymethyl-3-(1-hydroxy-6-methyl-7-oxooctyl)dihydrofuran-2(3H)-one (1), 5-hydroxymethyl-3-(1,7-dihydroxy-6-methyloctyl)dihydrofuran-2(3H)-one (2), and two known compounds; 5'-methylthioinosine (3), and 5'-methylthioinosine sulfoxide (4), which are isolated first time from any natural source, along with four known compounds (5-8). The structures of the new compounds were deduced by HR-ESI-MS, 1D and 2D NMR data, and in comparison with related compounds from the literature. Additionally, owing to the current COVID-19 pandemic situation, we also computationally explored the therapeutic potential of our isolated compounds against SARS-CoV-2. Compound 4 showed the best binding energies of -6.2 and -6.6 kcal/mol for Mpro and spike proteins, respectively. The intermolecular interactions were also studied using 2-D and 3-D imagery, which also supported the binding energies as well as put several insights under the spotlight. Furthermore, Lipinski's rule of 5 was used to predict the drug likeness of compounds 1-4, which indicated all compounds obey Lipinski's rule of 5. The study of bioavailability radars of the compounds 1-4 also confirmed their drug likeness properties where all the five crucial drug likeness parameters are in color area, which is safe to be used as drugs. Our isolation and computational findings highly encourage the scientific community to do further in vitro and in vivo studies of compounds 1-4.

Debate on mandatory COVID-19 vaccination

BACKGROUND

Since January 2020, worldwide public health has been threatened by COVID-19, for which vaccines have been adopted from December 2020.

DISCUSSION

Although vaccines demonstrate effectiveness against this disease, vaccine hesitancy reveals concerns towards short-term and long-term side effects or adverse reactions such as post-inoculation death. Mandatory vaccination is used to provide herd immunity, but is refutable due to infringement of human rights and autonomy. Furthermore, the evidence testifies that vaccination cannot guarantee prevention of infection or re-infection, resulting in public resentment against this coercive measure, whilst post-inoculation anxiety continues.

PERSPECTIVE

This discussion suggests a holistic approach, involving the collective efforts of governments, medical experts and individuals, through basic preventive measures and alternative therapy to live with COVID-19 in a healthy and resourceful manner.

Emerging Vaccine-Breakthrough SARS-CoV-2 Variants

The surge of COVID-19 infections has been fueled by new SARS-CoV-2 variants, namely Alpha, Beta, Gamma, Delta, and so forth. The molecular mechanism underlying such surge is elusive due to the existence of 28 554 unique mutations, including 4 653 non-degenerate mutations on the spike protein. Understanding the molecular mechanism of SARS-CoV-2 transmission and evolution is a prerequisite to foresee the trend of emerging vaccine-breakthrough variants and the design of mutation-proof vaccines and monoclonal antibodies. We integrate the genotyping of 1 489 884 SARS-CoV-2 genomes, a library of 130 human antibodies, tens of thousands of mutational data, topological data analysis, and deep learning to reveal SARS-CoV-2 evolution mechanism and forecast emerging vaccine-breakthrough variants. We show that prevailing variants can be quantitatively explained by infectivity-strengthening and vaccine-escape (co-)mutations on the spike protein RBD due to natural selection and/or vaccination-induced evolutionary pressure. We illustrate that infectivity strengthening mutations were the main mechanism for viral evolution, while vaccine-escape mutations become a dominating viral evolutionary mechanism among highly vaccinated populations. We demonstrate that Lambda is as infectious as Delta but is more vaccine-resistant. We analyze emerging vaccine-breakthrough comutations in highly vaccinated countries, including the United Kingdom, the United States, Denmark, and so forth. Finally, we identify sets of comutations that have a high likelihood of massive growth: [A411S, L452R, T478K], [L452R, T478K, N501Y], [V401L, L452R, T478K], [K417N, L452R, T478K], [L452R, T478K, E484K, N501Y], and [P384L, K417N, E484K, N501Y]. We predict they can escape existing vaccines. We foresee an urgent need to develop new virus combating strategies.

Deep learning based on biologically interpretable genome representation predicts two types of human adaptation of SARS-CoV-2 variants

Explosively emerging SARS-CoV-2 variants challenge current nomenclature schemes based on genetic diversity and biological significance. Genomic composition-based machine learning methods have recently performed well in identifying phenotype–genotype relationships. We introduced a framework involving dinucleotide (DNT) composition representation (DCR) to parse the general human adaptation of RNA viruses and applied a three-dimensional convolutional neural network (3D CNN) analysis to learn the human adaptation of other existing coronaviruses (CoVs) and predict the adaptation of SARS-CoV-2 variants of concern (VOCs). A markedly separable, linear DCR distribution was observed in two major genes—receptor-binding glycoprotein and RNA-dependent RNA polymerase (RdRp)—of six families of single-stranded (ssRNA) viruses. Additionally, there was a general host-specific distribution of both the spike proteins and RdRps of CoVs. The 3D CNN based on spike DCR predicted a dominant type II adaptation of most Beta, Delta and Omicron VOCs, with high transmissibility and low pathogenicity. Type I adaptation with opposite transmissibility and pathogenicity was predicted for SARS-CoV-2 Alpha VOCs (77%) and Kappa variants of interest (58%). The identified adaptive determinants included D1118H and A570D mutations and local DNTs. Thus, the 3D CNN model based on DCR features predicts SARS-CoV-2, a major type II human adaptation and is qualified to predict variant adaptation in real time, facilitating the risk-assessment of emerging SARS-CoV-2 variants and COVID-19 control.

Omicron BA.2 (B.1.1.529.2): high potential to becoming the next dominating variant

The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly replaced the Delta variant as a dominating SARS-CoV-2 variant because of natural selection, which favors the variant with higher infectivity and stronger vaccine breakthrough ability. Omicron has three lineages or subvariants, BA.1 (B.1.1.529.1), BA.2 (B.1.1.529.2), and BA.3 (B.1.1.529.3). Among them, BA.1 is the currently prevailing subvariant. BA.2 shares 32 mutations with BA.1 but has 28 distinct ones. BA.3 shares most of its mutations with BA.1 and BA.2 except for one. BA.2 is found to be able to alarmingly reinfect patients originally infected by Omicron BA.1. An important question is whether BA.2 or BA.3 will become a new dominating ``variant of concern''. Currently, no experimental data has been reported about BA.2 and BA.3. We construct a novel algebraic topology-based deep learning model trained with tens of thousands of mutational and deep mutational data to systematically evaluate BA.2's and BA.3's infectivity, vaccine breakthrough capability, and antibody resistance. Our comparative analysis of all main variants namely, Alpha, Beta, Gamma, Delta, Lambda, Mu, BA.1, BA.2, and BA.3, unveils that BA.2 is about 1.5 and 4.2 times as contagious as BA.1 and Delta, respectively. It is also 30% and 17-fold more capable than BA.1 and Delta, respectively, to escape current vaccines. Therefore, we project that Omicron BA.2 is on its path to becoming the next dominating variant. We forecast that like Omicron BA.1, BA.2 will also seriously compromise most existing mAbs, except for sotrovimab developed by GlaxoSmithKline.

Omicron BA.2 (B.1.1.529.2): high potential to becoming the next dominating variant

The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly replaced the Delta variant as a dominating SARS-CoV-2 variant because of natural selection, which favors the variant with higher infectivity and stronger vaccine breakthrough ability. Omicron has three lineages or subvariants, BA.1 (B.1.1.529.1), BA.2 (B.1.1.529.2), and BA.3 (B.1.1.529.3). Among them, BA.1 is the currently prevailing subvariant. BA.2 shares 32 mutations with BA.1 but has 28 distinct ones. BA.3 shares most of its mutations with BA.1 and BA.2 except for one. BA.2 is found to be able to alarmingly reinfect patients originally infected by Omicron BA.1. An important question is whether BA.2 or BA.3 will become a new dominating "variant of concern". Currently, no experimental data has been reported about BA.2 and BA.3. We construct a novel algebraic topology-based deep learning model trained with tens of thousands of mutational and deep mutational data to systematically evaluate BA.2's and BA.3's infectivity, vaccine breakthrough capability, and antibody resistance. Our comparative analysis of all main variants namely, Alpha, Beta, Gamma, Delta, Lambda, Mu, BA.1, BA.2, and BA.3, unveils that BA.2 is about 1.5 and 4.2 times as contagious as BA.1 and Delta, respectively. It is also 30% and 17-fold more capable than BA.1 and Delta, respectively, to escape current vaccines. Therefore, we project that Omicron BA.2 is on its path to becoming the next dominating variant. We forecast that like Omicron BA.1, BA.2 will also seriously compromise most existing mAbs, except for sotrovimab developed by GlaxoSmithKline.

SERS-PCR assays of SARS-CoV-2 target genes using Au nanoparticles-internalized Au nanodimple substrates

The reverse transcription-polymerase chain reaction (RT-PCR) method has been adopted worldwide to diagnose severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although this method has good sensitivity and specificity, there is a need to develop a more rapid diagnostic technology, given the virus's rapid spread. However, the RT-PCR method takes a long time to diagnose SARS-CoV-2 because of the required thermocycling steps. Therefore, we developed a surface-enhanced Raman scattering (SERS)-PCR detection method using an AuNP-internalized Au nanodimple substrate (AuNDS) to shorten the diagnosis time by reducing the number of thermocycling steps needed to amplify the DNA. For the representative target markers, namely, the envelope protein (E) and RNA-dependent RNA polymerase (RdRp) genes of SARS-CoV-2, 25 RT-PCR thermocycles are required to reach a detectable threshold value, while 15 cycles are needed for magnetic bead-based SERS-PCR when the initial DNA concentration was 1.00× 105 copies/μL. However, only 8 cycles are needed for the AuNDS-based SERS-PCR. The corresponding detectable target DNA concentrations were 3.36 × 1012, 3.28 × 109, and 2.56 × 107 copies/μL, respectively. Therefore, AuNDS-based SERS-PCR is seen as being a new molecular diagnostic platform that can shorten the time required for the thermocycling steps relative to the conventional RT-PCR.

Omicron Variant (B.1.1.529): Infectivity, Vaccine Breakthrough, and Antibody Resistance

The latest severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant Omicron (B.1.1.529) has ushered panic responses around the world due to its contagious and vaccine escape mutations. The essential infectivity and antibody resistance of the SARS-CoV-2 variant are determined by its mutations on the spike (S) protein receptor-binding domain (RBD). However, a complete experimental evaluation of Omicron might take weeks or even months. Here, we present a comprehensive quantitative analysis of Omicron's infectivity, vaccine breakthrough, and antibody resistance. An artificial intelligence (AI) model, which has been trained with tens of thousands of experimental data and extensively validated by experimental results on SARS-CoV-2, reveals that Omicron may be over 10 times more contagious than the original virus or about 2.8 times as infectious as the Delta variant. On the basis of 185 three-dimensional (3D) structures of antibody-RBD complexes, we unveil that Omicron may have an 88% likelihood to escape current vaccines. The U.S. Food and Drug Administration (FDA)-approved monoclonal antibodies (mAbs) from Eli Lilly may be seriously compromised. Omicron may also diminish the efficacy of mAbs from AstraZeneca, Regeneron mAb cocktail, Celltrion, and Rockefeller University. However, its impacts on GlaxoSmithKline's sotrovimab appear to be mild. Our work calls for new strategies to develop the next generation mutation-proof SARS-CoV-2 vaccines and antibodies.

Identification of HIV Rapid Mutations Using Differences in Nucleotide Distribution over Time

Mutation is the driving force of species evolution, which may change the genetic information of organisms and obtain selective competitive advantages to adapt to environmental changes. It may change the structure or function of translated proteins, and cause abnormal cell operation, a variety of diseases and even cancer. Therefore, it is particularly important to identify gene regions with high mutations. Mutations will cause changes in nucleotide distribution, which can be characterized by natural vectors globally. Based on natural vectors, we propose a mathematical formula for measuring the difference in nucleotide distribution over time to investigate the mutations of human immunodeficiency virus. The studied dataset is from public databases and includes gene sequences from twenty HIV-infected patients. The results show that the mutation rate of the nine major genes or gene segment regions in the genome exhibits discrepancy during the infected period, and the Env gene has the fastest mutation rate. We deduce that the peak of virus mutation has a close temporal relationship with viral divergence and diversity. The mutation study of HIV is of great significance to clinical diagnosis and drug design.