Structural analysis of SARS-Cov-2 nonstructural protein 1 polymorphisms found in the Brazilian Amazon.

The coronavirus disease COVID-19 has been the cause of millions of deaths worldwide. Among the SARS-CoV-2 proteins, the non-structural protein 1 (NSP1) has great importance during the virus infection process and is present in both alpha and beta-CoVs. Therefore, monitoring of NSP1 polymorphisms is crucial in order to understand their role during infection and virus-induced pathogenicity. Herein, we analyzed how mutations detected in the circulating SARS-CoV-2 in the population of the city of Manaus, Amazonas state, Brazil could modify the tertiary structure of the NSP1 protein. Three mutations were detected in the SARS-CoV-2 NSP1 gene: deletion of the amino acids KSF from positions 141 to 143 (delKSF), SARS-CoV-2, lineage B.1.195; and two substitutions, R29H and R43C, SARS-CoV-2 lineage B.1.1.28 and B.1.1.33, respectively. The delKSF was found in 47 samples, whereas R29H and R43C were found in two samples, one for each mutation. The NSP1 structures carrying the mutations R43C and R29H on the N-terminal portion (e.g. residues 10 to 127) showed minor backbone divergence compared to the Wuhan model. However, the NSP1 C-terminal region (residues 145 to 180) was severely affected in the delKSF and R29H mutants. The intermediate variable region (residues 144 to 148) leads to changes in the C-terminal region, particularly in the delKSF structure. New investigations must be carried out to analyze how these changes affect NSP1 activity during the infection. Our results reinforce the need for continuous genomic surveillance of SARS-CoV-2 to better understand virus evolution and assess the potential impact of the viral mutations on the approved vaccines and future therapies.

Genomic Characterization of a Novel Alphacoronavirus Isolated from Bats, Korea, 2020.

Coronavirus, an important zoonotic disease, raises concerns of future pandemics. The bat is considered a source of noticeable viruses resulting in human and livestock infections, especially the coronavirus. Therefore, surveillance and genetic analysis of coronaviruses in bats are essential in order to prevent the risk of future diseases. In this study, the genome of HCQD-2020, a novel alphacoronavirus detected in a bat (Eptesicus serotinus), was assembled and described using next-generation sequencing and bioinformatics analysis. The comparison of the whole-genome sequence and the conserved amino acid sequence of replicated proteins revealed that the new strain was distantly related with other known species in the Alphacoronavirus genus. Phylogenetic construction indicated that this strain formed a separated branch with other species, suggesting a new species of Alphacoronavirus. Additionally, in silico prediction also revealed the risk of cross-species infection of this strain, especially in the order Artiodactyla. In summary, this study provided the genetic characteristics of a possible new species belonging to Alphacoronavirus.

Revelation of Potent Epitopes Present in Unannotated ORF Antigens of SARS-CoV-2 for Epitope-Based Polyvalent Vaccine Design Using Immunoinformatics Approach.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) kills thousands of people worldwide every day, thus necessitating rapid development of countermeasures. Immunoinformatics analyses carried out here in search of immunodominant regions in recently identified SARS-CoV-2 unannotated open reading frames (uORFs) have identified eight linear B-cell, one conformational B-cell, 10 CD4+ T-cell, and 12 CD8+ T-cell promising epitopes. Among them, ORF9b B-cell and T-cell epitopes are the most promising followed by M.ext and ORF3c epitopes. ORF9b40-48 (CD8+ T-cell epitope) is found to be highly immunogenic and antigenic with the highest allele coverage. Furthermore, it has overlap with four potent CD4+ T-cell epitopes. Structure-based B-cell epitope prediction has identified ORF9b61-68 to be immunodominant, which partially overlaps with one of the linear B-cell epitopes (ORF9b65-69). ORF3c CD4+ T-cell epitopes (ORF3c2-16, ORF3c3-17, and ORF3c4-18) and linear B-cell epitope (ORF3c14-22) have also been identified as the candidate epitopes. Similarly, M.ext and 7a.iORF1 (overlap with M and ORF7a) proteins have promising immunogenic regions. By considering the level of antigen expression, four ORF9b and five M.ext epitopes are finally shortlisted as potent epitopes. Mutation analysis has further revealed that the shortlisted potent uORF epitopes are resistant to recurrent mutations. Additionally, four N-protein (expressed by canonical ORF) epitopes are found to be potent. Thus, SARS-CoV-2 uORF B-cell and T-cell epitopes identified here along with canonical ORF epitopes may aid in the design of a promising epitope-based polyvalent vaccine (when connected through appropriate linkers) against SARS-CoV-2. Such a vaccine can act as a bulwark against SARS-CoV-2, especially in the scenario of emergence of variants with recurring mutations in the spike protein.

A molecular sensor determines the ubiquitin substrate specificity of SARS-CoV-2 papain-like protease.

The SARS-CoV-2 papain-like protease (PLpro) is a target for antiviral drug development. It is essential for processing viral polyproteins for replication and functions in host immune evasion by cleaving ubiquitin (Ub) and ubiquitin-like protein (Ubl) conjugates. While highly conserved, SARS-CoV-2 and SARS-CoV PLpro have contrasting Ub/Ubl substrate preferences. Using a combination of structural analyses and functional assays, we identify a molecular sensor within the S1 Ub-binding site of PLpro that serves as a key determinant of substrate specificity. Variations within the S1 sensor specifically alter cleavage of Ub substrates but not of the Ubl interferon-stimulated gene 15 protein (ISG15). Significantly, a variant of concern associated with immune evasion carries a mutation in the S1 sensor that enhances PLpro activity on Ub substrates. Collectively, our data identify the S1 sensor region as a potential hotspot of variability that could alter host antiviral immune responses to newly emerging SARS-CoV-2 lineages.

The emergence and ongoing convergent evolution of the SARS-CoV-2 N501Y lineages.

The independent emergence late in 2020 of the B.1.1.7, B.1.351, and P.1 lineages of SARS-CoV-2 prompted renewed concerns about the evolutionary capacity of this virus to overcome public health interventions and rising population immunity. Here, by examining patterns of synonymous and non-synonymous mutations that have accumulated in SARS-CoV-2 genomes since the pandemic began, we find that the emergence of these three "501Y lineages" coincided with a major global shift in the selective forces acting on various SARS-CoV-2 genes. Following their emergence, the adaptive evolution of 501Y lineage viruses has involved repeated selectively favored convergent mutations at 35 genome sites, mutations we refer to as the 501Y meta-signature. The ongoing convergence of viruses in many other lineages on this meta-signature suggests that it includes multiple mutation combinations capable of promoting the persistence of diverse SARS-CoV-2 lineages in the face of mounting host immune recognition.

Molecular evolutionary characteristics of SARS-CoV-2 emerging in the United States.

SARS-CoV-2 is a newly discovered beta coronavirus at the end of 2019, which is highly pathogenic and poses a serious threat to human health. In this paper, 1875 SARS-CoV-2 whole genome sequences and the sequence coding spike protein (S gene) sampled from the United States were used for bioinformatics analysis to study the molecular evolutionary characteristics of its genome and spike protein. The MCMC method was used to calculate the evolution rate of the whole genome sequence and the nucleotide mutation rate of the S gene. The results showed that the nucleotide mutation rate of the whole genome was 6.677 × 10-4 substitution per site per year, and the nucleotide mutation rate of the S gene was 8.066 × 10-4 substitution per site per year, which was at a medium level compared with other RNA viruses. Our findings confirmed the scientific hypothesis that the rate of evolution of the virus gradually decreases over time. We also found 13 statistically significant positive selection sites in the SARS-CoV-2 genome. In addition, the results showed that there were 101 nonsynonymous mutation sites in the amino acid sequence of S protein, including seven putative harmful mutation sites. This paper has preliminarily clarified the evolutionary characteristics of SARS-CoV-2 in the United States, providing a scientific basis for future surveillance and prevention of virus variants.

Urgent Need for Field Surveys of Coronaviruses in Southeast Asia to Understand the SARS-CoV-2 Phylogeny and Risk Assessment for Future Outbreaks.

Phylogenetic analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is focused on a single isolate of bat coronaviruses (bat CoVs) which does not adequately represent genetically related coronaviruses (CoVs) [...].

A parsimonious approach for recognizing SARS-CoV-2 and host interactions.

Effective countermeasures against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) demand a better understanding of the pathogen-host interactions. However, such information about the targets, responses, and effects in the host due to the virus is limited, especially so in the case of newly emerged pathogens. The peptide domains that form the interfaces of host and pathogen interacting proteins being evolutionarily conserved, it may be hypothesized that such interactions can be inferred from the similarities in the nucleotide sequences between the host and the pathogen. This communication reports the results of a study based on a parsimonious approach for the identification of the host-virus interactions, where sequence complementarity between the human and SARS-Cov-2 genomes was used to predict several interactions between the host and SARS-CoV-2 at different levels of biological organization. In particular, the findings are suggestive of a direct effect of SARS-CoV-2 on cardiac health. The existing literature on host responses to SARS-CoV-2 and other viruses attest to many of these predicted interactions, supporting the utility of the proposed approach for the identification of host interactions with other novel pathogens.

Cryo-EM Structures of SARS-CoV-2 Spike without and with ACE2 Reveal a pH-Dependent Switch to Mediate Endosomal Positioning of Receptor-Binding Domains.

The SARS-CoV-2 spike employs mobile receptor-binding domains (RBDs) to engage the human ACE2 receptor and to facilitate virus entry, which can occur through low-pH-endosomal pathways. To understand how ACE2 binding and low pH affect spike conformation, we determined cryo-electron microscopy structures-at serological and endosomal pH-delineating spike recognition of up to three ACE2 molecules. RBDs freely adopted "up" conformations required for ACE2 interaction, primarily through RBD movement combined with smaller alterations in neighboring domains. In the absence of ACE2, single-RBD-up conformations dominated at pH 5.5, resolving into a solitary all-down conformation at lower pH. Notably, a pH-dependent refolding region (residues 824-858) at the spike-interdomain interface displayed dramatic structural rearrangements and mediated RBD positioning through coordinated movements of the entire trimer apex. These structures provide a foundation for understanding prefusion-spike mechanics governing endosomal entry; we suggest that the low pH all-down conformation potentially facilitates immune evasion from RBD-up binding antibody.

Antisense Peptide Technology for Diagnostic Tests and Bioengineering Research.

Antisense peptide technology (APT) is based on a useful heuristic algorithm for rational peptide design. It was deduced from empirical observations that peptides consisting of complementary (sense and antisense) amino acids interact with higher probability and affinity than the randomly selected ones. This phenomenon is closely related to the structure of the standard genetic code table, and at the same time, is unrelated to the direction of its codon sequence translation. The concept of complementary peptide interaction is discussed, and its possible applications to diagnostic tests and bioengineering research are summarized. Problems and difficulties that may arise using APT are discussed, and possible solutions are proposed. The methodology was tested on the example of SARS-CoV-2. It is shown that the CABS-dock server accurately predicts the binding of antisense peptides to the SARS-CoV-2 receptor binding domain without requiring predefinition of the binding site. It is concluded that the benefits of APT outweigh the costs of random peptide screening and could lead to considerable savings in time and resources, especially if combined with other computational and immunochemical methods.

DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor.

Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly benefit vaccine development and cancer immunotherapy. However, identifying immunogenic peptides accurately is still a huge challenge. Most of the antigen peptides predicted in silico fail to elicit immune responses in vivo without considering TCR as a key factor. This inevitably causes costly and time-consuming experimental validation test for predicted antigens. Therefore, it is necessary to develop novel computational methods for precisely and effectively predicting immunogenic peptide recognized by TCR. Here, we described DLpTCR, a multimodal ensemble deep learning framework for predicting the likelihood of interaction between single/paired chain(s) of TCR and peptide presented by major histocompatibility complex molecules. To investigate the generality and robustness of the proposed model, COVID-19 data and IEDB data were constructed for independent evaluation. The DLpTCR model exhibited high predictive power with area under the curve up to 0.91 on COVID-19 data while predicting the interaction between peptide and single TCR chain. Additionally, the DLpTCR model achieved the overall accuracy of 81.03% on IEDB data while predicting the interaction between peptide and paired TCR chains. The results demonstrate that DLpTCR has the ability to learn general interaction rules and generalize to antigen peptide recognition by TCR. A user-friendly webserver is available at http://jianglab.org.cn/DLpTCR/. Additionally, a stand-alone software package that can be downloaded from https://github.com/jiangBiolab/DLpTCR.

ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides.

Viruses represent one of the greatest threats to human health, necessitating the development of new antiviral drug candidates. Antiviral peptides often possess excellent biological activity and a favourable toxicity profile, and therefore represent a promising field of novel antiviral drugs. As the quantity of sequencing data grows annually, the development of an accurate in silico method for the prediction of peptide antiviral activities is important. This study leverages advances in deep learning and cheminformatics to produce a novel sequence-based deep neural network classifier for the prediction of antiviral peptide activity. The method outperforms the existent best-in-class, with an external test accuracy of 93.9%, Matthews correlation coefficient of 0.87 and an Area Under the Curve of 0.93 on the dataset of experimentally validated peptide activities. This cutting-edge classifier is available as an online web server at https://research.timmons.eu/ennavia, facilitating in silico screening and design of peptide antiviral drugs by the wider research community.

Short Report: Early genomic detection of SARS-CoV-2 P.1 variant in Northeast Brazil.

Tracking the spread of SARS-CoV-2 variants of concern is crucial to inform public health efforts and control the ongoing pandemic. Here, we report genetic evidence for circulation of the P.1 variant in Northeast Brazil. We advocate for increased active surveillance to ensure adequate control of this variant throughout the country.

AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches.

Antiviral peptide (AVP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against virus infection. Machine learning-based prediction with a computational biology approach can facilitate the development of the novel therapeutic agents. In this study, we proposed a double-stage classification scheme, named AVPIden, for predicting the AVPs and their functional activities against different viruses. The first stage is to distinguish the AVP from a broad-spectrum peptide collection, including not only the regular peptides (non-AMP) but also the AMPs without antiviral functions (non-AVP). The second stage is responsible for characterizing one or more virus families or species that the AVP targets. Imbalanced learning is utilized to improve the performance of prediction. The AVPIden uses multiple descriptors to precisely demonstrate the peptide properties and adopts explainable machine learning strategies based on Shapley value to exploit how the descriptors impact the antiviral activities. Finally, the evaluation performance of the proposed model suggests its ability to predict the antivirus activities and their potential functions against six virus families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight kinds of virus (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). The AVPIden gives an option for reinforcing the development of AVPs with the computer-aided method and has been deployed at http://awi.cuhk.edu.cn/AVPIden/.

A Comparative Analysis of Coronavirus Nucleocapsid (N) Proteins Reveals the SADS-CoV N Protein Antagonizes IFN-ß Production by Inducing Ubiquitination of RIG-I.

Coronaviruses (CoVs) are a known global threat, and most recently the ongoing COVID-19 pandemic has claimed more than 2 million human lives. Delays and interference with IFN responses are closely associated with the severity of disease caused by CoV infection. As the most abundant viral protein in infected cells just after the entry step, the CoV nucleocapsid (N) protein likely plays a key role in IFN interruption. We have conducted a comprehensive comparative analysis and report herein that the N proteins of representative human and animal CoVs from four different genera [swine acute diarrhea syndrome CoV (SADS-CoV), porcine epidemic diarrhea virus (PEDV), severe acute respiratory syndrome CoV (SARS-CoV), SARS-CoV-2, Middle East respiratory syndrome CoV (MERS-CoV), infectious bronchitis virus (IBV) and porcine deltacoronavirus (PDCoV)] suppress IFN responses by multiple strategies. In particular, we found that the N protein of SADS-CoV interacted with RIG-I independent of its RNA binding activity, mediating K27-, K48- and K63-linked ubiquitination of RIG-I and its subsequent proteasome-dependent degradation, thus inhibiting the host IFN response. These data provide insight into the interaction between CoVs and host, and offer new clues for the development of therapies against these important viruses.

Tracking the pipeline: immunoinformatics and the COVID-19 vaccine design.

With the onset of the COVID-19 pandemic, the amount of data on genomic and proteomic sequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) stored in various databases has exponentially grown. A large volume of these data has led to the production of equally immense sets of immunological data, which require rigorous computational approaches to sort through and make sense of. Immunoinformatics has emerged in the recent decades as a field capable of offering this approach by bridging experimental and theoretical immunology with state-of-the-art computational tools. Here, we discuss how immunoinformatics can assist in the development of high-performance vaccines and drug discovery needed to curb the spread of SARS-CoV-2. Immunoinformatics can provide a set of computational tools to extract meaningful connections from the large sets of COVID-19 patient data, which can be implemented in the design of effective vaccines. With this in mind, we represent a pipeline to identify the role of immunoinformatics in COVID-19 treatment and vaccine development. In this process, a number of free databases of protein sequences, structures and mutations are introduced, along with docking web servers for assessing the interaction between antibodies and the SARS-CoV-2 spike protein segments as most commonly considered antigens in vaccine design.

ACE2 models of frequently contacted animals provide clues of their SARS-CoV-2 S protein affinity and viral susceptibility.

The outbreak of atypical pneumonia (coronavirus disease 2019 [COVID-19]) has been a global pandemic and has caused severe losses to the global economy. The virus responsible for COVID-9, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has a spike glycoprotein (S protein) that binds angiotensin-converting enzyme 2 (ACE2) present on host cell membranes to gain entry. Based on the full-length human ACE2 cryo-EM structure, we generated homology models of full-length ACE2 proteins from various species (gorilla, monkey, pig, bovine, sheep, cat, dog, mouse, and rat). Although these ACE2 molecules were found to share similar overall structures, their S-ACE2 interface residues differed. These differences likely result in variations in the ACE2 binding affinities to the SARS-CoV-2 S protein. The highest affinities are predicted for human, gorilla, and monkey, while mouse and rat ACE2 are predicted to have the lowest affinities. Cat ACE2 is predicted to have a lower S protein affinity than dog ACE2. Although affinity is not the only factor that affects viral susceptibility, it is one of the most important factors. Thus, we believe that care should be taken with these animals to prevent the spread of SARS-CoV-2 among animal and human populations.

Immunoinformatic analysis of structural and epitope variations in the spike and Orf8 proteins of SARS-CoV-2/B.1.1.7.

A newly emerged strain of SARS-CoV-2 of B.1.1.7 lineage has caused a significant surge in the SARS-CoV-2 infections in the UK. In this study, changes in the epitopes of spike and orf8 proteins in SARS-CoV-2 of B.1.1.7 lineage were investigated. Genomic alignment of the SARS-CoV-2/B.1.1.7 with SARS-CoV-2/Wuhan showed the presence of several mutations in orf1a/b, spike, orf8, and N proteins of SARS-CoV-2/B.1.1.7. Molecular models of spike and orf8 proteins were constructed by homology modeling. Superimposition between the spike proteins of SARS-CoV-2/Wuhan and SARS-CoV-2/B.1.1.7 showed noticeable variations in the spatial orientation in Val70-Asn74 and Thr250-Ser255 regions. This may have also resulted in the extension of the epitopic region at Ser244-Gly249 in the SARS-CoV-2/B.1.1.7 spike protein. Superimposition of the SARS-CoV-2/B.1.1.7 spike protein over Fab-spike protein complexes of SARS-CoV-2/Wuhan also showed subtle variations in the antibody binding affinity targeting the N-terminal domain of the spike protein. Epitopic variations were also observed between the corresponding orf8 regions of SARS-CoV-2/Wuhan and SARS-CoV-2/B.1.1.7. Moreover, the presence of a stop codon at position 27 in orf8 connotes the emergence of two frames (orf8a and orf8b) in SARS-CoV-2, which further hampers its extracellular secretion, and in turn, immunogenicity. The findings of the present study could further be used to develop targeted immunotherapeutics.

Prediction and Evolution of the Molecular Fitness of SARS-CoV-2 Variants: Introducing SpikePro.

The understanding of the molecular mechanisms driving the fitness of the SARS-CoV-2 virus and its mutational evolution is still a critical issue. We built a simplified computational model, called SpikePro, to predict the SARS-CoV-2 fitness from the amino acid sequence and structure of the spike protein. It contains three contributions: the inter-human transmissibility of the virus predicted from the stability of the spike protein, the infectivity computed in terms of the affinity of the spike protein for the ACE2 receptor, and the ability of the virus to escape from the human immune response based on the binding affinity of the spike protein for a set of neutralizing antibodies. Our model reproduces well the available experimental, epidemiological and clinical data on the impact of variants on the biophysical characteristics of the virus. For example, it is able to identify circulating viral strains that, by increasing their fitness, recently became dominant at the population level. SpikePro is a useful, freely available instrument which predicts rapidly and with good accuracy the dangerousness of new viral strains. It can be integrated and play a fundamental role in the genomic surveillance programs of the SARS-CoV-2 virus that, despite all the efforts, remain time-consuming and expensive.

Peptide microarray-based analysis of antibody responses to SARS-CoV-2 identifies unique epitopes with potential for diagnostic test development.

Humoral immunity to the Severe Adult Respiratory Syndrome (SARS) Coronavirus (CoV)-2 is not fully understood yet but is a crucial factor of immune protection. The possibility of antibody cross-reactivity between SARS-CoV-2 and other human coronaviruses (HCoVs) would have important implications for immune protection but also for the development of specific diagnostic ELISA tests. Using peptide microarrays, n = 24 patient samples and n = 12 control samples were screened for antibodies against the entire SARS-CoV-2 proteome as well as the Spike (S), Nucleocapsid (N), VME1 (V), R1ab, and Protein 3a (AP3A) of the HCoV strains SARS, MERS, OC43, and 229E. While widespread cross-reactivity was revealed across several immunodominant regions of S and N, IgG binding to several SARS-CoV-2-derived peptides provided statistically significant discrimination between COVID-19 patients and controls. Selected target peptides may serve as capture antigens for future, highly COVID-19-specific diagnostic antibody tests.