Charting and tracking the evolution of the SARS CoV-2 coronavirus variants of concern with protein mass spectrometry.

The evolution of the SARS-CoV2 coronavirus spike S-protein is studied using a mass spectrometry based protein phylogenetic approach. A study of a large dataset comprising sets of peptide masses derived from over 3000 proteins of the SARS-CoV2 virus shows that the approach is capable of resolving and correctly displaying the evolution of the major variants of concern. Using these numerical datasets, through a pairwise comparison of sets of proteolytic peptide masses for each protein, the tree is built without the need for the sequence data itself or any sequence alignment. In the same analysis, single point mutations are calculated from peptide mass differences of different protein sets and these are displayed at the branch nodes on the tree. The tree topology is found to be consistent with that generated using conventional sequence-based phylogenetics by a manual visualisation and using a tree comparison algorithm. The mass tree resolves major variants of the virus and displays non-synonymous mutations, calculated based on the mass data alone, on the tree that enable protein evolution to be charted and tracked along interconnected branches. Tracking the evolution of the SARS-CoV2 coronavirus S-protein is of particular importance given its role in the attachment of the virus to host cells ahead of viral replication.

Highly conserved binding region of ACE2 as a receptor for SARS-CoV-2 between humans and mammals.

Several cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection transmitted from human owners to their dogs have recently been reported. The first ever case of SARS-CoV-2 transmission from a human owner to a domestic cat was confirmed on March 27, 2020. A tiger from a zoo in New York, USA, was also reportedly infected with SARS-CoV-2. It is believed that SARS-CoV-2 was transmitted to tigers from their caretakers, who were previously infected with this virus. On May 25, 2020, the Dutch Minister of Agriculture, Nature and Food Quality reported that two employees were infected with SARS-CoV-2 transmitted from minks. These reports have influenced us to perform a comparative analysis among angiotensin-converting enzyme 2 (ACE2) homologous proteins for verifying the conservation of specific protein regions. One of the most conserved peptides is represented by the peptide "353-KGDFR-357 (H. sapiens ACE2 residue numbering), which is located on the surface of the ACE2 molecule and participates in the binding of SARS-CoV-2 spike receptor binding domain (RBD). Multiple sequence alignments of the ACE2 proteins by ClustalW, whereas the three-dimensional structure of its binding region for the spike glycoprotein of SARS-CoV-2 was assessed by means of Spanner, a structural homology modeling pipeline method. In addition, evolutionary phylogenetic tree analysis by ETE3 was used. ACE2 works as a receptor for the SARS-CoV-2 spike glycoprotein between humans, dogs, cats, tigers, minks, and other animals, except for snakes. The three-dimensional structure of the KGDFR hosting protein region involved in direct interactions with SARS-CoV-2 spike RBD of the mink ACE2 appears to form a loop structurally related to the human ACE2 corresponding protein loop, despite of the reduced available protein length (401 residues of the mink ACE2 available sequence vs 805 residues of the human ACE2). The multiple sequence alignments of the ACE2 proteins shows high homology and complete conservation of the five amino acid residues: 353-KGDFR-357 with humans, dogs, cats, tigers, minks, and other animals, except for snakes. Where the information revealed from our examinations can support precision vaccine design and the discovery of antiviral therapeutics, which will accelerate the development of medical countermeasures, the World Health Organization recently reported on the possible risks of reciprocal infections regarding SARS-CoV-2 transmission from animals to humans.

Targetable elements in SARS-CoV-2 S2 subunit for the design of pan-coronavirus fusion inhibitors and vaccines.

The ongoing global pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused devastating impacts on the public health and the global economy. Rapid viral antigenic evolution has led to the continual generation of new variants. Of special note is the recently expanding Omicron subvariants that are capable of immune evasion from most of the existing neutralizing antibodies (nAbs). This has posed new challenges for the prevention and treatment of COVID-19. Therefore, exploring broad-spectrum antiviral agents to combat the emerging variants is imperative. In sharp contrast to the massive accumulation of mutations within the SARS-CoV-2 receptor-binding domain (RBD), the S2 fusion subunit has remained highly conserved among variants. Hence, S2-based therapeutics may provide effective cross-protection against new SARS-CoV-2 variants. Here, we summarize the most recently developed broad-spectrum fusion inhibitors (e.g., nAbs, peptides, proteins, and small-molecule compounds) and candidate vaccines targeting the conserved elements in SARS-CoV-2 S2 subunit. The main focus includes all the targetable S2 elements, namely, the fusion peptide, stem helix, and heptad repeats 1 and 2 (HR1-HR2) bundle. Moreover, we provide a detailed summary of the characteristics and action-mechanisms for each class of cross-reactive fusion inhibitors, which should guide and promote future design of S2-based inhibitors and vaccines against new coronaviruses.

An accessible infrastructure for artificial intelligence using a Docker-based JupyterLab in Galaxy.

BACKGROUND: Artificial intelligence (AI) programs that train on large datasets require powerful compute infrastructure consisting of several CPU cores and GPUs. JupyterLab provides an excellent framework for developing AI programs, but it needs to be hosted on such an infrastructure to enable faster training of AI programs using parallel computing. FINDINGS: An open-source, docker-based, and GPU-enabled JupyterLab infrastructure is developed that runs on the public compute infrastructure of Galaxy Europe consisting of thousands of CPU cores, many GPUs, and several petabytes of storage to rapidly prototype and develop end-to-end AI projects. Using a JupyterLab notebook, long-running AI model training programs can also be executed remotely to create trained models, represented in open neural network exchange (ONNX) format, and other output datasets in Galaxy. Other features include Git integration for version control, the option of creating and executing pipelines of notebooks, and multiple dashboards and packages for monitoring compute resources and visualization, respectively. CONCLUSIONS: These features make JupyterLab in Galaxy Europe highly suitable for creating and managing AI projects. A recent scientific publication that predicts infected regions in COVID-19 computed tomography scan images is reproduced using various features of JupyterLab on Galaxy Europe. In addition, ColabFold, a faster implementation of AlphaFold2, is accessed in JupyterLab to predict the 3-dimensional structure of protein sequences. JupyterLab is accessible in 2 ways-one as an interactive Galaxy tool and the other by running the underlying Docker container. In both ways, long-running training can be executed on Galaxy's compute infrastructure. Scripts to create the Docker container are available under MIT license at https://github.com/usegalaxy-eu/gpu-jupyterlab-docker.

Sars-escape network for escape prediction of SARS-COV-2.

MOTIVATION: Viruses have coevolved with their hosts for over millions of years and learned to escape the host's immune system. Although not all genetic changes in viruses are deleterious, some significant mutations lead to the escape of neutralizing antibodies and weaken the immune system, which increases infectivity and transmissibility, thereby impeding the development of antiviral drugs or vaccines. Accurate and reliable identification of viral escape mutational sequences could be a good indicator for therapeutic design. We developed a computational model that recognizes significant mutational sequences based on escape feature identification using natural language processing along with prior knowledge of experimentally validated escape mutants. RESULTS: Our machine learning-based computational approach can recognize the significant spike protein sequences of severe acute respiratory syndrome coronavirus 2 using sequence data alone. This modelling approach can be applied to other viruses, such as influenza, monkeypox and HIV using knowledge of escape mutants and relevant protein sequence datasets. AVAILABILITY: Complete source code and pre-trained models for escape prediction of severe acute respiratory syndrome coronavirus 2 protein sequences are available on Github at https://github.com/PremSinghBist/Sars-CoV-2-Escape-Model.git. The dataset is deposited to Zenodo at: doi: 10.5281/zenodo.7142638. The Python scripts are easy to run and customize as needed. CONTACT: premsing212@jbnu.ac.kr.

Allosteric regulation and crystallographic fragment screening of SARS-CoV-2 NSP15 endoribonuclease.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19). The NSP15 endoribonuclease enzyme, known as NendoU, is highly conserved and plays a critical role in the ability of the virus to evade the immune system. NendoU is a promising target for the development of new antiviral drugs. However, the complexity of the enzyme's structure and kinetics, along with the broad range of recognition sequences and lack of structural complexes, hampers the development of inhibitors. Here, we performed enzymatic characterization of NendoU in its monomeric and hexameric form, showing that hexamers are allosteric enzymes with a positive cooperative index, and with no influence of manganese on enzymatic activity. Through combining cryo-electron microscopy at different pHs, X-ray crystallography and biochemical and structural analysis, we showed that NendoU can shift between open and closed forms, which probably correspond to active and inactive states, respectively. We also explored the possibility of NendoU assembling into larger supramolecular structures and proposed a mechanism for allosteric regulation. In addition, we conducted a large fragment screening campaign against NendoU and identified several new allosteric sites that could be targeted for the development of new inhibitors. Overall, our findings provide insights into the complex structure and function of NendoU and offer new opportunities for the development of inhibitors.

Structure-based design of a SARS-CoV-2 Omicron-specific inhibitor.

The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) introduced a relatively large number of mutations, including three mutations in the highly conserved heptad repeat 1 (HR1) region of the spike glycoprotein (S) critical for its membrane fusion activity. We show that one of these mutations, N969K induces a substantial displacement in the structure of the heptad repeat 2 (HR2) backbone in the HR1HR2 postfusion bundle. Due to this mutation, fusion-entry peptide inhibitors based on the Wuhan strain sequence are less efficacious. Here, we report an Omicron-specific peptide inhibitor designed based on the structure of the Omicron HR1HR2 postfusion bundle. Specifically, we inserted an additional residue in HR2 near the Omicron HR1 K969 residue to better accommodate the N969K mutation and relieve the distortion in the structure of the HR1HR2 postfusion bundle it introduced. The designed inhibitor recovers the loss of inhibition activity of the original longHR2_42 peptide with the Wuhan strain sequence against the Omicron variant in both a cell-cell fusion assay and a vesicular stomatitis virus (VSV)-SARS-CoV-2 chimera infection assay, suggesting that a similar approach could be used to combat future variants. From a mechanistic perspective, our work suggests the interactions in the extended region of HR2 may mediate the initial landing of HR2 onto HR1 during the transition of the S protein from the prehairpin intermediate to the postfusion state.

Bioactive Peptides in Human Health and Disease.

Bioactive peptides are defined as short amino acid sequences that may have specific physiological functions, ultimately affecting human health and protecting against the development of several diseases [...].

Methyltransferases of Riboviria.

Many viruses from the realm Riboviria infecting eukaryotic hosts encode protein domains with sequence similarity to S-adenosylmethionine-dependent methyltransferases. These protein domains are thought to be involved in methylation of the 5'-terminal cap structures in virus mRNAs. Some methyltransferase-like domains of Riboviria are homologous to the widespread cellular FtsJ/RrmJ-like methyltransferases involved in modification of cellular RNAs; other methyltransferases, found in a subset of positive-strand RNA viruses, have been assigned to a separate "Sindbis-like" family; and coronavirus-specific Nsp13/14-like methyltransferases appeared to be different from both those classes. The representative structures of proteins from all three groups belong to a specific variety of the Rossmann fold with a seven-stranded ß-sheet, but it was unclear whether this structural similarity extends to the level of conserved sequence signatures. Here I survey methyltransferases in Riboviria and derive a joint sequence alignment model that covers all groups of virus methyltransferases and subsumes the previously defined conserved sequence motifs. Analysis of the spatial structures indicates that two highly conserved residues, a lysine and an aspartate, frequently contact a water molecule, which is located in the enzyme active center next to the methyl group of S-adenosylmethionine cofactor and could play a key role in the catalytic mechanism of the enzyme. Phylogenetic evidence indicates a likely origin of all methyltransferases of Riboviria from cellular RrmJ-like enzymes and their rapid divergence with infrequent horizontal transfer between distantly related viruses.

Functional antibodies exhibit light chain coherence.

The vertebrate adaptive immune system modifies the genome of individual B cells to encode antibodies that bind particular antigens¹. In most mammals, antibodies are composed of heavy and light chains that are generated sequentially by recombination of V, D (for heavy chains), J and C gene segments. Each chain contains three complementarity-determining regions (CDR1-CDR3), which contribute to antigen specificity. Certain heavy and light chains are preferred for particular antigens^2-22. Here we consider pairs of B cells that share the same heavy chain V gene and CDRH3 amino acid sequence and were isolated from different donors, also known as public clonotypes^23,24. We show that for naive antibodies (those not yet adapted to antigens), the probability that they use the same light chain V gene is around 10%, whereas for memory (functional) antibodies, it is around 80%, even if only one cell per clonotype is used. This property of functional antibodies is a phenomenon that we call light chain coherence. We also observe this phenomenon when similar heavy chains recur within a donor. Thus, although naive antibodies seem to recur by chance, the recurrence of functional antibodies reveals surprising constraint and determinism in the processes of V(D)J recombination and immune selection. For most functional antibodies, the heavy chain determines the light chain.

Integrated mRNA sequence optimization using deep learning.

The coronavirus disease of 2019 pandemic has catalyzed the rapid development of mRNA vaccines, whereas, how to optimize the mRNA sequence of exogenous gene such as severe acute respiratory syndrome coronavirus 2 spike to fit human cells remains a critical challenge. A new algorithm, iDRO (integrated deep-learning-based mRNA optimization), is developed to optimize multiple components of mRNA sequences based on given amino acid sequences of target protein. Considering the biological constraints, we divided iDRO into two steps: open reading frame (ORF) optimization and 5' untranslated region (UTR) and 3'UTR generation. In ORF optimization, BiLSTM-CRF (bidirectional long-short-term memory with conditional random field) is employed to determine the codon for each amino acid. In UTR generation, RNA-Bart (bidirectional auto-regressive transformer) is proposed to output the corresponding UTR. The results show that the optimized sequences of exogenous genes acquired the pattern of human endogenous gene sequence. In experimental validation, the mRNA sequence optimized by our method, compared with conventional method, shows higher protein expression. To the best of our knowledge, this is the first study by introducing deep-learning methods to integrated mRNA sequence optimization, and these results may contribute to the development of mRNA therapeutics.

SARS-CoV-2 E protein: Pathogenesis and potential therapeutic development.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a devastating global pandemic, which has seriously affected human health worldwide. The discovery of therapeutic agents is extremely urgent, and the viral structural proteins are particularly important as potential drug targets. SARS-CoV-2 envelope (E) protein is one of the main structural proteins of the virus, which is involved in multiple processes of the virus life cycle and is directly related to pathogenesis process. In this review, we present the amino acid sequence of the E protein and compare it with other two human coronaviruses. We then explored the role of E protein in the viral life cycle and discussed the pathogenic mechanisms that E protein may be involved in. Next, we summarize the potential drugs against E protein discovered in the current studies. Finally, we described the possible effects of E protein mutation on virus and host. This established a knowledge system of E protein to date, aiming to provide theoretical insights for mitigating the current COVID-19 pandemic and potential future coronavirus outbreaks.

Unravelling lead antiviral phytochemicals for the inhibition of SARS-CoV-2 Mpro enzyme through in silico approach.

A new SARS coronavirus (SARS-CoV-2) belonging to the genus Betacoronavirus has caused a pandemic known as COVID-19. Among coronaviruses, the main protease (Mpro) is an essential drug target which, along with papain-like proteases catalyzes the processing of polyproteins translated from viral RNA and recognizes specific cleavage sites. There are no human proteases with similar cleavage specificity and therefore, inhibitors are highly likely to be nontoxic. Therefore, targeting the SARS-CoV-2 Mpro enzyme with small molecules can block viral replication. The present study is aimed at the identification of promising lead molecules for SARS-CoV-2 Mpro enzyme through virtual screening of antiviral compounds from plants. The binding affinity of selected small drug-like molecules to SARS-CoV-2 Mpro, SARS-CoV Mpro and MERS-CoV Mpro were studied using molecular docking. Bonducellpin D was identified as the best lead molecule which shows higher binding affinity (-9.28 kcal/mol) as compared to the control (-8.24 kcal/mol). The molecular binding was stabilized through four hydrogen bonds with Glu166 and Thr190 as well as hydrophobic interactions via eight residues. The SARS-CoV-2 Mpro shows identities of 96.08% and 50.65% to that of SARS-CoV Mpro and MERS-CoV Mpro respectively at the sequence level. At the structural level, the root mean square deviation (RMSD) between SARS-CoV-2 Mpro and SARS-CoV Mpro was found to be 0.517 Å and 0.817 Å between SARS-CoV-2 Mpro and MERS-CoV Mpro. Bonducellpin D exhibited broad-spectrum inhibition potential against SARS-CoV Mpro and MERS-CoV Mpro and therefore is a promising drug candidate, which needs further validations through in vitro and in vivo studies.

Mutations in SARS-CoV-2 structural proteins: a global analysis.

BACKGROUND: Emergence of new variants mainly variants of concerns (VOC) is caused by mutations in main structural proteins of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Therefore, we aimed to investigate the mutations among structural proteins of SARS-CoV-2 globally. METHODS: We analyzed samples of amino-acid sequences (AASs) for envelope (E), membrane (M), nucleocapsid (N), and spike (S) proteins from the declaration of the coronavirus 2019 (COVID-19) as pandemic to January 2022. The presence and location of mutations were then investigated by aligning the sequences to the reference sequence and categorizing them based on frequency and continent. Finally, the related human genes with the viral structural genes were discovered, and their interactions were reported. RESULTS: The results indicated that the most relative mutations among the E, M, N, and S AASs occurred in the regions of 7 to 14, 66 to 88, 164 to 205, and 508 to 635 AAs, respectively. The most frequent mutations in E, M, N, and S proteins were T9I, I82T, R203M/R203K, and D614G. D614G was the most frequent mutation in all six geographical areas. Following D614G, L18F, A222V, E484K, and N501Y, respectively, were ranked as the most frequent mutations in S protein globally. Besides, A-kinase Anchoring Protein 8 Like (AKAP8L) was shown as the linkage unit between M, E, and E cluster genes. CONCLUSION: Screening the structural protein mutations can help scientists introduce better drug and vaccine development strategies.

AbAgIntPre: A deep learning method for predicting antibody-antigen interactions based on sequence information.

Introduction: Antibody-mediated immunity is an essential part of the immune system in vertebrates. The ability to specifically bind to antigens allows antibodies to be widely used in the therapy of cancers and other critical diseases. A key step in antibody therapeutics is the experimental identification of antibody-antigen interactions, which is generally time-consuming, costly, and laborious. Although some computational methods have been proposed to screen potential antibodies, the dependence on 3D structures still limits the application of these methods. Methods: Here, we developed a deep learning-assisted prediction method (i.e., AbAgIntPre) for fast identification of antibody-antigen interactions that only relies on amino acid sequences. A Siamese-like convolutional neural network architecture was established with the amino acid composition encoding scheme for both antigens and antibodies. Results and Discussion: The generic model of AbAgIntPre achieved satisfactory performance with the Area Under Curve (AUC) of 0.82 on a high-quality generic independent test dataset. Besides, this approach also showed competitive performance on the more specific SARS-CoV dataset. We expect that AbAgIntPre can serve as an important complement to traditional experimental methods for antibody screening and effectively reduce the workload of antibody design. The web server of AbAgIntPre is freely available at http://www.zzdlab.com/AbAgIntPre.

A new intracellular targeting motif in the cytoplasmic tail of the spike protein may act as a target to inhibit SARS-CoV-2 assembly.

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a threat to global public health, underscoring the urgent need for the development of preventive and therapeutic measures. The spike (S) protein of SARS-CoV-2, which mediates receptor binding and subsequent membrane fusion to promote viral entry, is a major target for current drug development and vaccine design. The S protein comprises a large N-terminal extracellular domain, a transmembrane domain, and a short cytoplasmic tail (CT) at the C-terminus. CT truncation of the S protein has been previously reported to promote the infectivity of SARS-CoV and SARS-CoV-2 pseudoviruses. However, the underlying molecular mechanism has not been precisely elucidated. In addition, the CT of various viral membrane glycoproteins play an essential role in the assembly of virions, yet the role of the S protein CT in SARS-CoV-2 infection remains unclear. In this study, through constructing a series of mutations of the CT of the S protein and analyzing their impact on the packaging of the SARS-CoV-2 pseudovirus and live SARS-CoV-2 virus, we identified V1264L1265 as a new intracellular targeting motif in the CT of the S protein, that regulates the transport and subcellular localization of the spike protein through the interactions with cytoskeleton and vesicular transport-related proteins, ARPC3, SCAMP3, and TUBB8, thereby modulating SARS-CoV-2 pseudovirus and live SARS-CoV-2 virion assembly. Either disrupting the V1264L1265 motif or reducing the expression of ARPC3, SCAMP3, and TUBB8 significantly repressed the assembly of the live SARS-CoV-2 virion, raising the possibility that the V1264L1265 motif and the host responsive pathways involved could be new drug targets for the treatment of SARS-CoV-2 infection. Our results extend the understanding of the role played by the S protein CT in the assembly of pseudoviruses and live SARS-CoV-2 virions, which will facilitate the application of pseudoviruses to the study of SARS-CoV-2 and provide potential strategies for the treatment of SARS-CoV-2 infection.

Exploring Highly Conserved Regions of SARS-CoV-2 Spike S2 Subunit as Targets for Fusion Inhibition Using Chimeric Proteins.

Since the beginning of the COVID-19 pandemic, considerable efforts have been made to develop protective vaccines against SARS-CoV-2 infection. However, immunity tends to decline within a few months, and new virus variants are emerging with increased transmissibility and capacity to evade natural or vaccine-acquired immunity. Therefore, new robust strategies are needed to combat SARS-CoV-2 infection. The viral spike composed of S1 and S2 subunits mediates viral attachment and membrane fusion to infect the host cell. In this process, interaction between the highly conserved heptad repeat 1 and 2 regions (HR1 and HR2) of S2 is crucial and for this reason; these regions are promising targets to fight SARS-CoV-2. Here, we describe the design and characterization of chimeric proteins that structurally imitate the S2 HR1 region in a trimeric coiled-coil conformation. We biophysically characterized the proteins and determined their capacity to bind the HR2 region, as well as their inhibitory activity of SARS-CoV-2 infection in vitro. HR1 mimetic proteins showed conformational heterogeneity and a propensity to form oligomers. Moreover, their structure is composed of subdomains with varied stability. Interestingly, the full HR1 proteins showed high affinity for HR2-derived peptides and SARS-CoV-2 inhibitory activity, whereas smaller proteins mimicking HR1 subdomains had a decreased affinity for their complementary HR2 region and did not inhibit the virus. The results provide insight into effective strategies to create mimetic proteins with broad inhibitory activity and therapeutic potential against SARS-CoV-2.

SARS-CoV-2 ORF8: One protein, seemingly one structure, and many functions.

SARS-CoV-2 is the virus responsible for the COVID-19 pandemic. The genome of SARS-CoV-2 encodes nine accessory proteins that are involved in host-pathogen interaction. ORF8 is unique among these accessory proteins. SARS-CoV-2 ORF8 shares a surprisingly low amino acid sequence similarity with SARS-COV ORF8 (30%), and it is presumed to have originated from bat. Studies have shown that ORF8 exerts multiple different functions that interfere with host immune responses, including the downregulation of MHC class I molecules. These functions may represent strategies of host immune evasion. The x-ray crystal structure of ORF8 revealed an immunoglobulin-like domain with several distinguishing features. To date, there are numerous unanswered questions about SARS-CoV-2 ORF8 protein and its structure-function relationship that we discuss in this mini-review. A better understanding of how ORF8 interacts with components of the immune system is needed for elucidating COVID-19 pathogenesis and to develop new avenues for the treatment of the disease.