Viral Protein Sequences Research Articles

VPAgs-Dataset4ML: A Dataset to Predict Viral Protective Antigens for Machine Learning-Based Reverse Vaccinology

Reverse vaccinology (RV) is a computer-aided approach for vaccine development that identifies a subset of pathogen proteins as protective antigens (PAgs) or potential vaccine candidates. Machine learning (ML)-based RV is promising, but requires a dataset of PAgs (positives) and non-protective protein sequences (negatives). This study aimed to create an ML dataset, VPAgs-Dataset4ML, to predict viral PAgs based on PAgs obtained from Protegen. We performed seven steps to identify PAgs from the Protegen website and non-protective protein sequences from Universal Protein Resource (UniProt). The seven steps included downloading viral PAgs from Protegen, performing quality checks on PAgs using the standard BLASTp identity check ≤30% via MMseqs2, and computational steps running on Google Colaboratory and the Ubuntu terminal to retrieve and perform quality checks (similar to the PAgs) on non-protective protein sequences as negatives from UniProt. VPAgs-Dataset4ML contains 2145 viral protein sequences, with 210 PAgs in positive.fasta and 1935 non-protective protein sequences in negative.fasta. This dataset can be used to train ML models to predict antigens for various viral pathogens with the aim of developing effective vaccines.

Automated SSHHPS Analysis Predicts a Potential Host Protein Target Common to Several Neuroinvasive (+)ssRNA Viruses.

Within the viral genome, short stretches of homologous host pathogen sequences (SSHHPS) span the protease cleavage sites. To identify host proteins that may be cleaved during infection, we searched the human proteome for viral protease cleavage sites (~20 amino acids). We developed a sequence-to-symptom tool, automating the search and pairing process. We used the viral protein sequence, PHI-BLAST, and UniProt database for gene ontologies and disease relationships. We applied the tool to nine neuroinvasive viruses: Venezuelan and Eastern Equine encephalitis virus (VEEV, EEEV); severe acute respiratory syndrome (SARS, SARS-CoV-2); Middle East respiratory syndrome (MERS); EV-71; Japanese encephalitis virus (JEV); West Nile (WNV); and Zika (ZIKV). A comparison of the hits identified a protein common to all nine viruses called ADGRA2 (GPR124). ADGRA2 was a predicted hit of the 3CL main protease and papain-like protease (PLpro) of SARS-CoV-2. ADGRA2 is an adhesion G protein-coupled receptor and a key endothelial regulator of brain-specific angiogenesis. It is a Wnt7A/Wnt7B specific coactivator of beta-catenin signaling and is essential for blood-brain barrier (BBB) integrity in central nervous system (CNS) diseases. We show the cleavage of the predicted sequences in MYOM1, VWF by the SARS-CoV-2 PLpro; DNAH8 (dynein) by the MERS PLpro; ADGRA2 by the alphaviral VEEV nsP2 protease; and POT1 by the SARS-CoV-2 and MERS PLpro.

Detection and genetic diversity of porcine rotavirus A, B and C in eastern Australian piggeries.

Rotaviruses (RV) have a high prevalence in piggeries worldwide and are one of the major pathogens causing severe diarrhoea in young pigs. RV species A, B, and C have been linked to piglet diarrhoea in Australian pig herds, but their genetic diversity has not been studied in detail. Based on sequencing of the structural viral protein 7 (VP7) RVA G genotypes G3, G4 and G5, and RVC types G1, G3, G5, and G6 have been identified in Australian piggeries in previous studies. Although occurrence of RVB was reported in Australia in 1988, no further genetic analysis has been conducted. To improve health management decisions in Australian pig herds, more information on RV prevalence and genetic diversity is needed. Here, 243 enteric samples collected from 20 pig farms within Eastern Australia were analysed for the presence of RV in different age groups using a novel PCR-based multiplex assay (Pork MultiPath™ enteric panel). RVA, RVB, and RVC were detected in 10, 14, and 14 farms, respectively. Further sequencing of VP7 in selected RV-positive samples revealed G genotypes G2, G5, G9 (RVA), G6, G8, G14, G16, G20 (RVB), and G1, G3, G5, G6 (RVC) present. RVA was only detected in young (<10 weeks old) pigs whereas RVB and RVC were also detected in older animals (>11 weeks old). Interestingly, RVB and RVC G-type occurrence differed between age groups. In conclusion, this study provides new insights on the prevalence and diversity of different RV species in pig herds of Eastern Australia whilst demonstrating the ability of the Pork MultiPath™ technology to accurately differentiate between these RV species.

Hamlet-Pattern-Based Automated COVID-19 and Influenza Detection Model Using Protein Sequences.

SARS-CoV-2 and Influenza-A can present similar symptoms. Computer-aided diagnosis can help facilitate screening for the two conditions, and may be especially relevant and useful in the current COVID-19 pandemic because seasonal Influenza-A infection can still occur. We have developed a novel text-based classification model for discriminating between the two conditions using protein sequences of varying lengths. We downloaded viral protein sequences of SARS-CoV-2 and Influenza-A with varying lengths (all 100 or greater) from the NCBI database and randomly selected 16,901 SARS-CoV-2 and 19,523 Influenza-A sequences to form a two-class study dataset. We used a new feature extraction function based on a unique pattern, HamletPat, generated from the text of Shakespeare's Hamlet, and a signum function to extract local binary pattern-like bits from overlapping fixed-length (27) blocks of the protein sequences. The bits were converted to decimal map signals from which histograms were extracted and concatenated to form a final feature vector of length 1280. The iterative Chi-square function selected the 340 most discriminative features to feed to an SVM with a Gaussian kernel for classification. The model attained 99.92% and 99.87% classification accuracy rates using hold-out (75:25 split ratio) and five-fold cross-validations, respectively. The excellent performance of the lightweight, handcrafted HamletPat-based classification model suggests that it can be a valuable tool for screening protein sequences to discriminate between SARS-CoV-2 and Influenza-A infections.

Shared 6mer Peptides of Human and Omicron (21K and 21L) at SARS-CoV-2 Mutation Sites.

We investigated the short sequences involving Omicron 21K and Omicron 21L variants to reveal any possible molecular mimicry-associated autoimmunity risks and changes in those. We first identified common 6mers of the viral and human protein sequences present for both the mutant (Omicron) and nonmutant (SARS-CoV-2) versions of the same viral sequence and then predicted the binding affinities of those sequences to the HLA supertype representatives. We evaluated change in the potential autoimmunity risk, through comparative assessment of the nonmutant and mutant viral sequences and their similar human peptides with common 6mers and affinities to the same HLA allele. This change is the lost and the new, or de novo, autoimmunity risk, associated with the mutations in the Omicron 21K and Omicron 21L variants. Accordingly, e.g., the affinity of virus-similar sequences of the Ig heavy chain junction regions shifted from the HLA-B*15:01 to the HLA-A*01:01 allele at the mutant sequences. Additionally, peptides of different human proteins sharing 6mers with SARS-CoV-2 proteins at the mutation sites of interest and with affinities to the HLA-B*07:02 allele, such as the respective SARS-CoV-2 sequences, were lost. Among all, any possible molecular mimicry-associated novel risk appeared to be prominent in HLA-A*24:02 and HLA-B*27:05 serotypes upon infection with Omicron 21L. Associated disease, pathway, and tissue expression data supported possible new risks for the HLA-B*27:05 and HLA-A*01:01 serotypes, while the risks for the HLA-B*07:02 serotypes could have been lost or diminished, and those for the HLA-A*03:01 serotypes could have been retained, for the individuals infected with Omicron variants under study. These are likely to affect the complications related to cross-reactions influencing the relevant HLA serotypes upon infection with Omicron 21K and Omicron 21L.

Fatal canine parvovirus type 2a and 2c infections in wild Chinese pangolins (Manis pentadactyla) in southern China.

The Chinese pangolin (Manis pentadactyla) is a critically endangered scale-covered mammal belonging to the order Pholidota. Wild pangolins are notably susceptible to pathogen infection and are typically characterized by impoverished health. However, little is currently known regarding the viruses prevalent among pangolins. In this study, we report the detection of two subtypes of canine parvovirus type 2 (CPV-2), namely CPV-2a and CPV-2c, both of which caused severe diarrheal disease in two post-rescue pangolins with fatal consequences. As in CPV-2-infected dogs, intensive lesion of the mucosal layer of the small intestines is a prominent feature in infected pangolins. Moreover, the immunochemistry results demonstrated that CPV-2 antigens were distributed in the crypts of small intestine. Additionally, typical parvovirus-associated CPV-2 were detected after four passages in F81 cells, and typical parvovirus-like particles, approximately 20nm in diameter, were observed in the cell supernatants. Phylogenetic analysis revealed that the VP2 viral protein sequences (GenBank accession number OP208805) isolated from one pangolin (termed P1) were classified as CPV-2c, with 99.8% identity to a CPV-2c strain (MN832850) isolated from a Taiwanese pangolin found in Taiwan Province. In contrast, VP2 sequences (#OP208806) obtained from the second pangolin (P2) were classified as CPV-2a, with 99.8% identity to a CPV-2a strain (KY386858) isolated from southern China. In this study, we thus confirmed the infection of pangolins with CPV-2c in mainland China and demonstrated that CPV-2a also can infect pangolins. Based on these findings, we recommend that further investigations should be conducted to establish the interspecies transmission of these viruses among wild pangolins, wild carnivores, and stray dogs.

Designing of Chimeric Vaccine against Canine Distemper Virus Targeting Hemaglutanin Protein

The canine distemper virus is highly contagious and affects dogs' respiratory systems. The virus belongs to the paramyxoviridae family and order Mononegavirales. This class of viruses contains a negative-strand RNA. This virus also affects raccoons, foxes, and other animals. The current study aims to design a vaccine against the virus employing reverse vaccinology. The target candidate for the vaccine design is a surface protein called Hemagglutinin. Viral hemagglutinin protein sequences were retrieved from the Uniprot database, and conserved regions were identified. Possible B-cell epitope regions were identified using the ABCpred server. These epitopes were analyzed for allergenic and antigenic properties using the Allergen FP server and VaxiJen v2.0 server. The epitopes, which were antigenic and non-allergenic, were screened for T cell epitopes using NetMHC and NetMHC2 servers. The toxicity of the selected peptides was evaluated using the Toxinpred server. The epitopes were further screened for transmembrane helices and signal peptides employing TMHMM v. 2.0 and SignalP 4.1 servers, respectively. The epitopes were then checked for the parameters using the ProtParam tool. Finally, the solubility of the epitopes was determined using the SOLPro server. Using the selected epitopes, a chimeric vaccine construct was constructed with the peptides by linking the peptides with the GPGPG linker to the cholera toxin subunit B. The chimeric vaccine was modeled using the Robetta server, and codon optimization of the construct was performed using the JCAT tool.

Design of a novel multi-epitope vaccine candidate against hepatitis C virus using structural and nonstructural proteins: An immunoinformatics approach.

Hepatitis C virus (HCV) infects the liver and causes chronic infection. Several mutations in the viral genome have been associated with drug resistance development. Currently, there is no approved vaccine against the HCV. The employment of computational biology is the primary and crucial step for vaccine design or antiviral therapy which can substantially reduce the duration and cost of studies. Therefore, in this study, we designed a multi-epitope vaccine using various immunoinformatics tools to elicit the efficient human immune responses against the HCV. Initially, various potential (antigenic, immunogenic, non-toxic and non-allergenic) epitope segments were extracted from viral structural and non-structural protein sequences using multiple screening methods. The selected epitopes were linked to each other properly. Then, toll-like receptors (TLRs) 3 and 4 agonists (50S ribosomal protein L7/L12 and human β-defensin 2, respectively) were added to the N-terminus of the final vaccine sequence to increase its immunogenicity. The 3D structure of the vaccine was modeled. Molecular dynamics simulations studies verified the high stability of final free vaccines and in complex with TLR3 and TLR4. These constructs were also antigenic, non-allergenic, nontoxic and immunogenic. Although the designed vaccine traits were promising as a potential candidate against the HCV infection, experimental studies and clinical trials are required to verify the protective traits and safety of the designed vaccine.

Prediction of viral-host interactions of COVID-19 by computational methods

Experimental approaches are currently used to determine viral-host interactions, but these approaches are both time-consuming and costly. For these reasons, computational-based approaches are recommended. In this study, using computational-based approaches, viral-host interactions of SARS-CoV-2 virus and human proteins were predicted. The study consists of four different stages; in the first stage viral and host protein sequences were obtained. In the second stage, protein sequences were converted into numerical expressions by various protein mapping methods. These methods are entropy-based, AVL-tree, FIBHASH, binary encoding, CPNR, PAM250, BLOSUM62, Atchley factors, Meiler parameters, EIIP, AESNN1, Miyazawa energies, Micheletti potentials, Z-scale, and hydrophobicity. In the third stage, a deep learning model was designed and BiLSTM was used for this. In the last stage, the protein sequences were classified, and the viral-host interactions were predicted. The performances of protein mapping methods were determined by accuracy, F1-score, specificity, sensitivity, and AUC scores. According to the classification results, the best classification process was obtained by the entropy-based method. With this method, 94.74% accuracy, and 0.95 AUC score were calculated. Then, the most successful classification process was performed with the Z-scale and 91.23% accuracy, and 0.96 AUC score were obtained. Although other protein mapping methods are not as efficient as Z-scale and entropy-based methods, they have achieved successful classification. AVL-tree, FIBHASH, binary encoding, CPNR, PAM250, BLOSUM62, Atchley factors, Meiler parameters and AESNN1 methods showed over 80% accuracy, F1-score, and AUC score. Accuracy scores of EIIP, Miyazawa energies, Micheletti potentials and hydrophobicity methods remained below 80%. When the results were examined in general, it was observed that the computational approaches were successful in predicting viral-host interactions between SARS-CoV-2 virus and human proteins.

LGCA-VHPPI: A local-global residue context aware viral-host protein-protein interaction predictor.

Viral-host protein protein interaction (PPI) analysis is essential to decode the molecular mechanism of viral pathogen and host immunity processes which eventually help to control viral diseases and optimize therapeutics. The state-of-the-art viral-host PPI predictor leverages unsupervised embedding learning technique (doc2vec) to generate statistical representations of viral-host protein sequences and a Random Forest classifier for interaction prediction. However, doc2vec approach generates the statistical representations of viral-host protein sequences by merely modelling the local context of residues which only partially captures residue semantics. The paper in hand proposes a novel technique for generating better statistical representations of viral and host protein sequences based on the infusion of comprehensive local and global contextual information of the residues. While local residue context aware encoding captures semantic relatedness and short range dependencies of residues. Global residue context aware encoding captures comprehensive long-range residues dependencies, positional invariance of residues, and unique residue combination distribution important for interaction prediction. Using concatenated rich statistical representations of viral and host protein sequences, a robust machine learning framework “LGCA-VHPPI” is developed which makes use of a deep forest model to effectively model complex non-linearity of viral-host PPI sequences. An in-depth performance comparison of the proposed LGCA-VHPPI framework with existing diverse sequence encoding schemes based viral-host PPI predictors reveals that LGCA-VHPPI outperforms state-of-the-art predictor by 6%, 2%, and 2% in terms of matthews correlation coefficient over 3 different benchmark viral-host PPI prediction datasets.

Characterization of SARS-CoV-2 replication complex elongation and proofreading activity

The replication complex (RC) of SARS-CoV-2 was recently shown to be one of the fastest RNA-dependent RNA polymerases of any known coronavirus. With this rapid elongation, the RC is more prone to incorporate mismatches during elongation, resulting in a highly variable genomic sequence. Such mutations render the design of viral protein targets difficult, as drugs optimized for a given viral protein sequence can quickly become inefficient as the genomic sequence evolves. Here, we use biochemical experiments to characterize features of RNA template recognition and elongation fidelity of the SARS-CoV-2 RdRp, and the role of the exonuclease, nsp14. Our study highlights the 2′OH group of the RNA ribose as a critical component for RdRp template recognition and elongation. We show that RdRp fidelity is reduced in the presence of the 3′ deoxy-terminator nucleotide 3′dATP, which promotes the incorporation of mismatched nucleotides (leading to U:C, U:G, U:U, C:U, and A:C base pairs). We find that the nsp10–nsp14 heterodimer is unable to degrade RNA products lacking free 2′OH or 3′OH ribose groups. Our results suggest the potential use of 3′ deoxy-terminator nucleotides in RNA-derived oligonucleotide inhibitors as antivirals against SARS-CoV-2.

Immunoinformatics approach of epitope prediction for SARS-CoV-2

BackgroundThe novel coronavirus (SARS-CoV-2) caused lethal infections worldwide during an unprecedented pandemic. Identification of the candidate viral epitopes is the first step in the design of vaccines against the viral infection. Several immunoinformatic approaches were employed to identify the SARS-CoV-2 epitopes that bind specifically with the major histocompatibility molecules class I (MHC-I). We utilized immunoinformatic tools to analyze the whole viral protein sequences, to identify the SARS-CoV-2 epitopes responsible for binding to the most frequent human leukocyte antigen (HLA) alleles in the Egyptian population. These alleles were also found with high frequency in other populations worldwide. ResultsMolecular docking approach showed that using the co-crystallized MHC-I and T cell receptor (TCR) instead of using MHC-I structure only, significantly enhanced docking scores and stabilized the conformation, as well as the binding affinity of the identified SARS-CoV-2 epitopes. Our approach directly predicts 7 potential vaccine subunits from the available SARS-CoV-2 spike and ORF1ab protein sequence. This prediction has been confirmed by published experimentally validated and in silico predicted spike epitope. On the other hand, we predicted novel epitopes (RDLPQGFSA and FCLEASFNY) showing high docking scores and antigenicity response with both MHC-I and TCR. Moreover, antigenicity, allergenicity, toxicity, and physicochemical properties of the predicted SARS-CoV-2 epitopes were evaluated via state-of-the-art bioinformatic approaches, showing high efficacy of the proposed epitopes as a vaccine candidate. ConclusionOur predicted SARS-CoV-2 epitopes can facilitate vaccine development to enhance the immunogenicity against SARS-CoV-2 and provide supportive data for further experimental validation. Our proposed molecular docking approach of exploiting both MHC and TCR structures can be used to identify potential epitopes for most microbial pathogens, provided the crystal structure of MHC co-crystallized with TCR.

High-Resolution Structure of the Nuclease Domain of the Human Parvovirus B19 Main Replication Protein NS1.

Two new structures of the N-terminal domain of the main replication protein, NS1, of human parvovirus B19 (B19V) are presented here. This domain (NS1-nuc) plays an important role in the "rolling hairpin" replication of the single-stranded B19V DNA genome, recognizing origin of replication sequences in double-stranded DNA, and cleaving (i.e., nicking) single-stranded DNA at a nearby site known as the terminal resolution site (trs). The three-dimensional structure of NS1-nuc is well conserved between the two forms, as well as with a previously solved structure of a sequence variant of the same domain; however, it is shown here at a significantly higher resolution (2.4 Å). Using structures of NS1-nuc homologues bound to single- and double-stranded DNA, models for DNA recognition and nicking by B19V NS1-nuc are presented that predict residues important for DNA cleavage and for sequence-specific recognition at the viral origin of replication. IMPORTANCE The high-resolution structure of the DNA binding and cleavage domain of the main replicative protein, NS1, from the human-pathogenic virus human parvovirus B19 is presented here. Included also are predictions of how the protein recognizes important sequences in the viral DNA which are required for viral replication. These predictions can be used to further investigate the function of this protein, as well as to predict the effects on viral viability due to mutations in the viral protein and viral DNA sequences. Finally, the high-resolution structure facilitates structure-guided drug design efforts to develop antiviral compounds against this important human pathogen.

Potential SARS-CoV-2 nonstructural proteins inhibitors: drugs repurposing with drug-target networks and deep learning.

In the current COVID-19 pandemic, with an absence of approved drugs and widely accessible vaccines, repurposing existing drugs is vital to quickly developing a treatment for the disease. In this study, we used a dataset consisting of sequences of viral proteins and chemical structures of pharmaceutical drugs for known drug-target interactions (DTIs) and artificially generated non-interacting DTIs to train a binary classifier with the ability to predict new DTIs. Random Forest (RF), deep neural network (DNN), and convolutional neural networks (CNN) were tested. The CNN and RF models were selected for the classification task. The models generalized well to the given DTI data and were used to predict DTIs involving SARS-CoV-2 nonstructural proteins (NSPs). We elucidated (with the CNN) 29 drugs involved in 82 DTIs with a 97% probability of interaction, 44 DTIs of which had a 99% probability of interaction, to treat COVID-19. The RF elucidated 6 drugs involved in 17 DTIs with a 90% probability of interacting. These results give new insight into possible inhibitors of the viral proteins beyond pharmacophore models and molecular docking procedures used in recent studies.

Librator: a platform for the optimized analysis, design, and expression of mutable influenza viral antigens.

Artificial mutagenesis and protein engineering have laid the foundation for antigenic characterization and universal vaccine design for influenza viruses. However, many methods used in this process require manual sequence editing and protein expression, limiting their efficiency and utility in high-throughput applications. More streamlined in silico tools allowing researchers to properly analyze and visualize influenza viral protein sequences with accurate nomenclature are necessary to improve antigen design and productivity. To address this need, we developed Librator, a system for analyzing and designing custom protein sequences of influenza virus hemagglutinin (HA) and neuraminidase (NA) glycoproteins. Within Librator's graphical interface, users can easily interrogate viral sequences and phylogenies, visualize antigen structures and conservation, mutate target residues and design custom antigens. Librator also provides optimized fragment design for Gibson Assembly of HA and NA expression constructs based on peptide conservation of all historical HA and NA sequences, ensuring fragments are reusable and compatible across related subtypes, thereby promoting reagent savings. Finally, the program facilitates single-cell immune profiling, epitope mapping of monoclonal antibodies and mosaic protein design. Using Librator-based antigen construction, we demonstrate that antigenicity can be readily transferred between HA molecules of H3, but not H1, lineage viruses. Altogether, Librator is a valuable tool for analyzing influenza virus HA and NA proteins and provides an efficient resource for optimizing recombinant influenza antigen synthesis.

Evolutionary Relationship of Protein Sequences of SARS-CoV-2 and Other Viruses through Chaos Game Representation

Comparison between different biological sequences is a key step in bioinformatics when analyzing similarities of sequences and phylogenetic relationships. A method of graphically representing biological sequences known as Chaos Game Representation (CGR) has achieved many applications in the studies of bioinformatics. The key issue in the application of CGR is to extract as many useful features as possible from CGR. Initially, CGR was applied to DNA sequences, but in this paper, a CGR-based approach is used to extract suitable features for comparing protein sequences of SARS-CoV-2 and other viruses. For this aim, several viral protein sequences from 12 groups are considered and CGR centroid, amino acid frequency, compounded frequency, Shannon entropy, and Kullback-Lieber Discrimination Information are applied to find the inter-relationship among the sequences. The experimental results demonstrate the potential strengths of CGR-based method for examining the evolutionary relationship of protein sequences. Our method is powerful for extracting effective features from protein sequences, and therefore important in classifying proteins and inferring the phylogeny of viruses.

Immunoinformatics approach for designing a universal multiepitope vaccine against Chandipura Virus

Chandipura vesiculovirus (CHPV) is a fast-emerging virus that causes acute encephalitis with a high death rate. Because of its extensive prevalence in African and Asian countries, this infection has become a global hazard, and there is an urgent need to create an effective and non-allergenic vaccine or appropriate treatment to combat it. A vaccine candidate is offered utilizing a computational technique in this study. To build a potential vaccine candidate, viral protein sequences were acquired from the National Center for Biotechnology Information database and evaluated with several bioinformatics techniques to identify B-cell and T-cell epitopes. V1 was shown to be superior in terms of various physicochemical qualities, as well as highly immunogenic and non-allergic. Molecular docking revealed that the CHPV vaccine construct had a greater binding affinity with human Toll-like receptors (TLR-3 and TLR-8) and that it was stable in molecular dynamics simulations. MEC-CHPV was in silico cloned in the pET28a (+) expression vector using codon optimization. The current research identifies potential antigenic epitopes that could be used as vaccine candidates to eradicate the CHPV. This in-silico development of a CHPV vaccine with multiple epitopes could open the path for future rapid laboratory tests.

Virus-derived sequences from the transcriptomes of two snail vectors of schistosomiasis, Biomphalaria pfeifferi and Bulinus globosus from Kenya.

Schistosomiasis, which infects more than 230 million people, is vectored by freshwater snails. We identified viral sequences in the transcriptomes of Biomphalaria pfeifferi (BP) and Bulinus globosus (BuG), two of the world’s most important schistosomiasis vectors in Africa. Sequences from 26 snails generated using Illumina Hi-Seq or 454 sequencing were assembled using Trinity and CAP3 and putative virus sequences were identified using a bioinformatics pipeline. Phylogenetic analyses were performed using viral RNA-dependent RNA polymerase and coat protein sequences to establish relatedness between virus sequences identified and those of known viruses. Viral sequences were identified from the entire snail holobiont, including symbionts, ingested material and organisms passively associated with the snails. Sequences derived from more than 17 different viruses were found including five near full-length genomes, most of which were small RNA viruses with positive sense RNA genomes (i.e., picorna-like viruses) and some of which are likely derived from adherent or ingested diatoms. Based on phylogenetic analysis, five of these viruses (including BPV2 and BuGV2) along with four Biomphalaria glabrata viruses reported previously, cluster with known invertebrate viruses and are putative viruses of snails. The presence of RNA sequences derived from four of these novel viruses in samples was confirmed. Identification of the genome sequences of candidate snail viruses provides a first step toward characterization of additional gastropod viruses, including from species of biomedical significance.

Potential of application of the RNA interference phenomenon in the treatment of new coronavirus infection COVID-19.

COVID-19 has killed more than 4 million people to date and is the most significant global health problem. The first recorded case of COVID-19 had been noted in Wuhan, China in December 2019, and already on March 11, 2020, World Health Organization declared a pandemic due to the rapid spread of this infection. In addition to the damage to the respiratory system, SARS-CoV-2 is capable of causing severe complications that can affect almost all organ systems. Due to the insufficient effectiveness of the COVID-19 therapy, there is an urgent need to develop effective specific medicines. Among the known approaches to the creation of antiviral drugs, a very promising direction is the development of drugs whose action is mediated by the mechanism of RNA interference (RNAi). A small interfering RNA (siRNA) molecule suppresses the expression of a target gene in this regulatory pathway. The phenomenon of RNAi makes it possible to quickly create a whole series of highly effective antiviral drugs, if the matrix RNA (mRNA) sequence of the target viral protein is known. This review examines the possibility of clinical application of siRNAs aimed at suppressing reproduction of the SARS-CoV-2, taking into account the experience of similar studies using SARS-CoV and MERS-CoV infection models. It is important to remember that the effectiveness of siRNA molecules targeting viral genes may decrease due to the formation of viral resistance. In this regard, the design of siRNAs targeting the cellular factors necessary for the reproduction of SARS-CoV-2 deserves special attention.

Heterogeneous graph attention networks for drug virus association prediction.

Coronavirus Disease-19 (COVID-19) has lead global epidemics with high morbidity and mortality. However, there are currently no proven effective drugs targeting COVID-19. Identifying drug-virus associations can not only provide insights into the understanding of drug-virus interaction mechanism, but also guide and facilitate the screening of compound candidates for antiviral drug discovery. Since conventional experiment methods are time-consuming, laborious and expensive, computational methods to identify potential drug candidates for viruses (e.g., COVID-19) provide an alternative strategy. In this work, we propose a novel framework of Heterogeneous Graph Attention Networks for Drug-Virus Association predictions, named HGATDVA. First, we fully incorporate multiple sources of biomedical data, e.g., drug chemical information, virus genome sequences and viral protein sequences, to construct abundant features for drugs and viruses. Second, we construct two drug-virus heterogeneous graphs. For each graph, we design a self-enhanced graph attention network (SGAT) to explicitly model the dependency between a node and its local neighbors and derive the graph-specific representations for nodes. Third, we further develop a neural network architecture with tri-aggregator to aggregate the graph-specific representations to generate the final node representations. Extensive experiments were conducted on two datasets, i.e., DrugVirus and MDAD, and the results demonstrated that our model outperformed 7 state-of-the-art methods. Case study on SARS-CoV-2 validated the effectiveness of our model in identifying potential drugs for viruses.