Probing antiviral drugs against SARS-CoV-2 through virus-drug association prediction based on the KATZ method

It is urgent to find an effective antiviral drug against SARS-CoV-2. In this study, 96 virus-drug associations (VDAs) from 12 viruses including SARS-CoV-2 and similar viruses and 78 small molecules are selected. Complete genomic sequence similarity of viruses and chemical structure similarity of drugs are then computed. A KATZ-based VDA prediction method (VDA-KATZ) is developed to infer possible drugs associated with SARS-CoV-2. VDA-KATZ obtained the best AUCs of 0.8803 when the walking length is 2. The predicted top 3 antiviral drugs against SARS-CoV-2 are remdesivir, oseltamivir, and zanamivir. Molecular docking is conducted between the predicted top 10 drugs and the virus spike protein/human ACE2. The results showed that the above 3 chemical agents have higher molecular binding energies with ACE2. For the first time, we found that zidovudine may be effective clues of treatment of COVID-19. We hope that our predicted drugs could help to prevent the spreading of COVID.     

Immunoinformatic analysis of the SARS-CoV-2 envelope protein as a strategy to assess cross-protection against COVID-19

Envelope protein of coronaviruses is a structural protein existing in both monomeric and homo-pentameric form. It has been related to a multitude of roles including virus infection, replication, dissemination and immune response stimulation. In the present study, we employed an immunoinformatic approach to investigate the major immunogenic domains of the SARS-CoV-2 envelope protein and map them among the homologue proteins of coronaviruses with tropism for animal species that are closely inter-related with the human beings population all over the world. Also, when not available, we predicted the envelope protein structural folding and mapped SARS-CoV-2 epitopes. Envelope sequences alignment provides evidence of high sequence homology for some of the investigated virus specimens; while the structural mapping of epitopes resulted in the interesting maintenance of the structural folding and epitope sequence localization also in the envelope proteins scoring a lower alignment score. In line with the One-Health approach, our evidences provide a molecular structural rationale for a potential role of taxonomically related coronaviruses in conferring protection from SARS-CoV-2 infection and identifying potential candidates for the development of diagnostic tools and prophylactic-oriented strategies.     

ProViDE: A software tool for accurate estimation of viral diversity in metagenomic samples

Given the absence of universal marker genes in the viral kingdom, researchers typically use BLAST (with stringent E-values) for taxonomic classification of viral metagenomic sequences. Since majority of metagenomic sequences originate from hitherto unknown viral groups, using stringent e-values results in most sequences remaining unclassified. Furthermore, using less stringent e-values results in a high number of incorrect taxonomic assignments. The SOrt-ITEMS algorithm provides an approach to address the above issues. Based on alignment parameters, SOrt-ITEMS follows an elaborate work-flow for assigning reads originating from hitherto unknown archaeal/bacterial genomes. In SOrt-ITEMS, alignment parameter thresholds were generated by observing patterns of sequence divergence within and across various taxonomic groups belonging to bacterial and archaeal kingdoms. However, many taxonomic groups within the viral kingdom lack a typical Linnean-like taxonomic hierarchy. In this paper, we present ProViDE (Program for Viral Diversity Estimation), an algorithm that uses a customized set of alignment parameter thresholds, specifically suited for viral metagenomic sequences. These thresholds capture the pattern of sequence divergence and the non-uniform taxonomic hierarchy observed within/across various taxonomic groups of the viral kingdom. Validation results indicate that the percentage of 'correct' assignments by ProViDE is around 1.7 to 3 times higher than that by the widely used similarity based method MEGAN. The misclassification rate of ProViDE is around 3 to 19% (as compared to 5 to 42% by MEGAN) indicating significantly better assignment accuracy. ProViDE software and a supplementary file (containing supplementary figures and tables referred to in this article) is available for download from http://metagenomics.atc.tcs.com/binning/ProViDE/     

AGenDA: homology-based gene prediction

We present a www server for homology-based gene prediction. The user enters a pair of evolutionary related genomic sequences, for example from human and mouse. Our software system uses CHAOS and DIALIGN to calculate an alignment of the input sequences and then searches for conserved splicing signals and start/stop codons around regions of local sequence similarity. This way, candidate exons are identified that are used, in turn, to calculate optimal gene models. The server returns the constructed gene model by email, together with a graphical representation of the underlying genomic alignment.     

Application of discrete Fourier inter-coefficient difference for assessing genetic sequence similarity

Digital signal processing (DSP) techniques for biological sequence analysis continue to grow in popularity due to the inherent digital nature of these sequences. DSP methods have demonstrated early success for detection of coding regions in a gene. Recently, these methods are being used to establish DNA gene similarity. We present the inter-coefficient difference (ICD) transformation, a novel extension of the discrete Fourier transformation, which can be applied to any DNA sequence. The ICD method is a mathematical, alignment-free DNA comparison method that generates a genetic signature for any DNA sequence that is used to generate relative measures of similarity among DNA sequences. We demonstrate our method on a set of insulin genes obtained from an evolutionarily wide range of species, and on a set of avian influenza viral sequences, which represents a set of highly similar sequences. We compare phylogenetic trees generated using our technique against trees generated using traditional alignment techniques for similarity and demonstrate that the ICD method produces a highly accurate tree without requiring an alignment prior to establishing sequence similarity.     

PCR assay for discriminating between infectious hypodermal and hematopoietic necrosis virus (IHHNV) and virus-related sequences in the genome of Penaeus monodon

We developed a PCR assay that can detect infectious hypodermal and hematopoietic necrosis virus (IHHNV) but that does not react with IHHNV-related sequences in the genome of Penaeus monodon from Africa and Australia. IHHNV is a single-stranded DNA virus that has caused severe mortality and stunted growth in penaeid shrimp. Recently, IHHNV-related sequences were found in the genome of some stocks of P. monodon from Africa and Australia. These virus-related sequences have a high degree of similarity (86 and 92% identities in nucleotide sequence) to the viral genome, which has often generated false-positive reactions during PCR screening of these stocks. For this assay, a pair of IHHNV primers (IHHNV309F/R) was selected. The sequences of these primers match (100% of nucleotides) the target sequence in IHHNV, but mismatch 9 or 12 nucleotides of the genomic IHHNV-related sequences. This PCR assay was tested with various IHHNV isolates and with a number of samples of shrimp DNA that contained IHHNV-related sequences. This assay can reliably distinguish IHHNV DNA from shrimp DNA: it only detects IHHNV. Also, this pair of primers was included in a duplex PCR to detect IHHNV and simultaneously determine the presence of an IHHNV-related sequence. Using these primers, the PCR assay has a sensitivity equivalent to a PCR assay commonly used for detecting IHHNV in Litopenaeus vannamei, and can be used for routine detection.     

IsoSVM – Distinguishing isoforms and paralogs on the protein level

Background  

Recent progress in cDNA and EST sequencing is yielding a deluge of sequence data. Like database search results and proteome databases, this data gives rise to inferred protein sequences without ready access to the underlying genomic data. Analysis of this information (e.g. for EST clustering or phylogenetic reconstruction from proteome data) is hampered because it is not known if two protein sequences are isoforms (splice variants) or not (i.e. paralogs/orthologs). However, even without knowing the intron/exon structure, visual analysis of the pattern of similarity across the alignment of the two protein sequences is usually helpful since paralogs and orthologs feature substitutions with respect to each other, as opposed to isoforms, which do not.     

全球抗病毒药物研发进展与展望

病毒是危害人体健康的主要病原体之一,病毒感染和传播造成的传染性疾病严重威胁人类健康。目前,艾滋病、病毒性肝炎等发病率高、治愈率低的病毒性疾病仍在全球蔓延,流感病毒、冠状病毒等呼吸道病毒不断发生变异,2019年以来,新冠病毒引起的全球疫情对世界各国产生巨大影响,疫情走向还存在很大不确定性,开发安全有效的抗病毒药物成为应对病毒性疾病的重要手段。拟在总结全球抗病毒药物研发整体现状的基础上,分析抗艾滋病病毒、肝炎病毒、新冠病毒等重点领域的新药研发进展,提出抗病毒药物的发展建议,为未来研发更加高效的抗病毒药物提供指引和参考。     

A Method Enabling High-Throughput Sequencing of Human Cytomegalovirus Complete Genomes from Clinical Isolates

Human cytomegalovirus (HCMV) is a ubiquitous virus that can cause serious sequelae in immunocompromised patients and in the developing fetus. The coding capacity of the 235 kbp genome is still incompletely understood, and there is a pressing need to characterize genomic contents in clinical isolates. In this study, a procedure for the high-throughput generation of full genome consensus sequences from clinical HCMV isolates is presented. This method relies on low number passaging of clinical isolates on human fibroblasts, followed by digestion of cellular DNA and purification of viral DNA. After multiple displacement amplification, highly pure viral DNA is generated. These extracts are suitable for high-throughput next-generation sequencing and assembly of consensus sequences. Throughout a series of validation experiments, we showed that the workflow reproducibly generated consensus sequences representative for the virus population present in the original clinical material. Additionally, the performance of 454 GS FLX and/or Illumina Genome Analyzer datasets in consensus sequence deduction was evaluated. Based on assembly performance data, the Illumina Genome Analyzer was the platform of choice in the presented workflow. Analysis of the consensus sequences derived in this study confirmed the presence of gene-disrupting mutations in clinical HCMV isolates independent from in vitro passaging. These mutations were identified in genes RL5A, UL1, UL9, UL111A and UL150. In conclusion, the presented workflow provides opportunities for high-throughput characterization of complete HCMV genomes that could deliver new insights into HCMV coding capacity and genetic determinants of viral tropism and pathogenicity.     

Characterization of altered genomic landscape of SARS-CoV-2 variants isolated in Saudi Arabia in a comparative in silico study

SARS-CoV-2 has become one of the unprecedented global health challenge for human population. Genomic signature studies of SARS-CoV-2 reveals relation between geographical location of the isolates and genetic diversity. The present work is an in silico, cross sectional study aimed to determine the genetic heterogeneity of SARS-CoV-2 variants isolated in Saudi Arabia compared to the first isolated strain NC_045512 (reference sequence). Each sequence was aligned against the reference sequence using local alignment search tool (NCBI) Nucleotide-BLAST. A total of 58 SARS-CoV-2 genomes were isolated in KSA and retrieved from NCBI. Our study shows that KSA variants demonstrated homology ranging between 99.96 and 99.98 % compared to the reference strain. There are 89 nucleotide changes that have been identified among the KSA variants; the most common nucleotide change was C: T accounting for 50.6% (45/89). These nucleotides changes resulted in 53.9% (48/89) missense mutations and 42.7% (38/89) silent mutations; while the majority of mutations- 48.3% (43/89) occurred in ORF1ab gene. All structural genes displayed mutations; N gene harbored 16.9% (15/89) mutations, S gene displayed 15.7% (14/89) mutations, M gene exhibited 2.2% (2/89) mutations and E gene showed only 1 mutation which was silent. The most frequently changed nucleotide was C3037T (silent mutation) and A23403G (D614G), each of which occurred in 57 variants out of 58 followed by C14408T (P4715L) and C241T (5′UTR) which were found in 56 and 55 variants respectively. The Phylogenetic trees showed that SARS-CoV-2 variants isolated in Saudi Arabia clustered together closely.     

The Global Landscape of SARS-CoV-2 Genomes,Variants, and Haplotypes in 2019nCoVR

On January 22, 2020, China National Center for Bioinformation (CNCB) released the 2019 Novel Coronavirus Resource (2019nCoVR), an open-access information resource for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). 2019nCoVR features a comprehensive integration of sequence and clinical information for all publicly available SARS-CoV-2 isolates, which are manually curated with value-added annotations and quality evaluated by an automated in-house pipeline. Of particular note, 2019nCoVR offers systematic analyses to generate a dynamic landscape of SARS-CoV-2 genomic variations at a global scale. It provides all identified variants and their detailed statistics for each virus isolate, and congregates the quality score, functional annotation, and population frequency for each variant. Spatiotemporal change for each variant can be visualized and historical viral haplotype network maps for the course of the outbreak are also generated based on all complete and high-quality genomes available. Moreover, 2019nCoVR provides a full collection of SARS-CoV-2 relevant literature on the coronavirus disease 2019 (COVID-19), including published papers from PubMed as well as preprints from services such as bioRxiv and medRxiv through Europe PMC. Furthermore, by linking with relevant databases in CNCB, 2019nCoVR offers data submission services for raw sequence reads and assembled genomes, and data sharing with NCBI. Collectively, SARS-CoV-2 is updated daily to collect the latest information on genome sequences, variants, haplotypes, and literature for a timely reflection, making 2019nCoVR a valuable resource for the global research community. 2019nCoVR is accessible at https://bigd.big.ac.cn/ncov/.     

Molecular adaptive evolution of SARS-COV-2 spike protein in Saudi Arabia

The sequences of SARS-CoV-2 spike (S) from Saudi Arabia along with SARS-CoV and bat SARS-like CoVs were obtained. Positive selection analysis and secondary structure investigation of spike sequences were performed. Adaptive molecular evolution was observed in SARS-CoV-2 displayed by positive selection pressure at N-terminal domain (NTD; codons 41, 163, 174 and 218), Receptor binding domain (RBD; codons 378 and 404) and S1/S2 Cleavage site (codon 690). Furthermore, the spike protein secondary structure depicted by the homo-trimer structure showed a high similarity between Saudi SARS-CoV-2 isolate and the parental strain (bat SL-COVZC45). Despite the high similarity depicted in the spike sequence model alignment, it displayed a significant difference when each chain was treated solely owing to 7 motif differences in the three composing chains. In addition, SARS-CoV-2 S trimer model uncovered the presence of N-acetyl glucosamine ligands. Eventually, 3C-like proteinase cleavage site was observed in S2 domain could be used as a site for drug discovery. Genetics and molecular evolutionary facts are useful for assessment of evolution, host adaptation and epidemic patterns ultimately helpful for adaptation of control strategies.     

Designing allosteric modulators for active conformational state of m-glutamate G-protein coupled receptors

G-protein coupled receptors (GPCRs) are found to be attractive drug targets for the treatment of various neuronal diseases. Allosteric modulators have their role in enhancing or suppressing the effect of glutamate on mGluRs. Structure of mGluR1 was generated with the help of Modeller software by considering human B2-adrenergic GPCR protein as template. Structure of various already known drug molecules were used for similarity search in the ZINC database and a large number of similar molecules were obtained, than filtering of these molecules were done by applying drug features. Molecules were screened by Molegro Virtual Docking program and numbers of novel molecules were generated by using LigBuilder software. Finally 16 novel drug candidates were selected, which were showing better results than the seed molecule and previously known modulators. These results will help in designing and synthesis of better drugs against diseases like Epilepsy and Parkinson's.     

Discovery of re-purposed drugs that slow SARS-CoV-2 replication in human cells

COVID-19 vaccines based on the Spike protein of SARS-CoV-2 have been developed that appear to be largely successful in stopping infection. However, therapeutics that can help manage the disease are still required until immunity has been achieved globally. The identification of repurposed drugs that stop SARS-CoV-2 replication could have enormous utility in stemming the disease. Here, using a nano-luciferase tagged version of the virus (SARS-CoV-2-ΔOrf7a-NLuc) to quantitate viral load, we evaluated a range of human cell types for their ability to be infected and support replication of the virus, and performed a screen of 1971 FDA-approved drugs. Hepatocytes, kidney glomerulus, and proximal tubule cells were particularly effective in supporting SARS-CoV-2 replication, which is in-line with reported proteinuria and liver damage in patients with COVID-19. Using the nano-luciferase as a measure of virus replication we identified 35 drugs that reduced replication in Vero cells and human hepatocytes when treated prior to SARS-CoV-2 infection and found amodiaquine, atovaquone, bedaquiline, ebastine, LY2835219, manidipine, panobinostat, and vitamin D3 to be effective in slowing SARS-CoV-2 replication in human cells when used to treat infected cells. In conclusion, our study has identified strong candidates for drug repurposing, which could prove powerful additions to the treatment of COVID.     

Identification of natural compounds as potent inhibitors of SARS-CoV-2 main protease using combined docking and molecular dynamics simulations

Coronavirus disease 2019 (COVID-19) has emerged from China and globally affected the entire population through the human-to-human transmission of a newly emerged virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The genome of SARS-CoV-2 encodes several proteins that are essential for multiplication and pathogenesis. The main protease (M^pro or 3CL^pro) of SARS-CoV-2 plays a central role in its pathogenesis and thus is considered as an attractive drug target for the drug design and development of small-molecule inhibitors. We have employed an extensive structure-based high-throughput virtual screening to discover potential natural compounds from the ZINC database which could inhibit the M^pro of SARS-CoV-2. Initially, the hits were selected on the basis of their physicochemical and drug-like properties. Subsequently, the PAINS filter, estimation of binding affinities using molecular docking, and interaction analyses were performed to find safe and potential inhibitors of SARS-CoV-2 M^pro. We have identified ZINC02123811 (1-(3-(2,5,9-trimethyl-7-oxo-3-phenyl-7H-furo[3,2-g]chromen-6-yl)propanoyl)piperidine-4-carboxamide), a natural compound bearing appreciable affinity, efficiency, and specificity towards the binding pocket of SARS-CoV-2 M^pro. The identified compound showed a set of drug-like properties and preferentially binds to the active site of SARS-CoV-2 M^pro. All-atom molecular dynamics (MD) simulations were performed to evaluate the conformational dynamics, stability and interaction mechanism of M^pro with ZINC02123811. MD simulation results indicated that M^pro with ZINC02123811 forms a stable complex throughout the trajectory of 100 ns. These findings suggest that ZINC02123811 may be further exploited as a promising scaffold for the development of potential inhibitors of SARS-CoV-2 M^pro to address COVID-19.     

Comparative genomics of foot-and-mouth disease virus

Here we present complete genome sequences, including a comparative analysis, of 103 isolates of foot-and-mouth disease virus (FMDV) representing all seven serotypes and including the first complete sequences of the SAT1 and SAT3 genomes. The data reveal novel highly conserved genomic regions, indicating functional constraints for variability as well as novel viral genomic motifs with likely biological relevance. Previously undescribed invariant motifs were identified in the 5' and 3' untranslated regions (UTR), as was tolerance for insertions/deletions in the 5' UTR. Fifty-eight percent of the amino acids encoded by FMDV isolates are invariant, suggesting that these residues are critical for virus biology. Novel, conserved sequence motifs with likely functional significance were identified within proteins L(pro), 1B, 1D, and 3C. An analysis of the complete FMDV genomes indicated phylogenetic incongruities between different genomic regions which were suggestive of interserotypic recombination. Additionally, a novel SAT virus lineage containing nonstructural protein-encoding regions distinct from other SAT and Euroasiatic lineages was identified. Insights into viral RNA sequence conservation and variability and genetic diversity in nature will likely impact our understanding of FMDV infections, host range, and transmission.