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1.
Molecules ; 27(4)2022 Feb 21.
Article in English | MEDLINE | ID: covidwho-1715568

ABSTRACT

Baicalin is a major active ingredient of traditional Chinese medicine Scutellaria baicalensis, and has been shown to have antiviral, anti-inflammatory, and antitumor activities. However, the protein targets of baicalin have remained unclear. Herein, a chemical proteomics strategy was developed by combining baicalin-functionalized magnetic nanoparticles (BCL-N3@MNPs) and quantitative mass spectrometry to identify the target proteins of baicalin. Bioinformatics analysis with the use of Gene Ontology, STRING and Ingenuity Pathway Analysis, was performed to annotate the biological functions and the associated signaling pathways of the baicalin targeting proteins. Fourteen proteins in human embryonic kidney cells were identified to interact with baicalin with various binding affinities. Bioinformatics analysis revealed these proteins are mainly ATP-binding and/or ATPase activity proteins, such as CKB, HSP86, HSP70-1, HSP90, ATPSF1ß and ACTG1, and highly associated with the regulation of the role of PKR in interferon induction and the antiviral response signaling pathway (P = 10-6), PI3K/AKT signaling pathway (P = 10-5) and eNOS signaling pathway (P = 10-4). The results show that baicalin exerts multiply pharmacological functions, such as antiviral, anti-inflammatory, antitumor, and antioxidant functions, through regulating the PKR and PI3K/AKT/eNOS signaling pathways by targeting ATP-binding and ATPase activity proteins. These findings provide a fundamental insight into further studies on the mechanism of action of baicalin.


Subject(s)
Flavonoids/pharmacology , HSP70 Heat-Shock Proteins/antagonists & inhibitors , HSP90 Heat-Shock Proteins/antagonists & inhibitors , Nitric Oxide Synthase Type III/metabolism , Phosphatidylinositol 3-Kinases/metabolism , Proto-Oncogene Proteins c-akt/metabolism , Signal Transduction/drug effects , Animals , Dose-Response Relationship, Drug , Flavonoids/administration & dosage , Flavonoids/chemistry , Humans , Magnetite Nanoparticles/chemistry , Magnetite Nanoparticles/ultrastructure , Protein Interaction Mapping
2.
Cell Mol Life Sci ; 79(2): 75, 2022 Jan 17.
Article in English | MEDLINE | ID: covidwho-1630170

ABSTRACT

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a new member of the Betacoronaviridae family, responsible for the recent pandemic outbreak of COVID-19. To start exploring the molecular events that follow host cell infection, we queried VirusCircBase and identified a circular RNA (circRNA) predicted to be synthesized by SARS-CoV-2, circ_3205, which we used to probe: (i) a training cohort comprised of two pools of cells from three nasopharyngeal swabs of SARS-CoV-2 infected (positive) or uninfected (negative, UCs) individuals; (ii) a validation cohort made up of 12 positive and 3 negative samples. The expression of circRNAs, miRNAs and miRNA targets was assayed through real-time PCR. CircRNA-miRNA interactions were predicted by TarpMiR, Analysis of Common Targets for circular RNAs (ACT), and STarMir tools. Enrichment of the biological processes and the list of predicted miRNA targets were retrieved from DIANA miRPath v3.0. Our results showed that the predicted SARS-CoV-2 circ_3205 was expressed only in positive samples and its amount positively correlated with that of SARS-CoV-2 Spike (S) mRNA and the viral load (r values = 0.80952 and 0.84867, Spearman's correlation test, respectively). Human (hsa) miR-298 was predicted to interact with circ_3205 by all three predictive tools. KCNMB4 and PRKCE were predicted as hsa-miR-298 targets. Interestingly, the function of both is correlated with blood coagulation and immune response. KCNMB4 and PRKCE mRNAs were upregulated in positive samples as compared to UCs (6 and 8.1-fold, p values = 0.049 and 0.02, Student's t test, respectively) and their expression positively correlated with that of circ_3205 (r values = 0.6 and 0.25, Spearman's correlation test, respectively). We propose that our results convincingly suggest that circ_3205 is a circRNA synthesized by SARS-CoV-2 upon host cell infection and that it may behave as a competitive endogenous RNA (ceRNA), sponging hsa-miR-298 and contributing to the upregulation of KCNMB4 and PRKCE mRNAs.


Subject(s)
COVID-19/genetics , COVID-19/metabolism , RNA, Circular/genetics , RNA, Viral , SARS-CoV-2/genetics , Computational Biology , Gene Expression Regulation, Viral , Gene Regulatory Networks , Humans , Large-Conductance Calcium-Activated Potassium Channel beta Subunits/genetics , MicroRNAs/genetics , MicroRNAs/metabolism , Nasopharynx/virology , Nerve Tissue Proteins/genetics , Protein Interaction Mapping , Protein Kinase C-epsilon/genetics , Reproducibility of Results
3.
Bioengineered ; 13(2): 2486-2497, 2022 02.
Article in English | MEDLINE | ID: covidwho-1625949

ABSTRACT

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) can target cardiomyocytes (CMs) to directly invade the heart resulting in high mortality. This study aims to explore the biological characteristics of SARS-CoV-2 infected myocardium based on omics by collecting transcriptome data and analyzing them with a series of bioinformatics tools. Totally, 86 differentially expressed genes (DEGs) were discovered in SARS-CoV-2 infected CMs, and 15 miRNAs were discovered to target 60 genes. Functional enrichment analysis indicated that these DEGs were mainly enriched in the inflammatory signaling pathway. After the protein-protein interaction (PPI) network was constructed, several genes including CCL2 and CXCL8 were regarded as the hub genes. SRC inhibitor saracatinib was predicted to potentially act against the cardiac dysfunction induced by SARS-CoV-2. Among the 86 DEGs, 28 were validated to be dysregulated in SARS-CoV-2 infected hearts. Gene Set Enrichment Analysis (GSEA) analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) showed that malaria, IL-17 signaling pathway, and complement and coagulation cascades were significantly enriched. Immune infiltration analysis indicated that 'naive B cells' was significantly increased in the SARS-CoV-2 infected heart. The above results may help to improve the prognosis of patients with COVID-19.


Subject(s)
COVID-19/immunology , COVID-19/virology , Heart/physiopathology , Heart/virology , Myocardium/pathology , SARS-CoV-2 , Blood Coagulation , Chemokine CCL2/biosynthesis , Complement System Proteins , Computational Biology , Gene Expression Profiling , Gene Expression Regulation, Viral , Genome, Human , Humans , Inflammation , Interleukin-17/blood , Interleukin-8/biosynthesis , MicroRNAs/metabolism , Prognosis , Protein Interaction Mapping , Signal Transduction
4.
J Immunol ; 208(3): 753-761, 2022 02 01.
Article in English | MEDLINE | ID: covidwho-1614089

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has seriously threatened global public health. Severe COVID-19 has been reported to be associated with an impaired IFN response. However, the mechanisms of how SARS-CoV-2 antagonizes the host IFN response are poorly understood. In this study, we report that SARS-CoV-2 helicase NSP13 inhibits type I IFN production by directly targeting TANK-binding kinase 1 (TBK1) for degradation. Interestingly, inhibition of autophagy by genetic knockout of Beclin1 or pharmacological inhibition can rescue NSP13-mediated TBK1 degradation in HEK-293T cells. Subsequent studies revealed that NSP13 recruits TBK1 to p62, and the absence of p62 can also inhibit TBK1 degradation in HEK-293T and HeLa cells. Finally, TBK1 and p62 degradation and p62 aggregation were observed during SARS-CoV-2 infection in HeLa-ACE2 and Calu3 cells. Overall, our study shows that NSP13 inhibits type I IFN production by recruiting TBK1 to p62 for autophagic degradation, enabling it to evade the host innate immune response, which provides new insights into the transmission and pathogenesis of SARS-CoV-2 infection.


Subject(s)
Autophagy , COVID-19/immunology , Interferon Type I/biosynthesis , Methyltransferases/physiology , RNA Helicases/physiology , SARS-CoV-2/physiology , Sequestosome-1 Protein/metabolism , Viral Nonstructural Proteins/physiology , Beclin-1/antagonists & inhibitors , Cell Line , Down-Regulation , Humans , Immune Evasion , Immunity, Innate , Immunoprecipitation , Interferon Type I/genetics , Multiprotein Complexes , Protein Aggregates , Protein Interaction Mapping
5.
PLoS One ; 16(12): e0262056, 2021.
Article in English | MEDLINE | ID: covidwho-1596737

ABSTRACT

Characterization of protein complexes, i.e. sets of proteins assembling into a single larger physical entity, is important, as such assemblies play many essential roles in cells such as gene regulation. From networks of protein-protein interactions, potential protein complexes can be identified computationally through the application of community detection methods, which flag groups of entities interacting with each other in certain patterns. Most community detection algorithms tend to be unsupervised and assume that communities are dense network subgraphs, which is not always true, as protein complexes can exhibit diverse network topologies. The few existing supervised machine learning methods are serial and can potentially be improved in terms of accuracy and scalability by using better-suited machine learning models and parallel algorithms. Here, we present Super.Complex, a distributed, supervised AutoML-based pipeline for overlapping community detection in weighted networks. We also propose three new evaluation measures for the outstanding issue of comparing sets of learned and known communities satisfactorily. Super.Complex learns a community fitness function from known communities using an AutoML method and applies this fitness function to detect new communities. A heuristic local search algorithm finds maximally scoring communities, and a parallel implementation can be run on a computer cluster for scaling to large networks. On a yeast protein-interaction network, Super.Complex outperforms 6 other supervised and 4 unsupervised methods. Application of Super.Complex to a human protein-interaction network with ~8k nodes and ~60k edges yields 1,028 protein complexes, with 234 complexes linked to SARS-CoV-2, the COVID-19 virus, with 111 uncharacterized proteins present in 103 learned complexes. Super.Complex is generalizable with the ability to improve results by incorporating domain-specific features. Learned community characteristics can also be transferred from existing applications to detect communities in a new application with no known communities. Code and interactive visualizations of learned human protein complexes are freely available at: https://sites.google.com/view/supercomplex/super-complex-v3-0.


Subject(s)
Computational Biology/methods , Protein Interaction Maps , Proteins/immunology , Supervised Machine Learning , Viral Proteins/immunology , COVID-19/immunology , Humans , Protein Binding , Protein Interaction Mapping , SARS-CoV-2/immunology
6.
Nucleic Acids Res ; 50(D1): D858-D866, 2022 01 07.
Article in English | MEDLINE | ID: covidwho-1511005

ABSTRACT

SCoV2-MD (www.scov2-md.org) is a new online resource that systematically organizes atomistic simulations of the SARS-CoV-2 proteome. The database includes simulations produced by leading groups using molecular dynamics (MD) methods to investigate the structure-dynamics-function relationships of viral proteins. SCoV2-MD cross-references the molecular data with the pandemic evolution by tracking all available variants sequenced during the pandemic and deposited in the GISAID resource. SCoV2-MD enables the interactive analysis of the deposited trajectories through a web interface, which enables users to search by viral protein, isolate, phylogenetic attributes, or specific point mutation. Each mutation can then be analyzed interactively combining static (e.g. a variety of amino acid substitution penalties) and dynamic (time-dependent data derived from the dynamics of the local geometry) scores. Dynamic scores can be computed on the basis of nine non-covalent interaction types, including steric properties, solvent accessibility, hydrogen bonding, and other types of chemical interactions. Where available, experimental data such as antibody escape and change in binding affinities from deep mutational scanning experiments are also made available. All metrics can be combined to build predefined or custom scores to interrogate the impact of evolving variants on protein structure and function.


Subject(s)
COVID-19/virology , Databases, Genetic , Molecular Dynamics Simulation , SARS-CoV-2/genetics , Software , Viral Proteins/genetics , Evolution, Molecular , Gene Expression Regulation, Viral , Genome, Viral , Humans , Hydrogen Bonding , Internet , Models, Molecular , Phylogeny , Point Mutation , Protein Binding , Protein Interaction Mapping , SARS-CoV-2/growth & development , SARS-CoV-2/metabolism , SARS-CoV-2/pathogenicity , Structure-Activity Relationship , Viral Proteins/chemistry , Viral Proteins/metabolism
7.
Nucleic Acids Res ; 50(D1): D632-D639, 2022 01 07.
Article in English | MEDLINE | ID: covidwho-1506219

ABSTRACT

Network medicine has proven useful for dissecting genetic organization of complex human diseases. We have previously published HumanNet, an integrated network of human genes for disease studies. Since the release of the last version of HumanNet, many large-scale protein-protein interaction datasets have accumulated in public depositories. Additionally, the numbers of research papers and functional annotations for gene-phenotype associations have increased significantly. Therefore, updating HumanNet is a timely task for further improvement of network-based research into diseases. Here, we present HumanNet v3 (https://www.inetbio.org/humannet/, covering 99.8% of human protein coding genes) constructed by means of the expanded data with improved network inference algorithms. HumanNet v3 supports a three-tier model: HumanNet-PI (a protein-protein physical interaction network), HumanNet-FN (a functional gene network), and HumanNet-XC (a functional network extended by co-citation). Users can select a suitable tier of HumanNet for their study purpose. We showed that on disease gene predictions, HumanNet v3 outperforms both the previous HumanNet version and other integrated human gene networks. Furthermore, we demonstrated that HumanNet provides a feasible approach for selecting host genes likely to be associated with COVID-19.


Subject(s)
Algorithms , COVID-19/genetics , Communicable Diseases/genetics , Databases, Genetic , Gene Regulatory Networks , Software , COVID-19/virology , Communicable Diseases/classification , Gene Ontology , Humans , Internet , Molecular Sequence Annotation , Protein Interaction Mapping , SARS-CoV-2/pathogenicity
9.
J Nat Prod ; 84(11): 3001-3007, 2021 11 26.
Article in English | MEDLINE | ID: covidwho-1483081

ABSTRACT

The pressing need for SARS-CoV-2 controls has led to a reassessment of strategies to identify and develop natural product inhibitors of zoonotic, highly virulent, and rapidly emerging viruses. This review article addresses how contemporary approaches involving computational chemistry, natural product (NP) and protein databases, and mass spectrometry (MS) derived target-ligand interaction analysis can be utilized to expedite the interrogation of NP structures while minimizing the time and expense of extraction, purification, and screening in BioSafety Laboratories (BSL)3 laboratories. The unparalleled structural diversity and complexity of NPs is an extraordinary resource for the discovery and development of broad-spectrum inhibitors of viral genera, including Betacoronavirus, which contains MERS, SARS, SARS-CoV-2, and the common cold. There are two key technological advances that have created unique opportunities for the identification of NP prototypes with greater efficiency: (1) the application of structural databases for NPs and target proteins and (2) the application of modern MS techniques to assess protein-ligand interactions directly from NP extracts. These approaches, developed over years, now allow for the identification and isolation of unique antiviral ligands without the immediate need for BSL3 facilities. Overall, the goal is to improve the success rate of NP-based screening by focusing resources on source materials with a higher likelihood of success, while simultaneously providing opportunities for the discovery of novel ligands to selectively target proteins involved in viral infection.


Subject(s)
Antiviral Agents/pharmacology , Betacoronavirus/drug effects , Biological Products/pharmacology , Drug Discovery , Computational Biology , Databases, Chemical , Databases, Protein , Ligands , Mass Spectrometry , Protein Interaction Mapping , SARS-CoV-2/drug effects
10.
Sci Rep ; 11(1): 20687, 2021 10 19.
Article in English | MEDLINE | ID: covidwho-1475486

ABSTRACT

This analysis presents a systematic evaluation of the extent of therapeutic opportunities that can be obtained from drug repurposing by connecting drug targets with disease genes. When using FDA-approved indications as a reference level we found that drug repurposing can offer an average of an 11-fold increase in disease coverage, with the maximum number of diseases covered per drug being increased from 134 to 167 after extending the drug targets with their high confidence first neighbors. Additionally, by network analysis to connect drugs to disease modules we found that drugs on average target 4 disease modules, yet the similarity between disease modules targeted by the same drug is generally low and the maximum number of disease modules targeted per drug increases from 158 to 229 when drug targets are neighbor-extended. Moreover, our results highlight that drug repurposing is more dependent on target proteins being shared between diseases than on polypharmacological properties of drugs. We apply our drug repurposing and network module analysis to COVID-19 and show that Fostamatinib is the drug with the highest module coverage.


Subject(s)
COVID-19/drug therapy , Drug Repositioning/methods , Gene Regulatory Networks/drug effects , Protein Interaction Maps/genetics , SARS-CoV-2 , Antiviral Agents/pharmacology , Bayes Theorem , Computational Biology/methods , Drug Delivery Systems , Drug Discovery , Humans , Polypharmacology , Protein Interaction Mapping , United States , United States Food and Drug Administration
11.
Mol Syst Biol ; 17(10): e10387, 2021 10.
Article in English | MEDLINE | ID: covidwho-1478718

ABSTRACT

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.


Subject(s)
COVID-19/immunology , Computational Biology/methods , Databases, Factual , SARS-CoV-2/immunology , Software , Antiviral Agents/therapeutic use , COVID-19/drug therapy , COVID-19/genetics , COVID-19/virology , Computer Graphics , Cytokines/genetics , Cytokines/immunology , Data Mining/statistics & numerical data , Gene Expression Regulation , Host Microbial Interactions/genetics , Host Microbial Interactions/immunology , Humans , Immunity, Cellular/drug effects , Immunity, Humoral/drug effects , Immunity, Innate/drug effects , Lymphocytes/drug effects , Lymphocytes/immunology , Lymphocytes/virology , Metabolic Networks and Pathways/genetics , Metabolic Networks and Pathways/immunology , Myeloid Cells/drug effects , Myeloid Cells/immunology , Myeloid Cells/virology , Protein Interaction Mapping , SARS-CoV-2/drug effects , SARS-CoV-2/genetics , SARS-CoV-2/pathogenicity , Signal Transduction , Transcription Factors/genetics , Transcription Factors/immunology , Viral Proteins/genetics , Viral Proteins/immunology
12.
Comput Math Methods Med ; 2021: 2203636, 2021.
Article in English | MEDLINE | ID: covidwho-1443668

ABSTRACT

Coronavirus disease 2019 (COVID-19) arising from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a global pandemic since its first report in December 2019. So far, SARS-CoV-2 nucleic acid detection has been deemed as the golden standard of COVID-19 diagnosis. However, this detection method often leads to false negatives, thus triggering missed COVID-19 diagnosis. Therefore, it is urgent to find new biomarkers to increase the accuracy of COVID-19 diagnosis. To explore new biomarkers of COVID-19 in this study, expression profiles were firstly accessed from the GEO database. On this basis, 500 feature genes were screened by the minimum-redundancy maximum-relevancy (mRMR) feature selection method. Afterwards, the incremental feature selection (IFS) method was used to choose a classifier with the best performance from different feature gene-based support vector machine (SVM) classifiers. The corresponding 66 feature genes were set as the optimal feature genes. Lastly, the optimal feature genes were subjected to GO functional enrichment analysis, principal component analysis (PCA), and protein-protein interaction (PPI) network analysis. All in all, it was posited that the 66 feature genes could effectively classify positive and negative COVID-19 and work as new biomarkers of the disease.


Subject(s)
Biomarkers/metabolism , COVID-19/genetics , COVID-19/metabolism , Algorithms , COVID-19 Testing , Computational Biology , False Negative Reactions , False Positive Reactions , Gene Expression Profiling , Humans , Machine Learning , Models, Statistical , Principal Component Analysis , Protein Interaction Mapping , Research Design , Sensitivity and Specificity
13.
Mol Syst Biol ; 17(9): e10079, 2021 09.
Article in English | MEDLINE | ID: covidwho-1406892

ABSTRACT

We modeled 3D structures of all SARS-CoV-2 proteins, generating 2,060 models that span 69% of the viral proteome and provide details not available elsewhere. We found that ˜6% of the proteome mimicked human proteins, while ˜7% was implicated in hijacking mechanisms that reverse post-translational modifications, block host translation, and disable host defenses; a further ˜29% self-assembled into heteromeric states that provided insight into how the viral replication and translation complex forms. To make these 3D models more accessible, we devised a structural coverage map, a novel visualization method to show what is-and is not-known about the 3D structure of the viral proteome. We integrated the coverage map into an accompanying online resource (https://aquaria.ws/covid) that can be used to find and explore models corresponding to the 79 structural states identified in this work. The resulting Aquaria-COVID resource helps scientists use emerging structural data to understand the mechanisms underlying coronavirus infection and draws attention to the 31% of the viral proteome that remains structurally unknown or dark.


Subject(s)
Angiotensin-Converting Enzyme 2/metabolism , Host-Pathogen Interactions/genetics , Protein Processing, Post-Translational , SARS-CoV-2/metabolism , Spike Glycoprotein, Coronavirus/metabolism , Amino Acid Transport Systems, Neutral/chemistry , Amino Acid Transport Systems, Neutral/genetics , Amino Acid Transport Systems, Neutral/metabolism , Angiotensin-Converting Enzyme 2/chemistry , Angiotensin-Converting Enzyme 2/genetics , Binding Sites , COVID-19/genetics , COVID-19/metabolism , COVID-19/virology , Computational Biology/methods , Coronavirus Envelope Proteins/chemistry , Coronavirus Envelope Proteins/genetics , Coronavirus Envelope Proteins/metabolism , Coronavirus Nucleocapsid Proteins/chemistry , Coronavirus Nucleocapsid Proteins/genetics , Coronavirus Nucleocapsid Proteins/metabolism , Humans , Mitochondrial Membrane Transport Proteins/chemistry , Mitochondrial Membrane Transport Proteins/genetics , Mitochondrial Membrane Transport Proteins/metabolism , Models, Molecular , Molecular Mimicry , Neuropilin-1/chemistry , Neuropilin-1/genetics , Neuropilin-1/metabolism , Phosphoproteins/chemistry , Phosphoproteins/genetics , Phosphoproteins/metabolism , Protein Binding , Protein Conformation, alpha-Helical , Protein Conformation, beta-Strand , Protein Interaction Domains and Motifs , Protein Interaction Mapping/methods , Protein Multimerization , SARS-CoV-2/chemistry , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/chemistry , Spike Glycoprotein, Coronavirus/genetics , Viral Matrix Proteins/chemistry , Viral Matrix Proteins/genetics , Viral Matrix Proteins/metabolism , Viroporin Proteins/chemistry , Viroporin Proteins/genetics , Viroporin Proteins/metabolism , Virus Replication
14.
Proteomics ; 21(10): e2000279, 2021 05.
Article in English | MEDLINE | ID: covidwho-1384282

ABSTRACT

While protein-protein interaction is the first step of the SARS-CoV-2 infection, recent comparative proteomic profiling enabled the identification of over 11,000 protein dynamics, thus providing a comprehensive reflection of the molecular mechanisms underlying the cellular system in response to viral infection. Here we summarize and rationalize the results obtained by various mass spectrometry (MS)-based proteomic approaches applied to the functional characterization of proteins and pathways associated with SARS-CoV-2-mediated infections in humans. Comparative analysis of cell-lines versus tissue samples indicates that our knowledge in proteome profile alternation in response to SARS-CoV-2 infection is still incomplete and the tissue-specific response to SARS-CoV-2 infection can probably not be recapitulated efficiently by in vitro experiments. However, regardless of the viral infection period, sample types, and experimental strategies, a thorough cross-comparison of the recently published proteome, phosphoproteome, and interactome datasets led to the identification of a common set of proteins and kinases associated with PI3K-Akt, EGFR, MAPK, Rap1, and AMPK signaling pathways. Ephrin receptor A2 (EPHA2) was identified by 11 studies including all proteomic platforms, suggesting it as a potential future target for SARS-CoV-2 infection mechanisms and the development of new therapeutic strategies. We further discuss the potentials of future proteomics strategies for identifying prognostic SARS-CoV-2 responsive age-, gender-dependent, tissue-specific protein targets.


Subject(s)
COVID-19/metabolism , Host-Pathogen Interactions , Mass Spectrometry/methods , Proteomics/methods , SARS-CoV-2/physiology , Animals , COVID-19/diagnosis , COVID-19/pathology , Humans , Protein Interaction Mapping/methods , Protein Interaction Maps , Protein Kinases/analysis , Protein Kinases/metabolism , Protein Processing, Post-Translational , Proteome/analysis , Proteome/metabolism , Receptor, EphA2/analysis , Receptor, EphA2/metabolism , Signal Transduction
15.
Molecules ; 26(17)2021 Aug 24.
Article in English | MEDLINE | ID: covidwho-1374471

ABSTRACT

The emergence of COVID-19 continues to pose severe threats to global public health. The pandemic has infected over 171 million people and claimed more than 3.5 million lives to date. We investigated the binding potential of antiviral cyanobacterial proteins including cyanovirin-N, scytovirin and phycocyanin with fundamental proteins involved in attachment and replication of SARS-CoV-2. Cyanovirin-N displayed the highest binding energy scores (-16.8 ± 0.02 kcal/mol, -12.3 ± 0.03 kcal/mol and -13.4 ± 0.02 kcal/mol, respectively) with the spike protein, the main protease (Mpro) and the papainlike protease (PLpro) of SARS-CoV-2. Cyanovirin-N was observed to interact with the crucial residues involved in the attachment of the human ACE2 receptor. Analysis of the binding affinities calculated employing the molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) approach revealed that all forms of energy, except the polar solvation energy, favourably contributed to the interactions of cyanovirin-N with the viral proteins. With particular emphasis on cyanovirin-N, the current work presents evidence for the potential inhibition of SARS-CoV-2 by cyanobacterial proteins, and offers the opportunity for in vitro and in vivo experiments to deploy the cyanobacterial proteins as valuable therapeutics against COVID-19.


Subject(s)
Antiviral Agents/pharmacology , Bacterial Proteins/pharmacology , COVID-19/drug therapy , Coronavirus Protease Inhibitors/pharmacology , Antiviral Agents/therapeutic use , Bacterial Proteins/therapeutic use , Bacterial Proteins/ultrastructure , COVID-19/virology , Coronavirus 3C Proteases/antagonists & inhibitors , Coronavirus 3C Proteases/metabolism , Coronavirus 3C Proteases/ultrastructure , Coronavirus Papain-Like Proteases/antagonists & inhibitors , Coronavirus Papain-Like Proteases/metabolism , Coronavirus Papain-Like Proteases/ultrastructure , Coronavirus Protease Inhibitors/therapeutic use , Coronavirus Protease Inhibitors/ultrastructure , Humans , Molecular Docking Simulation , Molecular Dynamics Simulation , Protein Binding , Protein Interaction Mapping , Spike Glycoprotein, Coronavirus/antagonists & inhibitors , Spike Glycoprotein, Coronavirus/metabolism , Spike Glycoprotein, Coronavirus/ultrastructure , X-Ray Diffraction
16.
Medicine (Baltimore) ; 100(32): e26881, 2021 Aug 13.
Article in English | MEDLINE | ID: covidwho-1358518

ABSTRACT

ABSTRACT: Radix Isatidis (Banlangen) is a well-known traditional Chinese medicine for the treatment of different diseases and prevention of many body disorders. Besides, it also plays a pivotal role in novel coronavirus pneumonia, coronavirus disease 2019 (COVID-19). However, few researchers know its active ingredients and mechanism of action for COVID-19. To find whether Banlangen has a pharmacological effect on COVID-19. In this research, we systematically analyze Banlangen and COVID-19 through network pharmacology technology. A total of 33 active ingredients in Banlangen, 92 targets of the active ingredients, and 259 appropriate targets of COVID-19 were obtained, with 11 common targets. The analysis of the biological process of gene ontology and the enrichment of Kyoto Encyclopedia of Genes and Genomes signaling pathway suggests that Banlangen participated in the biological processes of protein phosphatase binding, tetrapyrrole binding, the apoptotic process involving cysteine-type endopeptidase activity, etc. The COVID-19 may be treated by regulating advanced glycation end products/a receptor for advanced glycation end products signaling pathway, interleukin-17 signaling pathway, tumor necrosis factor signaling pathway, sphingolipid signaling pathway, and p53 signaling pathway. Banlangen has a potential pharmacological effect on COVID-19, which has the value of further exploration in the following experiment and clinical application.


Subject(s)
COVID-19/drug therapy , Drugs, Chinese Herbal/pharmacokinetics , Drugs, Chinese Herbal/standards , Drugs, Chinese Herbal/therapeutic use , Humans , Protein Interaction Mapping/methods
17.
Bioengineered ; 12(1): 4054-4069, 2021 12.
Article in English | MEDLINE | ID: covidwho-1348035

ABSTRACT

During the pandemic of the coronavirus disease 2019, there exist quite a few studies on angiotensin-converting enzyme 2 (ACE2) and SARS-CoV-2 infection, while little is known about ACE2 in hepatocellular carcinoma (HCC). The detailed mechanism among ACE2 and HCC still remains unclear, which needs to be further investigated. In the current study with a total of 6,926 samples, ACE2 expression was downregulated in HCC compared with non-HCC samples (standardized mean difference = -0.41). With the area under the curve of summary receiver operating characteristic = 0.82, ACE2 expression showed a better ability to differentiate HCC from non-HCC. The mRNA expression of ACE2 was related to the age, alpha-fetoprotein levels and cirrhosis of HCC patients, and it was identified as a protected factor for HCC patients via Kaplan-Meier survival, Cox regression analyses. The potential molecular mechanism of ACE2 may be relevant to catabolic and cell division. In all, decreasing ACE2 expression can be seen in HCC, and its protective role for HCC patients and underlying mechanisms were explored in the study.


Subject(s)
Angiotensin-Converting Enzyme 2/genetics , Carcinoma, Hepatocellular/genetics , Liver Cirrhosis/genetics , Liver Neoplasms/genetics , Neoplasm Proteins/genetics , Receptors, Virus/genetics , alpha-Fetoproteins/genetics , Age Factors , Aged , Angiotensin-Converting Enzyme 2/metabolism , Area Under Curve , COVID-19/virology , Carcinoma, Hepatocellular/diagnosis , Carcinoma, Hepatocellular/mortality , Carcinoma, Hepatocellular/pathology , Databases, Genetic , Datasets as Topic , Female , Gene Expression Regulation, Neoplastic , Humans , Liver Cirrhosis/diagnosis , Liver Cirrhosis/mortality , Liver Cirrhosis/pathology , Liver Neoplasms/diagnosis , Liver Neoplasms/mortality , Liver Neoplasms/pathology , Male , Middle Aged , Neoplasm Proteins/classification , Neoplasm Proteins/metabolism , Protective Factors , Protein Interaction Mapping , ROC Curve , Receptors, Virus/metabolism , SARS-CoV-2/pathogenicity , Survival Analysis , alpha-Fetoproteins/metabolism
18.
Brief Bioinform ; 22(2): 832-844, 2021 03 22.
Article in English | MEDLINE | ID: covidwho-1343659

ABSTRACT

While leading to millions of people's deaths every year the treatment of viral infectious diseases remains a huge public health challenge.Therefore, an in-depth understanding of human-virus protein-protein interactions (PPIs) as the molecular interface between a virus and its host cell is of paramount importance to obtain new insights into the pathogenesis of viral infections and development of antiviral therapeutic treatments. However, current human-virus PPI database resources are incomplete, lack annotation and usually do not provide the opportunity to computationally predict human-virus PPIs. Here, we present the Human-Virus Interaction DataBase (HVIDB, http://zzdlab.com/hvidb/) that provides comprehensively annotated human-virus PPI data as well as seamlessly integrates online PPI prediction tools. Currently, HVIDB highlights 48 643 experimentally verified human-virus PPIs covering 35 virus families, 6633 virally targeted host complexes, 3572 host dependency/restriction factors as well as 911 experimentally verified/predicted 3D complex structures of human-virus PPIs. Furthermore, our database resource provides tissue-specific expression profiles of 6790 human genes that are targeted by viruses and 129 Gene Expression Omnibus series of differentially expressed genes post-viral infections. Based on these multifaceted and annotated data, our database allows the users to easily obtain reliable information about PPIs of various human viruses and conduct an in-depth analysis of their inherent biological significance. In particular, HVIDB also integrates well-performing machine learning models to predict interactions between the human host and viral proteins that are based on (i) sequence embedding techniques, (ii) interolog mapping and (iii) domain-domain interaction inference. We anticipate that HVIDB will serve as a one-stop knowledge base to further guide hypothesis-driven experimental efforts to investigate human-virus relationships.


Subject(s)
Databases, Protein , Protein Interaction Mapping/methods , Proteins/metabolism , Viral Proteins/metabolism , Gene Expression Profiling , Humans , Machine Learning , Protein Array Analysis , Protein Conformation , Proteins/chemistry , Proteins/genetics , Viral Proteins/chemistry , Viral Proteins/genetics
19.
Cell Rep ; 36(5): 109482, 2021 08 03.
Article in English | MEDLINE | ID: covidwho-1312984

ABSTRACT

Bearing a relatively large single-stranded RNA genome in nature, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) utilizes sophisticated replication/transcription complexes (RTCs), mainly composed of a network of nonstructural proteins and nucleocapsid protein, to establish efficient infection. In this study, we develop an innovative interaction screening strategy based on phase separation in cellulo, namely compartmentalization of protein-protein interactions in cells (CoPIC). Utilizing CoPIC screening, we map the interaction network among RTC-related viral proteins. We identify a total of 47 binary interactions among 14 proteins governing replication, discontinuous transcription, and translation of coronaviruses. Further exploration via CoPIC leads to the discovery of extensive ternary complexes composed of these components, which infer potential higher-order complexes. Taken together, our results present an efficient and robust interaction screening strategy, and they indicate the existence of a complex interaction network among RTC-related factors, thus opening up opportunities to understand SARS-CoV-2 biology and develop therapeutic interventions for COVID-19.


Subject(s)
COVID-19/virology , Protein Interaction Mapping/methods , Proteome , SARS-CoV-2/pathogenicity , Viral Nonstructural Proteins/physiology , Animals , Caco-2 Cells , Cell Compartmentation , Cell Line , Chlorocebus aethiops , HEK293 Cells , Humans , Protein Interaction Maps , Vero Cells , Virus Replication
20.
Brief Bioinform ; 22(6)2021 11 05.
Article in English | MEDLINE | ID: covidwho-1279281

ABSTRACT

Viral infection involves a large number of protein-protein interactions (PPIs) between human and virus. The PPIs range from the initial binding of viral coat proteins to host membrane receptors to the hijacking of host transcription machinery. However, few interspecies PPIs have been identified, because experimental methods including mass spectrometry are time-consuming and expensive, and molecular dynamic simulation is limited only to the proteins whose 3D structures are solved. Sequence-based machine learning methods are expected to overcome these problems. We have first developed the LSTM model with word2vec to predict PPIs between human and virus, named LSTM-PHV, by using amino acid sequences alone. The LSTM-PHV effectively learnt the training data with a highly imbalanced ratio of positive to negative samples and achieved AUCs of 0.976 and 0.973 and accuracies of 0.984 and 0.985 on the training and independent datasets, respectively. In predicting PPIs between human and unknown or new virus, the LSTM-PHV learned greatly outperformed the existing state-of-the-art PPI predictors. Interestingly, learning of only sequence contexts as words is sufficient for PPI prediction. Use of uniform manifold approximation and projection demonstrated that the LSTM-PHV clearly distinguished the positive PPI samples from the negative ones. We presented the LSTM-PHV online web server and support data that are freely available at http://kurata35.bio.kyutech.ac.jp/LSTM-PHV.


Subject(s)
Computational Biology/methods , Host-Pathogen Interactions , Protein Interaction Mapping/methods , Software , Viral Proteins/metabolism , Virus Diseases/metabolism , Virus Diseases/virology , Algorithms , Amino Acid Sequence , Benchmarking , Databases, Protein , Deep Learning , Humans , Protein Interaction Domains and Motifs , Protein Interaction Maps , Reproducibility of Results , Web Browser
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