A SARS-CoV-2 – host proximity interactome

AbstractViral replication is dependent on interactions between viral polypeptides and host proteins. Identifying virus-host protein interactions can thus uncover unique opportunities for interfering with the virus life cycle via novel drug compounds or drug repurposing. Importantly, many viral-host protein interactions take place at intracellular membranes and poorly soluble organelles, which are difficult to profile using classical biochemical purification approaches. Applying proximity-dependent biotinylation (BioID) with the fast-acting miniTurbo enzyme to 27 SARS-CoV-2 proteins in a lung adenocarcinoma cell line (A549), we detected 7810 proximity interactions (7382 of which are new for SARS-CoV-2) with 2242 host proteins (results available at covid19interactome.org). These results complement and dramatically expand upon recent affinity purification-based studies identifying stable host-virus protein complexes, and offer an unparalleled view of membrane-associated processes critical for viral production. Host cell organellar markers were also subjected to BioID in parallel, allowing us to propose modes of action for several viral proteins in the context of host proteome remodelling. In summary, our dataset identifies numerous high confidence proximity partners for SARS-CoV-2 viral proteins, and describes potential mechanisms for their effects on specific host cell functions.

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The Coronavirus Network Explorer: Mining a large-scale knowledge graph for effects of SARS-CoV-2 on host cell function

10.1101/2020.09.14.296327 ◽

2020 ◽

Author(s):

Andreas Krämer ◽

Jean-Noël Billaud ◽

Stuart Tugendreich ◽

Dan Shiftman ◽

Martin Jones ◽

...

Keyword(s):

Host Cell ◽

Drug Targets ◽

Large Scale ◽

Cell Function ◽

Affinity Purification ◽

Viral Proteins ◽

Biomedical Literature ◽

Knowledge Graph ◽

Cell Functions ◽

Interactive Network

Building on recent work that identified human host proteins that interact with SARS-CoV-2 viral proteins in the context of an affinity-purification mass spectrometry screen, we use a machine learning-based approach to connect the viral proteins to relevant biological functions and diseases in a large-scale knowledge graph derived from the biomedical literature. Our aim is to explore how SARS-CoV-2 could interfere with various host cell functions, and also to identify additional drug targets amongst the host genes that could potentially be modulated against COVID-19. Results are presented in the form of interactive network visualizations, that allow exploration of underlying experimental evidence. A selection of networks is discussed in the context of recent clinical observations.

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Impact Analysis of SARS-CoV2 on Signaling Pathways during COVID19 Pathogenesis using Codon Usage Assisted Host-Viral Protein Interactions

10.1101/2020.07.29.226217 ◽

2020 ◽

Author(s):

Jayanta Kumar Das ◽

Subhadip Chakraborty ◽

Swarup Roy

Keyword(s):

Codon Usage ◽

Signaling Pathways ◽

Protein Interaction ◽

Protein Interactions ◽

Viral Protein ◽

Viral Proteins ◽

Host Protein ◽

Host Proteins ◽

Disease Pathogenesis ◽

Pattern Similarity

AbstractUnderstanding the molecular mechanism of COVID19 disease pathogenesis helps in the rapid development of therapeutic targets. Usually, viral protein targets host proteins in an organized fashion. The pathogen may target cell signaling pathways to disrupt the pathway genes’ regular activities, resulting in disease. Understanding the interaction mechanism of viral and host proteins involved in different signaling pathways may help decipher the attacking mechanism on the signal transmission during diseases, followed by discovering appropriate therapeutic solutions.The expression of any viral gene depends mostly on the host translational machinery. Recent studies report the great significance of codon usage biases in establishing host-viral protein-protein interactions (PPI). Exploiting the codon usage patterns between a pair of co-evolved host and viral proteins may present novel insight into the host-viral protein interactomes during disease pathogenesis. Leveraging the codon usage pattern similarity (and dissimilarity), we propose a computational scheme to recreate the hostviral protein interaction network (HVPPI). We use seventeen (17) essential signaling pathways for our current work and study the possible targeting mechanism of SARS-CoV2 viral proteins on such pathway proteins. We infer both negatively and positively interacting edges in the network. We can find a relationship where one host protein may target by more than one viral protein.Extensive analysis performed to understand the network topologically and the attacking behavior of the viral proteins. Our study reveals that viral proteins, mostly utilize codons, rare in the targeted host proteins (negatively correlated interaction). Among non-structural proteins, NSP3 and structural protein, Spike (S) protein, are the most influential proteins in interacting multiple host proteins. In ranking the most affected pathways, MAPK pathways observe to be worst affected during the COVID-19 disease. A good number of targeted proteins are highly central in host protein interaction networks. Proteins participating in multiple pathways are also highly connected in their own PPI and mostly targeted by multiple viral proteins.

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Comprehensive analysis of the host-virus interactome of SARS-CoV-2

10.1101/2020.12.31.424961 ◽

2021 ◽

Author(s):

Zhen Chen ◽

Chao Wang ◽

Xu Feng ◽

Litong Nie ◽

Mengfan Tang ◽

...

Keyword(s):

Protein Interactions ◽

Drug Targets ◽

Affinity Purification ◽

Functional Characterization ◽

Viral Proteins ◽

Host Cells ◽

Virus Protein ◽

N Protein ◽

Protein Protein Interaction ◽

Human Coronaviruses

Host-virus protein-protein interaction is the key component of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lifecycle. We conducted a comprehensive interactome study between the virus and host cells using tandem affinity purification and proximity labeling strategies and identified 437 human proteins as the high-confidence interacting proteins. Functional characterization and further validation of these interactions elucidated how distinct SARS-CoV-2 viral proteins participate in its lifecycle, and discovered potential drug targets to the treatment of COVID-19. The interactomes of two key SARS-CoV-2 encoded viral proteins, NSP1 and N protein, were compared with the interactomes of their counterparts in other human coronaviruses. These comparisons not only revealed common host pathways these viruses manipulate for their survival, but also showed divergent protein-protein interactions that may explain differences in disease pathology. This comprehensive interactome of coronavirus disease-2019 provides valuable resources for understanding and treating this disease.

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Different Subtypes of Influenza Viruses Target Different Human Proteins and Pathways Leading to Different Pathogenic Phenotypes

BioMed Research International ◽

10.1155/2019/4794910 ◽

2019 ◽

Vol 2019 ◽

pp. 1-7

Author(s):

Yujie Wang ◽

Ting Song ◽

Kaiwu Li ◽

Yuan Jin ◽

Junjie Yue ◽

...

Keyword(s):

Protein Interactions ◽

Influenza A ◽

Viral Proteins ◽

Influenza Viruses ◽

Host Protein ◽

Protein Protein Interactions ◽

Influenza A Viruses ◽

Host Proteins ◽

Pathogenic Mechanisms ◽

Human Proteins

Different subtypes of influenza A viruses (IAVs) cause different pathogenic phenotypes after infecting human bodies. Analysis of the interactions between viral proteins and the host proteins may provide insights into the pathogenic mechanisms of the virus. In this paper, we found that the same proteins (nucleoprotein and neuraminidase) of H1N1 and H5N1 have different impacts on the NF-κB activation. By further examining the virus–host protein–protein interactions, we found that both NP and NA proteins of the H1N1 and H5N1 viruses target different host proteins. These results indicate that different subtypes of influenza viruses target different human proteins and pathways leading to different pathogenic phenotypes.

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Mapping the SARS-CoV-2–Host Protein–Protein Interactome by Affinity Purification Mass Spectrometry and Proximity-Dependent Biotin Labeling: A Rational and Straightforward Route to Discover Host-Directed Anti-SARS-CoV-2 Therapeutics

International Journal of Molecular Sciences ◽

10.3390/ijms22020532 ◽

2021 ◽

Vol 22 (2) ◽

pp. 532

Author(s):

Rosa Terracciano ◽

Mariaimmacolata Preianò ◽

Annalisa Fregola ◽

Corrado Pelaia ◽

Tiziana Montalcini ◽

...

Keyword(s):

Mass Spectrometry ◽

Drug Discovery ◽

Protein Interactions ◽

Drug Targets ◽

Affinity Purification ◽

Drug Repurposing ◽

Host Protein ◽

Host Cells ◽

Traditional Drug ◽

Approved Drugs

Protein–protein interactions (PPIs) are the vital engine of cellular machinery. After virus entry in host cells the global organization of the viral life cycle is strongly regulated by the formation of virus-host protein interactions. With the advent of high-throughput -omics platforms, the mirage to obtain a “high resolution” view of virus–host interactions has come true. In fact, the rapidly expanding approaches of mass spectrometry (MS)-based proteomics in the study of PPIs provide efficient tools to identify a significant number of potential drug targets. Generation of PPIs maps by affinity purification-MS and by the more recent proximity labeling-MS may help to uncover cellular processes hijacked and/or altered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), providing promising therapeutic targets. The possibility to further validate putative key targets from high-confidence interactions between viral bait and host protein through follow-up MS-based multi-omics experiments offers an unprecedented opportunity in the drug discovery pipeline. In particular, drug repurposing, making use of already existing approved drugs directly targeting these identified and validated host interactors, might shorten the time and reduce the costs in comparison to the traditional drug discovery process. This route might be promising for finding effective antiviral therapeutic options providing a turning point in the fight against the coronavirus disease-2019 (COVID-19) outbreak.

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Poxvirus Proteomics and Virus-Host Protein Interactions

Microbiology and Molecular Biology Reviews ◽

10.1128/mmbr.00026-09 ◽

2009 ◽

Vol 73 (4) ◽

pp. 730-749 ◽

Cited By ~ 33

Author(s):

Kim Van Vliet ◽

Mohamed R. Mohamed ◽

Leiliang Zhang ◽

Nancy Yaneth Villa ◽

Steven J. Werden ◽

...

Keyword(s):

Protein Interactions ◽

Viral Proteins ◽

Specific Protein ◽

Host Protein ◽

Virus Protein ◽

Protein Protein Interactions ◽

Host Interaction ◽

Chip Technology ◽

Poxvirus Genome ◽

Protein Components

SUMMARY Studies of the functional proteins encoded by the poxvirus genome provide information about the composition of the virus as well as individual virus-virus protein and virus-host protein interactions, which provides insight into viral pathogenesis and drug discovery. Widely used proteomic techniques to identify and characterize specific protein-protein interactions include yeast two-hybrid studies and coimmunoprecipitations. Recently, various mass spectrometry techniques have been employed to identify viral protein components of larger complexes. These methods, combined with structural studies, can provide new information about the putative functions of viral proteins as well as insights into virus-host interaction dynamics. For viral proteins of unknown function, identification of either viral or host binding partners provides clues about their putative function. In this review, we discuss poxvirus proteomics, including the use of proteomic methodologies to identify viral components and virus-host protein interactions. High-throughput global protein expression studies using protein chip technology as well as new methods for validating putative protein-protein interactions are also discussed.

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A systems-based method to repurpose marketed therapeutics for antiviral use: a SARS-CoV-2 case study

Life Science Alliance ◽

10.26508/lsa.202000904 ◽

2021 ◽

Vol 4 (5) ◽

pp. e202000904

Author(s):

Mengran Wang ◽

Johanna B Withers ◽

Piero Ricchiuto ◽

Ivan Voitalov ◽

Michael McAnally ◽

...

Keyword(s):

Protein Interactions ◽

Sequence Similarity ◽

Interaction Network ◽

Drug Repurposing ◽

Viral Proteins ◽

Antiviral Drugs ◽

Host Protein ◽

Physical Interaction ◽

Protein Protein Interactions ◽

Emerging Viruses

This study describes two complementary methods that use network-based and sequence similarity tools to identify drug repurposing opportunities predicted to modulate viral proteins. This approach could be rapidly adapted to new and emerging viruses. The first method built and studied a virus–host–physical interaction network; a three-layer multimodal network of drug target proteins, human protein–protein interactions, and viral–host protein–protein interactions. The second method evaluated sequence similarity between viral proteins and other proteins, visualized by constructing a virus–host–similarity interaction network. Methods were validated on the human immunodeficiency virus, hepatitis B, hepatitis C, and human papillomavirus, then deployed on SARS-CoV-2. Comparison of virus–host–physical interaction predictions to known antiviral drugs had AUCs of 0.69, 0.59, 0.78, and 0.67, respectively, reflecting that the scores are predictive of effective drugs. For SARS-CoV-2, 569 candidate drugs were predicted, of which 37 had been included in clinical trials for SARS-CoV-2 (AUC = 0.75, P-value 3.21 × 10−3). As further validation, top-ranked candidate antiviral drugs were analyzed for binding to protein targets in silico; binding scores generated by BindScope indicated a 70% success rate.

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The Coronavirus Network Explorer: mining a large-scale knowledge graph for effects of SARS-CoV-2 on host cell function

BMC Bioinformatics ◽

10.1186/s12859-021-04148-x ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Andreas Krämer ◽

Jean-Noël Billaud ◽

Stuart Tugendreich ◽

Dan Shiffman ◽

Martin Jones ◽

...

Keyword(s):

Machine Learning ◽

Host Cell ◽

Large Scale ◽

Drug Repurposing ◽

Viral Proteins ◽

Biomedical Literature ◽

Knowledge Graph ◽

Biological Functions ◽

Cell Functions ◽

Clinical Observations

Abstract Background Leveraging previously identified viral interactions with human host proteins, we apply a machine learning-based approach to connect SARS-CoV-2 viral proteins to relevant host biological functions, diseases, and pathways in a large-scale knowledge graph derived from the biomedical literature. Our goal is to explore how SARS-CoV-2 could interfere with various host cell functions, and to identify drug targets amongst the host genes that could potentially be modulated against COVID-19 by repurposing existing drugs. The machine learning model employed here involves gene embeddings that leverage causal gene expression signatures curated from literature. In contrast to other network-based approaches for drug repurposing, our approach explicitly takes the direction of effects into account, distinguishing between activation and inhibition. Results We have constructed 70 networks connecting SARS-CoV-2 viral proteins to various biological functions, diseases, and pathways reflecting viral biology, clinical observations, and co-morbidities in the context of COVID-19. Results are presented in the form of interactive network visualizations through a web interface, the Coronavirus Network Explorer (CNE), that allows exploration of underlying experimental evidence. We find that existing drugs targeting genes in those networks are strongly enriched in the set of drugs that are already in clinical trials against COVID-19. Conclusions The approach presented here can identify biologically plausible hypotheses for COVID-19 pathogenesis, explicitly connected to the immunological, virological and pathological observations seen in SARS-CoV-2 infected patients. The discovery of repurposable drugs is driven by prior knowledge of relevant functional endpoints that reflect known viral biology or clinical observations, therefore suggesting potential mechanisms of action. We believe that the CNE offers relevant insights that go beyond more conventional network approaches, and can be a valuable tool for drug repurposing. The CNE is available at https://digitalinsights.qiagen.com/coronavirus-network-explorer.

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Cascading from SARS-CoV-2 to Parkinson’s Disease through Protein-Protein Interactions

Viruses ◽

10.3390/v13050897 ◽

2021 ◽

Vol 13 (5) ◽

pp. 897

Author(s):

Ernesto Estrada

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Protein Interactions ◽

Molecular Level ◽

Direct Consequence ◽

Viral Proteins ◽

Clinical Findings ◽

Post Translational Modification ◽

Host Proteins ◽

Bioinformatic Tools

Extensive extrapulmonary damages in a dozen of organs/systems, including the central nervous system (CNS), are reported in patients of the coronavirus disease 2019 (COVID-19). Three cases of Parkinson’s disease (PD) have been reported as a direct consequence of COVID-19. In spite of the scarce data for establishing a definitive link between COVID-19 and PD, some hypotheses have been proposed to explain the cases reported. They, however, do not fit well with the clinical findings reported for COVID-19 patients, in general, and for the PD cases reported, in particular. Given the importance of this potential connection, we present here a molecular-level mechanistic hypothesis that explains well these findings and will serve to explore the potential CNS damage in COVID-19 patients. The model explaining the cascade effects from COVID-19 to CNS is developed by using bioinformatic tools. It includes the post-translational modification of host proteins in the lungs by viral proteins, the transport of modified host proteins via exosomes out the lungs, and the disruption of protein-protein interaction in the CNS by these modified host proteins. Our hypothesis is supported by finding 44 proteins significantly expressed in the CNS which are associated with PD and whose interactions can be perturbed by 24 host proteins significantly expressed in the lungs. These 24 perturbators are found to interact with viral proteins and to form part of the cargoes of exosomes in human tissues. The joint set of perturbators and PD-vulnerable proteins form a tightly connected network with significantly more connections than expected by selecting a random cluster of proteins of similar size from the human proteome. The molecular-level mechanistic hypothesis presented here provides several routes for the cascading of effects from the lungs of COVID-19 patients to PD. In particular, the disruption of autophagy/ubiquitination processes appears as an important mechanism that triggers the generation of large amounts of exosomes containing perturbators in their cargo, which would insult several PD-vulnerable proteins, potentially triggering Parkinsonism in COVID-19 patients.

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Native Structure-Based Peptides as Potential Protein–Protein Interaction Inhibitors of SARS-CoV-2 Spike Protein and Human ACE2 Receptor

Molecules ◽

10.3390/molecules26082157 ◽

2021 ◽

Vol 26 (8) ◽

pp. 2157

Author(s):

Norbert Odolczyk ◽

Ewa Marzec ◽

Maria Winiewska-Szajewska ◽

Jarosław Poznański ◽

Piotr Zielenkiewicz

Keyword(s):

Drug Development ◽

Protein Interactions ◽

Rna Virus ◽

Antiviral Drug ◽

Host Protein ◽

Host Cells ◽

Cell Surface Receptor ◽

Short Peptides ◽

Virus Protein ◽

Positive Strand Rna

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) is a positive-strand RNA virus that causes severe respiratory syndrome in humans, which is now referred to as coronavirus disease 2019 (COVID-19). Since December 2019, the new pathogen has rapidly spread globally, with over 65 million cases reported to the beginning of December 2020, including over 1.5 million deaths. Unfortunately, currently, there is no specific and effective treatment for COVID-19. As SARS-CoV-2 relies on its spike proteins (S) to bind to a host cell-surface receptor angiotensin-converting enzyme-2(ACE2), and this interaction is proved to be responsible for entering a virus into host cells, it makes an ideal target for antiviral drug development. In this work, we design three very short peptides based on the ACE2 sequence/structure fragments, which may effectively bind to the receptor-binding domain (RBD) of S protein and may, in turn, disrupt the important virus-host protein–protein interactions, blocking early steps of SARS-CoV-2 infection. Two of our peptides bind to virus protein with affinity in nanomolar range, and as very short peptides have great potential for drug development.

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