scholarly journals A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing

2020 ◽  
Author(s):  
David E. Gordon ◽  
Gwendolyn M. Jang ◽  
Mehdi Bouhaddou ◽  
Jiewei Xu ◽  
Kirsten Obernier ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
Affinity Purification ◽  
Drug Repurposing ◽  
Human Protein ◽  
Protein Protein Interaction ◽  
Human Proteins ◽  
Interaction Map ◽  
Novel Coronavirus ◽  
Approved Drugs

ABSTRACTAn outbreak of the novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 290,000 people since the end of 2019, killed over 12,000, and caused worldwide social and economic disruption1,2. There are currently no antiviral drugs with proven efficacy nor are there vaccines for its prevention. Unfortunately, the scientific community has little knowledge of the molecular details of SARS-CoV-2 infection. To illuminate this, we cloned, tagged and expressed 26 of the 29 viral proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), which identified 332 high confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 existing FDA-approved drugs, drugs in clinical trials and/or preclinical compounds, that we are currently evaluating for efficacy in live SARS-CoV-2 infection assays. The identification of host dependency factors mediating virus infection may provide key insights into effective molecular targets for developing broadly acting antiviral therapeutics against SARS-CoV-2 and other deadly coronavirus strains.

scholarly journals 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

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.

scholarly journals A network biology approach for identifying crucial host targets for COVID-19

2020 ◽  
Author(s):  
Ranjan Kumar Barman ◽  
Anirban Mukhopadhyay ◽  
Ujjwal Maulik ◽  
Santasabuj Das
Keyword(s):  
Drug Targets ◽  
Rna Synthesis ◽  
Interaction Network ◽  
Drug Repurposing ◽  
Adenosine Monophosphate ◽  
Network Biology ◽  
Centrality Measures ◽  
Potential Candidate ◽  
Human Proteins ◽  
Novel Coronavirus

In late December 2019, an outbreak of novel coronavirus SARS-CoV-2 originated in Wuhan city of china, has infected over 30,00,000 people worldwide. SARS-CoV-2 is a causative agent of COVID-19 that has killed over 2,11,000 people and created social and financial crisis globally. Currently, there is no effective antiviral drugs and vaccine available for the prevention of COVID-19. Therefore, the scientific community is more focused on drug repurposing for the treatment of COVID-19. Here, we propose a network biology approach to identify candidate biomarkers for COVID-19. We critically analyze SARS-CoV-2 targeted human proteins and their interaction network. We utilize a combination of essential network centrality measures and functional properties of human proteins to find the critical human targets for SARS-CoV-2 infection. From the candidate pool of 301 human proteins, interestingly we found that PRKACA, RHOA, CDK5RAP2, and CEP250 are candidates for therapeutic targets for COVID-19. PRKACA and CEP250 have also been found by another group for potential candidates for drug targets in treating COVID-19. We found that potential candidate drugs/compounds such as guanosine triphosphate, remdesivir, adenosine monophosphate, MgATP, and H-89 dihydrochloride for COVID-19. Most of the therapeutics development studies for COVID-19 are tried to block RNA synthesis through RNA dependent RNA Polymerase (RdRP). Our findings also suggest for blocking RNA synthesis in treating COVID-19.

scholarly journals Identification of potential therapeutic targets and mechanisms of COVID-19 through network analysis and screening of chemicals and herbal ingredients

2021 ◽  
Author(s):  
Hong Wang ◽  
Jingqing Zhang ◽  
Zhigang Lu ◽  
Weina Dai ◽  
Chuanjiang Ma ◽  
...  
Keyword(s):  
Drug Targets ◽  
Drug Repurposing ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Therapeutic Effects ◽  
Protein Protein Interaction ◽  
Disease Module ◽  
Approved Drugs ◽  
Data Driven Modeling ◽  
The Relationship

Abstract After experiencing the COVID-19 pandemic, it is widely acknowledged that a rapid drug repurposing method is highly needed. A series of useful drug repurposing tools have been developed based on data-driven modeling and network pharmacology. Based on the disease module, we identified several hub proteins that play important roles in the onset and development of the COVID-19, which are potential targets for repositioning approved drugs. Moreover, different network distance metrics were applied to quantify the relationship between drug targets and COVID-19 disease targets in the protein–protein-interaction (PPI) network and predict COVID-19 therapeutic effects of bioactive herbal ingredients and chemicals. Furthermore, the tentative mechanisms of candidates were illustrated through molecular docking and gene enrichment analysis. We obtained 15 chemical and 15 herbal ingredient candidates and found that different drugs may play different roles in the process of virus invasion and the onset and development of the COVID-19 disease. Given pandemic outbreaks, our method has an undeniable immense advantage in the feasibility analysis of drug repurposing or drug screening, especially in the analysis of herbal ingredients.

scholarly journals An Mtb-Human Protein-Protein Interaction Map Reveals that Bacterial LpqN Antagonizes CBL, a Host Ubiquitin Ligase that Regulates the Balance Between Anti-Viral and Anti-Bacterial Responses

2017 ◽  
Author(s):  
Bennett H. Penn ◽  
Zoe Netter ◽  
Jeffrey R. Johnson ◽  
John Von Dollen ◽  
Gwendolyn M. Jang ◽  
...  
Keyword(s):  
Ubiquitin Ligase ◽  
Protein Interactions ◽  
Human Protein ◽  
Viral Immunity ◽  
Protein Protein Interactions ◽  
Affinity Tag ◽  
Intrinsic Polarization ◽  
Protein Protein Interaction ◽  
Human Ubiquitin ◽  
Interaction Map

SUMMARYAlthough macrophages are armed with potent anti-bacterial functions, Mycobacterium tuberculosis (Mtb) replicates inside these innate immune cells. Determinants of macrophage-intrinsic bacterial control, and the Mtb strategies to overcome them are poorly understood. To further study these processes, we used a systematic affinity tag purification mass spectrometry (AP-MS) approach to identify 187 Mtb-human protein-protein interactions (PPIs) involving 34 secreted Mtb proteins. This interaction map revealed two new factors involved in Mtb pathogenesis - the secreted Mtb protein, LpqN, and its binding partner, the human ubiquitin ligase CBL. We discovered that an lpqN Mtb mutant is attenuated in macrophages, but growth is restored when CBL is removed. Conversely, Cbl-/- macrophages are resistant to viral infection, indicating that CBL regulates cell-intrinsic polarization between anti-bacterial and anti-viral immunity. Collectively, these findings illustrate the utility of this Mtb-human PPI map as a resource for developing a deeper understanding of the intricate interactions between Mtb and its host.

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

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.

Profiling Molecular Simulations of SARS-CoV-2 Main Protease (Mpro) Binding to Repurposed Drugs Using Neural Network Force Fields

2020 ◽  
Author(s):  
Aayush Gupta
Keyword(s):  
Neural Network ◽  
Protein Interactions ◽  
Force Fields ◽  
Md Simulations ◽  
Interaction Sites ◽  
Main Protease ◽  
Rational Drug ◽  
Interaction Map ◽  
Novel Coronavirus ◽  
Approved Drugs

<div> <p> </p><div> <div> <div> <p> </p><div> <div> <div> <p> </p><div> <div> <div> <p>With the current pandemic situation caused by a novel coronavirus disease (COVID-19), there is an urgent call to develop a working therapeutic against it. Efficient computations aid to minimize the efforts by identifying a subset of drugs that can potentially bind to COVID-19 main protease or target protein (M<sup>PRO</sup>). The results of computations are always accompanied by their accuracy which depends on the details described by the model used. Machine learning models trained on millions of points and with unmatched accuracies are the best bet to employ in the process. In this work, I first identified and described the interaction sites of M<sup>PRO</sup> protein using a geometric deep learning model. Secondly, I conducted virtual screening (at one of the sites identified) on FDA approved drugs and picked 91 drugs having the highest binding affinity (below -8.0 kcal/mol). Then, I carried out 10 ns of molecular dynamics (MD) simulations using classical force fields and classified 37 drugs to be binding (includes drugs like Lopinavir, Saquinavir, Indinavir etc.) based on RMSD between MD-binding trajectories. To drastically improve the dynamics profile of selected 37 drugs, I brought in the highly accurate neural network force field (ANI) trained on coupled-cluster methods (CCSD(T)) data points and performed 1 ns of binding dynamics of each drug with protein. With the accurate approach, 19 drugs were qualified based on their RMSD cutoffs, and again with their free energy (ANI/MM/PBSA) computations another 7 drugs were rejected. The final selection of 12 drugs was validated based on MD trajectory clustering approach where 11 of 12 drugs (Targretin, Eltrombopag, Rifaximin, Deflazacort, Ergotamine, Doxazosin, Lastacaft, Rifampicin, Victrelis, Trajenta, Toposar, Indinavir) were confirmed to be binding. Further investigations were made to study their interactions with the protein and an accurate 2D- interaction map was generated. These findings and mapping of drug-protein interactions are highly accurate and could be potentially used to guide rational drug discovery against the COVID-19. </p> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>

scholarly journals Multiscale statistical physics of the Human-SARS-CoV-2 interactome

2020 ◽  
Author(s):  
Arsham Ghavasieh ◽  
Sebastiano Bontorin ◽  
Oriol Artime ◽  
Manlio De Domenico
Keyword(s):  
Influenza A ◽  
Statistical Physics ◽  
Multiple Scales ◽  
Drug Repurposing ◽  
The Novel ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Human Proteins ◽  
Novel Coronavirus ◽  
Distinguishing Features

Protein-protein interaction (PPI) networks have been used to investigate the influence of SARS-CoV-2 viral proteins on the function of human cells, laying out a deeper understanding of COVID--19 and providing ground for drug repurposing strategies. However, our knowledge of (dis)similarities between this one and other viral agents is still very limited. Here we compare the novel coronavirus PPI network against 45 known viruses, from the perspective of statistical physics. Our results show that classic analysis such as percolation is not sensitive to the distinguishing features of viruses, whereas the analysis of biochemical spreading patterns allows us to meaningfully categorize the viruses and quantitatively compare their impact on human proteins. Remarkably, when Gibbsian-like density matrices are used to represent each system's state, the corresponding macroscopic statistical properties measured by the spectral entropy reveals the existence of clusters of viruses at multiple scales. Overall, our results indicate that SARS-CoV-2 exhibits similarities to viruses like SARS-CoV and Influenza A at small scales, while at larger scales it exhibits more similarities to viruses such as HIV1 and HTLV1.

scholarly journals Computational identification of human biological processes and protein sequence motifs putatively targeted by SARS-CoV-2 proteins using protein-protein interaction networks

2020 ◽  
Author(s):  
Rachel Nadeau ◽  
Soroush Shahryari Fard ◽  
Amit Scheer ◽  
Emily Roth ◽  
Dallas Nygard ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Sequence ◽  
Drug Targets ◽  
Motif Discovery ◽  
Hek293 Cells ◽  
Sequence Motif ◽  
Sequence Motifs ◽  
Protein Protein Interaction ◽  
Human Proteins ◽  
Protein Sequence Motifs

AbstractWhile the COVID-19 pandemic is causing important loss of life, knowledge of the effects of the causative SARS-CoV-2 virus on human cells is currently limited. Investigating protein-protein interactions (PPIs) between viral and host proteins can provide a better understanding of the mechanisms exploited by the virus and enable the identification of potential drug targets. We therefore performed an in-depth computational analysis of the interactome of SARS-CoV-2 and human proteins in infected HEK293 cells published by Gordon et al. to reveal processes that are potentially affected by the virus and putative protein binding sites. Specifically, we performed a set of network-based functional and sequence motif enrichment analyses on SARS-CoV-2-interacting human proteins and on a PPI network generated by supplementing viral-host PPIs with known interactions. Using a novel implementation of our GoNet algorithm, we identified 329 Gene Ontology terms for which the SARS-CoV-2-interacting human proteins are significantly clustered in the network. Furthermore, we present a novel protein sequence motif discovery approach, LESMoN-Pro, that identified 9 amino acid motifs for which the associated proteins are clustered in the network. Together, these results provide insights into the processes and sequence motifs that are putatively implicated in SARS-CoV-2 infection and could lead to potential therapeutic targets.

scholarly journals Discovery of Anti-2019-nCoV Agents from 38 Chinese Patent Drugs toward Respiratory Diseases via Docking Screening

2020 ◽  
Author(s):  
Yong-Ming Yan ◽  
Xin Shen ◽  
Yong-Kai Cao ◽  
Jiao-Jiao Zhang ◽  
Yan Wang ◽  
...  
Keyword(s):  
Chlorogenic Acid ◽  
Drug Targets ◽  
Respiratory Diseases ◽  
Drug Repurposing ◽  
Botanical Knowledge ◽  
Main Protease ◽  
Common Strategy ◽  
Chinese Patent ◽  
Novel Coronavirus ◽  
Approved Drugs

The 2019 novel coronavirus (2019-nCoV) causes novel coronavirus pneumonia (NCP). Given that approved drug repurposing becomes a common strategy to quickly find antiviral treatments, a collection of FDA-approved drugs can be powerful resources for new anti-NCP indication discoveries. In addition to synthetic compounds, Chinese Patent Drugs (CPD), also play a key role in the treatment of virus related infections diseases in China. Here we compiled major components from 38 CPDs that are commonly used in the respiratory diseases and docked them against two drug targets, ACE2 receptor and viral main protease. According to our docking screening, 10 antiviral components, including hesperidin, saikosaponin A, rutin, corosolic acid, verbascoside, baicalin, glycyrrhizin, mulberroside A, cynaroside, and bilirubin, can directly bind to both host cell target ACE2 receptor and viral target main protease. In combination of the docking results, the natural abundance of the substances, and botanical knowledge, we proposed that artemisinin, rutin, glycyrrhizin, cholic acid, hyodeoxycholic acid, puerarin, oleanic acid, andrographolide, matrine, codeine, morphine, chlorogenic acid, and baicalin (or Yinhuang Injection containing chlorogenic acid and baicalin) might be of value for clinical trials during a 2019-nCov outbreak.

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