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 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 Genetically informed precision drug repurposing for lung function and implications for respiratory infection

2020 ◽  
Author(s):  
William R. Reay ◽  
Sahar I. El Shair ◽  
Michael P. Geaghan ◽  
Carlos Riveros ◽  
Elizabeth G. Holliday ◽  
...  
Keyword(s):  
Lung Function ◽  
Drug Targets ◽  
Causal Effect ◽  
Association Studies ◽  
Drug Repurposing ◽  
Human Adenovirus ◽  
Mendelian Randomisation ◽  
Polygenic Scores ◽  
Impaired Lung Function ◽  
Human Proteins

ABSTRACTImpaired lung function is associated with significant morbidity and mortality. Restrictive and obstructive lung disorders are a large contributor to decreased lung function, as well as the acute impact of infection. Measures of pulmonary function are heritable, and thus, we sought to utilise genomics to propose novel drug repurposing candidates which could improve respiratory outcomes. Lung function measures were found to be genetically correlated with metabolic and hormone traits which could be pharmacologically modulated, with a causal effect of increased fasting glucose on diminished lung function supported by latent causal variable models and Mendelian randomisation. We developed polygenic scores for lung function specifically within pathways with known drug targets to prioritise individuals who may benefit from particular drug repurposing opportunities, accompanied by transcriptome-wide association studies to identify drug-gene interactions with potential lung function increasing modes of action. These drug repurposing candidates were further considered relative to the host-viral interactome of three viruses with associated respiratory pathology (SARS-CoV2, influenza, and human adenovirus). We uncovered an enrichment amongst glycaemic pathways of human proteins which putatively interact with virally expressed SARS-CoV2 proteins, suggesting that antihyperglycaemic agents may have a positive effect both on lung function and SARS-CoV2 progression.

SARS-CoV-2 3CLpro whole human proteome cleavage prediction and enrichment/depletion analysis

2020 ◽  
Author(s):  
Lucas Prescott
Keyword(s):  
Drug Targets ◽  
Human Proteome ◽  
Host Protein ◽  
Nuclear Condensation ◽  
Viral Proteases ◽  
Nascent Chain ◽  
Human Proteins ◽  
Immune Tissues ◽  
Cellular Pathways ◽  
Novel Coronavirus

AbstractA novel coronavirus (SARS-CoV-2) has devastated the globe as a pandemic that has killed more than 1,600,000 people. Widespread vaccination is still uncertain, so many scientific efforts have been directed toward discovering antiviral treatments. Many drugs are being investigated to inhibit the coronavirus main protease, 3CLpro, from cleaving its viral polyprotein, but few publications have addressed this protease’s interactions with the host proteome or their probable contribution to virulence. Too few host protein cleavages have been experimentally verified to fully understand 3CLpro’s global effects on relevant cellular pathways and tissues. Here, I set out to determine this protease’s targets and corresponding potential drug targets. Using a neural network trained on cleavages from 388 coronavirus proteomes with a Matthews correlation coefficient of 0.983, I predict that a large proportion of the human proteome is vulnerable to 3CLpro, with 4,460 out of approximately 20,000 human proteins containing at least one putative cleavage site. These cleavages are nonrandomly distributed and are enriched in the epithelium along the respiratory tract, brain, testis, plasma, and immune tissues and depleted in olfactory and gustatory receptors despite the prevalence of anosmia and ageusia in COVID-19 patients. Affected cellular pathways include cytoskeleton/motor/cell adhesion proteins, nuclear condensation and other epigenetics, host transcription and RNAi, ribosomal stoichiometry and nascent-chain detection and degradation, coagulation, pattern recognition receptors, growth factors, lipoproteins, redox, ubiquitination, and apoptosis. This whole proteome cleavage prediction demonstrates the importance of 3CLpro in expected and nontrivial pathways affecting virulence, lead me to propose more than a dozen potential therapeutic targets against coronaviruses, and should therefore be applied to all viral proteases and subsequently experimentally verified.

scholarly journals Finding the “switch” in platelet activation Prediction of key mediators involved in platelet hyperreactivity using a novel network biology approach

2021 ◽  
Author(s):  
TP Lemmens ◽  
DM Coenen ◽  
ICL Niessen ◽  
F Swieringa ◽  
SLM Coort ◽  
...  
Keyword(s):  
Platelet Activation ◽  
Molecular Mechanisms ◽  
Shape Change ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Adenosine Monophosphate ◽  
Adenosine Diphosphate ◽  
Network Biology ◽  
Small Gtpases ◽  
Platelet Activity

Abstract The healthy endothelium controls platelet activity through release of prostaglandin I2 (PGI2) and nitric oxide. The loss of this natural brake on platelet activity can cause platelets to become hyperreactive. PGI2 attenuates platelet activation by adenosine diphosphate (ADP) through stimulation of cyclic adenosine monophosphate (cAMP) production and subsequent phosphorylation changes by protein kinase A (PKA). We hypothesize that proteins/processes involved in platelet hyperactivity downstream of the cAMP-PKA pathway can serve as a “switch” in platelet activation and inhibition. We designed a network biology approach to explore the entangled platelet signaling pathways downstream of PGI2 and ADP. The STRING database was used to build a protein-protein interaction network from proteins of interest in which we integrate a quantitative platelet proteome dataset with pathway information, relative RNA expression of hematopoietic cells, the likelihood of the proteins being phosphorylated by PKA, and drug-target information from DrugBank in a biological network. We distilled 30 proteins from existing phosphoproteomics datasets (PXD000242 and PXD001189) that putatively can be “turned on” after ADP-mediated platelet activation and subsequently switched “off” after platelet inhibition with iloprost. Enrichment analysis revealed biological processes related to vesicle secretion and cytoskeletal reorganization to be overrepresented coinciding with topological clusters in the network. Our method highlights novel proteins related to vesicle transport, platelet shape change, and small GTPases as potential switch proteins in platelet activation and inhibition. Our novel approach demonstrates the benefit of data integration by combining tools and datasets and visualization to obtain a more complete picture of complex molecular mechanisms.

scholarly journals DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug–target interactions

2021 ◽  
Author(s):  
Tilman Hinnerichs ◽  
Robert Hoehndorf
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Interaction Network ◽  
Drug Repurposing ◽  
Computational Method ◽  
Interaction Networks ◽  
Supplementary Information ◽  
Prediction Methods ◽  
Link Type ◽  
Molecular Features

AbstractMotivationIn silico drug–target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can proceed indirectly, top-down, using phenotypic effects of drugs to identify potential drug targets, or they can be direct, bottom-up and use molecular information to directly predict binding potentials. Both approaches can be combined with information about interaction networks.ResultsWe developed DTI-Voodoo as a computational method that combines molecular features and ontology-encoded phenotypic effects of drugs with protein–protein interaction networks, and uses a graph convolutional neural network to predict DTIs. We demonstrate that drug effect features can exploit information in the interaction network whereas molecular features do not. DTI-Voodoo is designed to predict candidate drugs for a given protein; we use this formulation to show that common DTI datasets contain intrinsic biases with major affects on performance evaluation and comparison of DTI prediction methods. Using a modified evaluation scheme, we demonstrate that DTI-Voodoo improves significantly over state of the art DTI prediction methods.AvailabilityDTI-Voodoo source code and data necessary to reproduce results are freely available at https://github.com/THinnerichs/DTI-VOODOO.Supplementary informationSupplementary data are available at https://github.com/ THinnerichs/DTI-VOODOO.

scholarly journals Network controllability solutions for computational drug repurposing using genetic algorithms

2021 ◽  
Author(s):  
Victor-Bogdan Popescu ◽  
Krishna Kanhaiya ◽  
Iulian Nastac ◽  
Eugen Czeizler ◽  
Ion Petre
Keyword(s):  
Genetic Algorithms ◽  
Drug Targets ◽  
Interaction Network ◽  
Drug Repurposing ◽  
Small World ◽  
Specific Protein ◽  
Scale Free ◽  
Protein Protein Interaction ◽  
External Interventions ◽  
Network Controllability

Abstract Control theory has seen recently impactful applications in network science, especially in connections with applications in network medicine. A key topic of research is that of finding minimal external interventions that offer control over the dynamics of a given network, a problem known as network controllability. We propose in this article a new solution for this problem based on genetic algorithms. We tailor our solution for applications in computational drug repurposing, seeking to maximize its use of FDA-approved drug targets in a given disease-specific protein-protein interaction network. We show how our algorithm identifies a number of potentially efficient drugs for breast, ovarian, and pancreatic cancer. We demonstrate our algorithm on several benchmark networks from cancer medicine, social networks, electronic circuits, and several random networks with their edges distributed according to the Erdös-Rényi, the scale-free, and the small world properties. Overall, we show that our new algorithm is more efficient in identifying relevant drug targets in a disease network, advancing the computational solutions needed for new therapeutic and drug repurposing approaches.

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.

Identification of Essential Proteins by Detecting Topological and Functional Clusters in Protein Interaction Network of Saccharomyces Cerevisiae

2019 ◽  
Vol 8 (1) ◽  
pp. 31-51
Author(s):  
Kaustav Sengupta ◽  
Sovan Saha ◽  
Piyali Chatterjee ◽  
Mahantapas Kundu ◽  
Mita Nasipuri ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Functional Annotation ◽  
Drug Targets ◽  
Protein Identification ◽  
State Of The Art ◽  
Interaction Network ◽  
Centrality Measures ◽  
Essential Proteins ◽  
Go Terms ◽  
Hub Proteins

Essential protein identification is an important factor to inspect the mechanisms of disease progression and to identify drug targets. With the advancement of high throughput genome sequencing projects, a bulk of protein data is available where the analysis of interaction pattern, functional annotation and characterization are necessary for detecting proteins' essentiality in network level. A set of centrality measure has been used to identify the highly connected proteins or hubs. From recent studies, it is observed that the majority of hubs are considered to be essential proteins. In this article, a method EPIN_Pred is proposed where a combination of several centrality measures is used to find the hub and non-hub proteins. Using the cohesiveness property, overlapping topological clusters are found. Using gene ontology (GO) terms, these topological clusters are again combined, if required. The performance of EPIN_Pred is also found to be superior when compared to other state-of-the-art methods.

scholarly journals Identification of disease treatment mechanisms through the multiscale interactome

2020 ◽  
Author(s):  
Camilo Ruiz ◽  
Marinka Zitnik ◽  
Jure Leskovec
Keyword(s):  
Drug Targets ◽  
Drug Effects ◽  
Interaction Network ◽  
Drug Efficacy ◽  
Therapeutic Effects ◽  
Biological Functions ◽  
Disease Treatment ◽  
Protein Protein Interaction ◽  
Associated Proteins ◽  
Human Proteins

Most diseases disrupt multiple proteins, and drugs treat such diseases by restoring the functions of the disrupted proteins. How drugs restore these functions, however, is often unknown as a drug’s therapeutic effects are not limited only to the proteins that the drug directly targets. Here, we develop the multiscale interactome, a powerful approach to explain disease treatment. We integrate disease-perturbed proteins, drug targets, and biological functions into a multiscale interactome network, which contains 478,728 interactions between 1,661 drugs, 840 diseases, 17,660 human proteins, and 9,798 biological functions. We find that a drug’s effectiveness can often be attributed to targeting proteins that are distinct from disease-associated proteins but that affect the same biological functions. We develop a random walk-based method that captures how drug effects propagate through a hierarchy of biological functions and are coordinated by the protein-protein interaction network in which drugs act. On three key pharmacological tasks, we find that the multiscale interactome predicts what drugs will treat a given disease more effectively than prior approaches, identifies proteins and biological functions related to treatment, and predicts genes that interfere with treatment to alter drug efficacy and cause serious adverse reactions. Our results indicate that physical interactions between proteins alone are unable to explain the therapeutic effects of drugs as many drugs treat diseases by affecting the same biological functions disrupted by the disease rather than directly targeting disease proteins or their regulators. We provide a general framework for identifying proteins and biological functions relevant in treatment, even when drugs seem unrelated to the diseases they are recommended for.

scholarly journals COV-Drug Target interaction server for Covid-19 Drug Repurposing

2021 ◽  
Author(s):  
Kamal Rawal ◽  
Prashant Singh ◽  
Robin Sinha ◽  
Priya Kumari ◽  
Swarsat Kaushik Nath ◽  
...  
Keyword(s):  
Drug Targets ◽  
Cathepsin L ◽  
Drug Repurposing ◽  
New Drugs ◽  
Pore Channel ◽  
Target Interaction ◽  
Viral Virulence ◽  
Cyclin G ◽  
Novel Coronavirus ◽  
Transmembrane Serine Protease

The outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has killed over 5 million people to date. So, there is an urgent requirement for new and effective medications that can treat the disease caused by SARS-CoV-2. To find new drugs, identification of drug targets is necessary (Chen et al., 2016). Number of research studies have identified therapeutic targets such as helicases, transmembrane serine protease 2, cathepsin L, cyclin G-associated kinase, adaptor associated kinase 1, two-pore channel, viral virulence factors, 3-chymotrypsin-like protease, suppression of excessive inflammatory response, inhibition of viral membrane, nucleocapsid, envelope, and accessory proteins, and inhibition of endocytosis. Here we present a web enabled tool which helps in ranking the COVID-19 drugs based upon underlying molecular targets. The users are allowed to give drugs in SMILE format and the tools will provide the list of relevant targets related to COVID-19.

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