scholarly journals Genetic architecture of host proteins interacting with SARS-CoV-2

2020 ◽  
Author(s):  
Maik Pietzner ◽  
Eleanor Wheeler ◽  
Julia Carrasco-Zanini ◽  
Johannes Raffler ◽  
Nicola D. Kerrison ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Host Response ◽  
Drug Targets ◽  
Genetic Architecture ◽  
Large Scale ◽  
Adverse Outcomes ◽  
Host Protein ◽  
Host Proteins ◽  
Proteomic Data ◽  
Cellular Assays

ABSTRACTStrategies to develop therapeutics for SARS-CoV-2 infection may be informed by experimental identification of viral-host protein interactions in cellular assays and measurement of host response proteins in COVID-19 patients. Identification of genetic variants that influence the level or activity of these proteins in the host could enable rapid ‘in silico’ assessment in human genetic studies of their causal relevance as molecular targets for new or repurposed drugs to treat COVID-19. We integrated large-scale genomic and aptamer-based plasma proteomic data from 10,708 individuals to characterize the genetic architecture of 179 host proteins reported to interact with SARS-CoV-2 proteins or to participate in the host response to COVID-19. We identified 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links, evidence that putative viral interaction partners such as MARK3 affect immune response, and establish the first link between a recently reported variant for respiratory failure of COVID-19 patients at the ABO locus and hypercoagulation, i.e. maladaptive host response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and dynamic and detailed interrogation of results is facilitated through an interactive webserver (https://omicscience.org/apps/covidpgwas/).

scholarly journals Genetic architecture of host proteins involved in SARS-CoV-2 infection

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Maik Pietzner ◽  
Eleanor Wheeler ◽  
Julia Carrasco-Zanini ◽  
Johannes Raffler ◽  
Nicola D. Kerrison ◽  
...  
Keyword(s):  
Host Response ◽  
Drug Targets ◽  
Genetic Architecture ◽  
Adverse Outcomes ◽  
Host Proteins ◽  
Interaction Partners ◽  
Dna Sequence Variants ◽  
New Drug Development ◽  
Genetic Discovery

AbstractUnderstanding the genetic architecture of host proteins interacting with SARS-CoV-2 or mediating the maladaptive host response to COVID-19 can help to identify new or repurpose existing drugs targeting those proteins. We present a genetic discovery study of 179 such host proteins among 10,708 individuals using an aptamer-based technique. We identify 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links and evidence that putative viral interaction partners such as MARK3 affect immune response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and detailed interrogation of results is facilitated through an interactive webserver (https://omicscience.org/apps/covidpgwas/).

scholarly journals Expanding the drug discovery space with predicted metabolite–target interactions

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andrea Nuzzo ◽  
Somdutta Saha ◽  
Ellen Berg ◽  
Channa Jayawickreme ◽  
Joel Tocker ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
Large Scale ◽  
Association Studies ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Host Protein ◽  
Multiple Drug ◽  
Genome Wide Association Studies

AbstractMetabolites produced in the human gut are known modulators of host immunity. However, large-scale identification of metabolite–host receptor interactions remains a daunting challenge. Here, we employed computational approaches to identify 983 potential metabolite–target interactions using the Inflammatory Bowel Disease (IBD) cohort dataset of the Human Microbiome Project 2 (HMP2). Using a consensus of multiple machine learning methods, we ranked metabolites based on importance to IBD, followed by virtual ligand-based screening to identify possible human targets and adding evidence from compound assay, differential gene expression, pathway enrichment, and genome-wide association studies. We confirmed known metabolite–target pairs such as nicotinic acid–GPR109a or linoleoyl ethanolamide–GPR119 and inferred interactions of interest including oleanolic acid–GABRG2 and alpha-CEHC–THRB. Eleven metabolites were tested for bioactivity in vitro using human primary cell-types. By expanding the universe of possible microbial metabolite–host protein interactions, we provide multiple drug targets for potential immune-therapies.

scholarly journals Visualization of Host-Polerovirus Interaction Topologies Using Protein Interaction Reporter Technology

2015 ◽  
Vol 90 (4) ◽  
pp. 1973-1987 ◽  
Author(s):  
Stacy L. DeBlasio ◽  
Juan D. Chavez ◽  
Mariko M. Alexander ◽  
John Ramsey ◽  
Jimmy K. Eng ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Interaction Network ◽  
Host Protein ◽  
Host Proteins ◽  
Content Type ◽  
Host Pathogen ◽  
Pathogen Protein ◽  
Binding Interfaces

ABSTRACTDemonstrating direct interactions between host and virus proteins during infection is a major goal and challenge for the field of virology. Most protein interactions are not binary or easily amenable to structural determination. Using infectious preparations of a polerovirus (Potato leafroll virus[PLRV]) and protein interaction reporter (PIR), a revolutionary technology that couples a mass spectrometric-cleavable chemical cross-linker with high-resolution mass spectrometry, we provide the first report of a host-pathogen protein interaction network that includes data-derived, topological features for every cross-linked site that was identified. We show that PLRV virions have hot spots of protein interaction and multifunctional surface topologies, revealing how these plant viruses maximize their use of binding interfaces. Modeling data, guided by cross-linking constraints, suggest asymmetric packing of the major capsid protein in the virion, which supports previous epitope mapping studies. Protein interaction topologies are conserved with other species in theLuteoviridaeand with unrelated viruses in theHerpesviridaeandAdenoviridae. Functional analysis of three PLRV-interacting host proteinsin plantausing a reverse-genetics approach revealed a complex, molecular tug-of-war between host and virus. Structural mimicry and diversifying selection—hallmarks of host-pathogen interactions—were identified within host and viral binding interfaces predicted by our models. These results illuminate the functional diversity of the PLRV-host protein interaction network and demonstrate the usefulness of PIR technology for precision mapping of functional host-pathogen protein interaction topologies.IMPORTANCEThe exterior shape of a plant virus and its interacting host and insect vector proteins determine whether a virus will be transmitted by an insect or infect a specific host. Gaining this information is difficult and requires years of experimentation. We used protein interaction reporter (PIR) technology to illustrate how viruses exploit host proteins during plant infection. PIR technology enabled our team to precisely describe the sites of functional virus-virus, virus-host, and host-host protein interactions using a mass spectrometry analysis that takes just a few hours. Applications of PIR technology in host-pathogen interactions will enable researchers studying recalcitrant pathogens, such as animal pathogens where host proteins are incorporated directly into the infectious agents, to investigate how proteins interact during infection and transmission as well as develop new tools for interdiction and therapy.

scholarly journals A map of direct SARS-CoV-2 protein interactions implicates specific human host processes

2021 ◽  
Author(s):  
Dae-Kyum Kim ◽  
Benjamin Weller ◽  
Chung-Wen Lin ◽  
Dayag Sheykhkarimli ◽  
Jennifer J Knapp ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Membrane Trafficking ◽  
Host Protein ◽  
Host Proteins ◽  
Systematic Map ◽  
High Data ◽  
Protein Ubiquitination ◽  
Human Proteins ◽  
Physical Interactions ◽  
Key Steps

Key steps in viral propagation, immune suppression and pathology are mediated by direct, binary physical interactions between viral and host proteins. To understand the biology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, we generated an unbiased systematic map of binary physical interactions between viral and host interactions, complementing previous co-complex association maps by conveying more direct mechanistic understanding and enabling targeted disruption of direct interactions. To this end, we deployed two parallel strategies, identifying 205 virus-host and 27 intraviral binary interactions amongst 171 host and 19 viral proteins, with orthogonal validation by an internally benchmarked NanoLuc two-hybrid system to ensure high data quality. Host proteins interacting with SARS-CoV-2 proteins were enriched in various cellular processes, including immune signaling and inflammation, protein ubiquitination, and membrane trafficking. Specific subnetworks provide new hypotheses related to viral modulation of host protein homeostasis and T-cell regulation. The direct virus-host protein interactions we identified can now be prioritized as targets for therapeutic intervention. More generally, we provide a resource of systematic maps describing which SARS-CoV-2 and human proteins interact directly.

scholarly journals Network-Guided Discovery of Influenza Virus Replication Host Factors

2018 ◽  
Author(s):  
Emily E. Ackerman ◽  
Eiryo Kawakami ◽  
Manami Katoh ◽  
Tokiko Watanabe ◽  
Shinji Watanabe ◽  
...  
Keyword(s):  
Influenza Virus ◽  
Virus Replication ◽  
Drug Targets ◽  
Antiviral Drug ◽  
Viral Proteins ◽  
Host Factors ◽  
Host Protein ◽  
Ppi Network ◽  
Host Proteins ◽  
Influenza Virus Replication

ABSTRACTThe position of host factors required for viral replication within a human protein-protein interaction (PPI) network can be exploited to identify drug targets that are robust to drug-mediated selective pressure. Host factors can physically interact with viral proteins, be a component of pathways regulated by viruses (where proteins themselves do not interact with viral proteins) or be required for viral replication but unregulated by viruses. Here, we demonstrate a method of combining a human PPI network with virus-host protein interaction data to improve antiviral drug discovery for influenza viruses by identifying target host proteins. Network analysis shows that influenza virus proteins physically interact with host proteins in network positions significant for information flow. We have isolated a subnetwork of the human PPI network which connects virus-interacting host proteins to host factors that are important for influenza virus replication without physically interacting with viral proteins. The subnetwork is enriched for signaling and immune processes. Selecting proteins based on network topology within the subnetwork, we performed an siRNA screen to determine if the subnetwork was enriched for virus replication host factors and if network position within the subnetwork offers an advantage in prioritization of drug targets to control influenza virus replication. We found that the subnetwork is highly enriched for target host proteins – more so than the set of host factors that physically interact with viral proteins. Our findings demonstrate that network positions are a powerful predictor to guide antiviral drug candidate prioritization.IMPORTANCEIntegrating virus-host interactions with host protein-protein interactions, we have created a method using these established network practices to identify host factors (i.e. proteins) that are likely candidates for antiviral drug targeting. We demonstrate that interaction cascades between host proteins that directly interact with viral proteins and host factors that are important to influenza replication are enriched for signaling and immune processes. Additionally, we show that host proteins that interact with viral proteins are in network locations of power. Finally, we demonstrate a new network methodology to predict novel host factors and validate predictions with an siRNA screen. Our results show that integrating virus-host proteins interactions is useful in the identification of antiviral drug target candidates.

scholarly journals CROssBAR: Comprehensive Resource of Biomedical Relations with Deep Learning Applications and Knowledge Graph Representations

2020 ◽  
Author(s):  
Tunca Doğan ◽  
Heval Atas ◽  
Vishal Joshi ◽  
Ahmet Atakan ◽  
Ahmet Sureyya Rifaioglu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Large Scale ◽  
Host Protein ◽  
Knowledge Graph ◽  
Biomedical Data ◽  
Data Resource ◽  
Graph Representations ◽  
Link Type ◽  
Systemic Analysis

AbstractSystemic analysis of available large-scale biological and biomedical data is critical for developing novel and effective treatment approaches against both complex and infectious diseases. Owing to the fact that different sections of the biomedical data is produced by different organizations/institutions using various types of technologies, the data are scattered across individual computational resources, without any explicit relations/connections to each other, which greatly hinders the comprehensive multi-omics-based analysis of data. We aimed to address this issue by constructing a new biological and biomedical data resource, CROssBAR, a comprehensive system that integrates large-scale biomedical data from various resources and store them in a new NoSQL database, enrich these data with deep-learning-based prediction of relations between numerous biomedical entities, rigorously analyse the enriched data to obtain biologically meaningful modules and display them to users via easy-to-interpret, interactive and heterogenous knowledge graph (KG) representations within an open access, user-friendly and online web-service at https://crossbar.kansil.org. As a use-case study, we constructed CROssBAR COVID-19 KGs (available at: https://crossbar.kansil.org/covid_main.php) that incorporate relevant virus and host genes/proteins, interactions, pathways, phenotypes and other diseases, as well as known and completely new predicted drugs/compounds. Our COVID-19 graphs can be utilized for a systems-level evaluation of relevant virus-host protein interactions, mechanisms, phenotypic implications and potential interventions.

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

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.

scholarly journals Integrating Genetics and the Plasma Proteome to Predict the Risk of Type 2 Diabetes

2020 ◽  
Vol 20 (11) ◽  
Author(s):  
Julia Carrasco Zanini ◽  
Maik Pietzner ◽  
Claudia Langenberg
Keyword(s):  
Type 2 Diabetes ◽  
Drug Targets ◽  
Information Transfer ◽  
Large Scale ◽  
Biomarker Discovery ◽  
Plasma Proteome ◽  
Protein Biomarkers ◽  
Genetic Studies ◽  
Proteomic Data

Abstract Purpose of the Review Proteins are the central layer of information transfer from genome to phenome and represent the largest class of drug targets. We review recent advances in high-throughput technologies that provide comprehensive, scalable profiling of the plasma proteome with the potential to improve prediction and mechanistic understanding of type 2 diabetes (T2D). Recent Findings Technological and analytical advancements have enabled identification of novel protein biomarkers and signatures that help to address challenges of existing approaches to predict and screen for T2D. Genetic studies have so far revealed putative causal roles for only few of the proteins that have been linked to T2D, but ongoing large-scale genetic studies of the plasma proteome will help to address this and increase our understanding of aetiological pathways and mechanisms leading to diabetes. Summary Studies of the human plasma proteome have started to elucidate its potential for T2D prediction and biomarker discovery. Future studies integrating genomic and proteomic data will provide opportunities to prioritise drug targets and identify pathways linking genetic predisposition to T2D development.

scholarly journals Ultra-High-Throughput Screening of Natural Product Extracts to Identify Proapoptotic Inhibitors of Bcl-2 Family Proteins

2014 ◽  
Vol 19 (8) ◽  
pp. 1201-1211 ◽  
Author(s):  
Christian A. Hassig ◽  
Fu-Yue Zeng ◽  
Paul Kung ◽  
Mehrak Kiankarimi ◽  
Sylvia Kim ◽  
...  
Keyword(s):  
Natural Product ◽  
High Throughput ◽  
Protein Interactions ◽  
High Throughput Screening ◽  
Large Scale ◽  
Active Components ◽  
Cellular Functions ◽  
Cellular Assays ◽  
Bioassay Guided Fractionation ◽  
Luminescent Assay

Antiapoptotic Bcl-2 family proteins are validated cancer targets composed of six related proteins. From a drug discovery perspective, these are challenging targets that exert their cellular functions through protein-protein interactions (PPIs). Although several isoform-selective inhibitors have been developed using structure-based design or high-throughput screening (HTS) of synthetic chemical libraries, no large-scale screen of natural product collections has been reported. A competitive displacement fluorescence polarization (FP) screen of nearly 150,000 natural product extracts was conducted against all six antiapoptotic Bcl-2 family proteins using fluorochrome-conjugated peptide ligands that mimic functionally relevant PPIs. The screens were conducted in 1536-well format and displayed satisfactory overall HTS statistics, with Z′-factor values ranging from 0.72 to 0.83 and a hit confirmation rate between 16% and 64%. Confirmed active extracts were orthogonally tested in a luminescent assay for caspase-3/7 activation in tumor cells. Active extracts were resupplied, and effort toward the isolation of pure active components was initiated through iterative bioassay-guided fractionation. Several previously described altertoxins were isolated from a microbial source, and the pure compounds demonstrate activity in both Bcl-2 FP and caspase cellular assays. The studies demonstrate the feasibility of ultra-high-throughput screening using natural product sources and highlight some of the challenges associated with this approach.

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