scholarly journals A Dual Controllability Analysis of Influenza Virus-Host Protein-Protein Interaction Networks for Antiviral Drug Target Discovery

2018 ◽  
Author(s):  
Emily E. Ackerman ◽  
John F. Alcorn ◽  
Takeshi Hase ◽  
Jason E. Shoemaker
Keyword(s):  
Influenza Virus ◽  
Protein Interaction ◽  
Drug Target ◽  
Influenza A ◽  
Antiviral Drug ◽  
Host Factors ◽  
Host Protein ◽  
Drug Target Discovery ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks

ABSTRACTHost factors of influenza virus replication are often found in key topological positions within protein-protein interaction networks. This work explores how protein states can be manipulated through controllability analysis: the determination of the minimum manipulation needed to drive the cell system to any desired state. Here, we complete a two-part controllability analysis of two protein networks: a host network representing the healthy cell state and an influenza A virus-host network representing the infected cell state. This knowledge can be utilized to understand disease dynamics and isolate proteins for study as drug target candidates. Both topological and controllability analyses provide evidence of wide-reaching network effects stemming from the addition of viral-host protein interactions. Virus interacting and driver host proteins are significant both topologically and in controllability, therefore playing important roles in cell behavior during infection. 24 proteins are identified as holding regulatory roles specific to the infected cell by measures of topology, controllability, and functional role. These proteins are recommended for further study as potential antiviral drug targets.ImportanceSeasonal outbreaks of influenza A virus are a major cause of illness and death around the world each year, with a constant threat of pandemic infection. Even so, the FDA has only approved four treatments, two of which are unsuited for at risk groups such as children and those with breathing complications. This research aims to increase the efficiency of antiviral drug target discovery using existing protein-protein interaction data and network analysis methods. Controllability analyses identify key regulating host factors of the infected cell’s progression, findings which are supported by biological context. These results are beneficial to future studies of influenza virus, both experimental and computational.

scholarly journals Host Protein Moloney Leukemia Virus 10 (MOV10) Acts as a Restriction Factor of Influenza A Virus by Inhibiting the Nuclear Import of the Viral Nucleoprotein

2016 ◽  
Vol 90 (8) ◽  
pp. 3966-3980 ◽  
Author(s):  
Junsong Zhang ◽  
Feng Huang ◽  
Likai Tan ◽  
Chuan Bai ◽  
Bing Chen ◽  
...  
Keyword(s):  
Influenza Virus ◽  
Influenza A ◽  
Leukemia Virus ◽  
Polymerase Activity ◽  
Influenza Viruses ◽  
Host Factors ◽  
Host Protein ◽  
Restriction Factor ◽  
Moloney Leukemia Virus ◽  
Importin Α

ABSTRACTThe viral ribonucleoprotein (vRNP) complex of influenza A viruses (IAVs) contains an RNA-dependent RNA polymerase complex (RdRp) and nucleoprotein (NP) and is the functional unit for viral RNA transcription and replication. The vRNP complex is an important determinant of virus pathogenicity and host adaptation, implying that its function can be affected by host factors. In our study, we identified host protein Moloney leukemia virus 10 (MOV10) as an inhibitor of IAV replication, since depletion of MOV10 resulted in a significant increase in virus yield. MOV10 inhibited the polymerase activity in a minigenome system through RNA-mediated interaction with the NP subunit of vRNP complex. Importantly, we found that the interaction between MOV10 and NP prevented the binding of NP to importin-α, resulting in the retention of NP in the cytoplasm. Both the binding of MOV10 to NP and its inhibitory effect on polymerase activity were independent of its helicase activity. These results suggest that MOV10 acts as an anti-influenza virus factor through specifically inhibiting the nuclear transportation of NP and subsequently inhibiting the function of the vRNP complex.IMPORTANCEThe interaction between the influenza virus vRNP complex and host factors is a major determinant of viral tropism and pathogenicity. Our study identified MOV10 as a novel host restriction factor for the influenza virus life cycle since it inhibited the viral growth rate. Conversely, importin-α has been shown as a determinant for influenza tropism and a positive regulator for viral polymerase activity in mammalian cells but not in avian cells. MOV10 disrupted the interaction between NP and importin-α, suggesting that MOV10 could also be an important host factor for influenza virus transmission and pathogenicity. Importantly, as an interferon (IFN)-inducible protein, MOV10 exerted a novel mechanism for IFNs to inhibit the replication of influenza viruses. Furthermore, our study potentially provides a new drug design strategy, the use of molecules that mimic the antiviral mechanism of MOV10.

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 Alignment of virus-host protein-protein interaction networks by integer linear programming: SARS-CoV-2

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0236304
Author(s):  
Mercè Llabrés ◽  
Gabriel Valiente
Keyword(s):  
Linear Programming ◽  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Host Protein ◽  
Host Interactions ◽  
Protein Protein Interaction ◽  
Human Proteins ◽  
Consensus Alignment ◽  
Protein Protein Interaction Networks

Motivation Beside socio-economic issues, coronavirus pandemic COVID-19, the infectious disease caused by the newly discovered coronavirus SARS-CoV-2, has caused a deep impact in the scientific community, that has considerably increased its effort to discover the infection strategies of the new virus. Among the extensive and crucial research that has been carried out in the last months, the analysis of the virus-host relationship plays an important role in drug discovery. Virus-host protein-protein interactions are the active agents in virus replication, and the analysis of virus-host protein-protein interaction networks is fundamental to the study of the virus-host relationship. Results We have adapted and implemented a recent integer linear programming model for protein-protein interaction network alignment to virus-host networks, and obtained a consensus alignment of the SARS-CoV-1 and SARS-CoV-2 virus-host protein-protein interaction networks. Despite the lack of shared human proteins in these virus-host networks, and the low number of preserved virus-host interactions, the consensus alignment revealed aligned human proteins that share a function related to viral infection, as well as human proteins of high functional similarity that interact with SARS-CoV-1 and SARS-CoV-2 proteins, whose alignment would preserve these virus-host interactions.

scholarly journals Alignment of biological networks by integer linear programming: virus-host protein-protein interaction networks

2020 ◽  
Vol 21 (S6) ◽  
Author(s):  
Mercè Llabrés ◽  
Gabriel Riera ◽  
Francesc Rosselló ◽  
Gabriel Valiente
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Protein Interaction ◽  
Software Tools ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Host Protein ◽  
Protein Protein Interaction ◽  
Programming Software ◽  
Protein Protein Interaction Networks

Abstract Background The alignment of protein-protein interaction networks was recently formulated as an integer quadratic programming problem, along with a linearization that can be solved by integer linear programming software tools. However, the resulting integer linear program has a huge number of variables and constraints, rendering it of no practical use. Results We present a compact integer linear programming reformulation of the protein-protein interaction network alignment problem, which can be solved using state-of-the-art mathematical modeling and integer linear programming software tools, along with empirical results showing that small biological networks, such as virus-host protein-protein interaction networks, can be aligned in a reasonable amount of time on a personal computer and the resulting alignments are structurally coherent and biologically meaningful. Conclusions The implementation of the integer linear programming reformulation using current mathematical modeling and integer linear programming software tools provided biologically meaningful alignments of virus-host protein-protein interaction networks.

scholarly journals A Machine Learning Framework for Predicting Drug-drug Interactions

2021 ◽  
Author(s):  
Suyu Mei ◽  
Kun Zhang
Keyword(s):  
Drug Interactions ◽  
Signaling Pathways ◽  
Protein Interaction ◽  
Drug Target ◽  
Target Genes ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Model Complexity ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks

Abstract Understanding drug-drug interaction is an essential step to reduce the risk of adverse drug events before clinical drug co-prescription. Existing methods commonly integrate multiple heterogeneous data sources to increase model performance but result in a high model complexity. To elucidate the molecular mechanisms behind drug-drug interactions and reserve rational biological interpretability is a major concern in computational modeling. In this study, we propose a simple representation of drug target profiles to depict drug pairs, based on which an l2-regularized logistic regression model is built to predict drug-drug interactions. In addition, we develop several statistical metrics to measure the communication intensity, interaction efficacy and action range between two drugs in the context of human protein-protein interaction networks and signaling pathways. Cross validation and independent test show that the simple feature representation via drug target profiles is effective to predict drug-drug interactions and outperforms the existing data integration methods. Statistical results show that two drugs easily interact when they target common genes, or their target genes communicate with each other via short paths in protein-protein interaction networks or through cross-talks between signaling pathways. The unravelled mechanisms provide biological insights into potential pharmacological risks of known drug-drug interactions and drug target genes.

scholarly journals Alignment of virus-host protein-protein interaction networks by integer linear programming: SARS-CoV-2

2020 ◽  
Author(s):  
Mercè Llabrés ◽  
Gabriel Valiente
Keyword(s):  
Linear Programming ◽  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Host Protein ◽  
Host Interactions ◽  
Protein Protein Interaction ◽  
Human Proteins ◽  
Consensus Alignment ◽  
Protein Protein Interaction Networks

AbstractBeside socio-economic issues, coronavirus pandemic COVID-19, the infectious disease caused by the newly discovered coronavirus SARS-CoV-2, has caused a deep impact in the scientific community, that has considerably increased its effort to discover the infection strategies of the new virus. Among the extensive and crucial research that has been carried out in the last few months, the analysis of the virus-host relationship plays an important role in drug discovery. Virus-host protein-protein interactions are the active agents in virus replication, and the analysis of virus-host protein-protein interaction networks is fundamental to the study of the virus-host relationship. We have adapted and implemented a recent integer linear programming model for protein-protein interaction network alignment to virus-host networks, and obtained a consensus alignment of the SARS-CoV-1 and SARS-CoV-2 virus-host protein-protein interaction networks. Despite the lack of shared human proteins in these virus-host networks and the low number of preserved virus-host interactions, the consensus alignment revealed aligned human proteins that share a function related to viral infection, as well as human proteins of high functional similarity that interact with SARS-CoV-1 and SARS-CoV-2 proteins, whose alignment would preserve these virus-host interactions.

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