A dual controllability analysis of influenza virus-host protein-protein interaction networks for antiviral drug target discovery

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.

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

PLoS ONE ◽

10.1371/journal.pone.0236304 ◽

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.

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

BMC Bioinformatics ◽

10.1186/s12859-020-03733-w ◽

2020 ◽

Vol 21 (S6) ◽

Cited By ~ 1

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.

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A Machine Learning Framework for Predicting Drug-drug Interactions

10.21203/rs.3.rs-503867/v1 ◽

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.

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

10.1101/2020.07.07.191247 ◽

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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