Two scenarios for overcoming drug resistance by co-targeting

Removal of proteins on an essential pathway of a pathogen is expected to prohibit the pathogen from performing a vital function. To disrupt these pathways, we consider a cut set S of simple graph G, where G representing the PPI network of the pathogen. After removing S, if the difference of sizes of...

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Published in:International journal of bioinformatics research and applications Vol. 11; no. 1; p. 72
Main Authors: Taheri, Golnaz, Ayati, Marzieh, Wong, Limsoon, Eslahchi, Changiz
Format: Journal Article
Language:English
Published: Switzerland 2015
Subjects:
Anti-Bacterial Agents - pharmacology
Bacterial Proteins - metabolism
Computer Simulation
Drug Design
Drug Resistance, Bacterial - drug effects
Drug Resistance, Bacterial - physiology
Models, Biological
Protein Interaction Mapping - methods
Signal Transduction - drug effects
Signal Transduction - physiology
protein protein interaction
balanced bipartitioning
E coli
pathogens
C jejuni
bioinformatics
PPI networks
drug targets
essential pathways
drug resistance
co–targeting
ISSN:1744-5485
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Abstract Removal of proteins on an essential pathway of a pathogen is expected to prohibit the pathogen from performing a vital function. To disrupt these pathways, we consider a cut set S of simple graph G, where G representing the PPI network of the pathogen. After removing S, if the difference of sizes of two partitions is high, the probability of existence of a functioning pathway is increased. We need to partition the graph into balanced partitions and approximate it with spectral bipartitioning. We consider two scenarios: in the first, we do not have any information on drug targets; in second, we consider information on drug targets. Our databases are E. coli and C. jejuni. In the first scenario, 20% and 17% of proteins in cut of E. coli and C. jejuni are drug targets and in the second scenario 53% and 63% of proteins in cut are drug targets respectively.
AbstractList Removal of proteins on an essential pathway of a pathogen is expected to prohibit the pathogen from performing a vital function. To disrupt these pathways, we consider a cut set S of simple graph G, where G representing the PPI network of the pathogen. After removing S, if the difference of sizes of two partitions is high, the probability of existence of a functioning pathway is increased. We need to partition the graph into balanced partitions and approximate it with spectral bipartitioning. We consider two scenarios: in the first, we do not have any information on drug targets; in second, we consider information on drug targets. Our databases are E. coli and C. jejuni. In the first scenario, 20% and 17% of proteins in cut of E. coli and C. jejuni are drug targets and in the second scenario 53% and 63% of proteins in cut are drug targets respectively.
Removal of proteins on an essential pathway of a pathogen is expected to prohibit the pathogen from performing a vital function. To disrupt these pathways, we consider a cut set S of simple graph G, where G representing the PPI network of the pathogen. After removing S, if the difference of sizes of two partitions is high, the probability of existence of a functioning pathway is increased. We need to partition the graph into balanced partitions and approximate it with spectral bipartitioning. We consider two scenarios: in the first, we do not have any information on drug targets; in second, we consider information on drug targets. Our databases are E. coli and C. jejuni. In the first scenario, 20% and 17% of proteins in cut of E. coli and C. jejuni are drug targets and in the second scenario 53% and 63% of proteins in cut are drug targets respectively.Removal of proteins on an essential pathway of a pathogen is expected to prohibit the pathogen from performing a vital function. To disrupt these pathways, we consider a cut set S of simple graph G, where G representing the PPI network of the pathogen. After removing S, if the difference of sizes of two partitions is high, the probability of existence of a functioning pathway is increased. We need to partition the graph into balanced partitions and approximate it with spectral bipartitioning. We consider two scenarios: in the first, we do not have any information on drug targets; in second, we consider information on drug targets. Our databases are E. coli and C. jejuni. In the first scenario, 20% and 17% of proteins in cut of E. coli and C. jejuni are drug targets and in the second scenario 53% and 63% of proteins in cut are drug targets respectively.
Author Taheri, Golnaz
Ayati, Marzieh
Wong, Limsoon
Eslahchi, Changiz
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  givenname: Golnaz
  surname: Taheri
  fullname: Taheri, Golnaz
  organization: School of Mathematics and Computer Science and Center of Excellence in Bioinformatics, College of Science, University of Tehran, Tehran, Iran; Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
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  givenname: Marzieh
  surname: Ayati
  fullname: Ayati, Marzieh
  organization: Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA
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  givenname: Limsoon
  surname: Wong
  fullname: Wong, Limsoon
  organization: Department of Computer Science, National University of Singapore, Singapore
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  givenname: Changiz
  surname: Eslahchi
  fullname: Eslahchi, Changiz
  organization: Department of Computer Science, Shahid Beheshti University G.C., Tehran, Iran
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Keywords protein protein interaction
balanced bipartitioning
E coli
pathogens
C jejuni
bioinformatics
PPI networks
drug targets
essential pathways
drug resistance
co–targeting
Language English
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Snippet Removal of proteins on an essential pathway of a pathogen is expected to prohibit the pathogen from performing a vital function. To disrupt these pathways, we...
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SubjectTerms Anti-Bacterial Agents - pharmacology
Bacterial Proteins - metabolism
Computer Simulation
Drug Design
Drug Resistance, Bacterial - drug effects
Drug Resistance, Bacterial - physiology
Models, Biological
Protein Interaction Mapping - methods
Signal Transduction - drug effects
Signal Transduction - physiology
Title Two scenarios for overcoming drug resistance by co-targeting
URI https://www.ncbi.nlm.nih.gov/pubmed/25667386
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