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Related Concept Videos

Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Predators consume prey for energy. Predators that acquire prey and prey that avoid predation both increase their chances of survival and reproduction (i.e., fitness). Routine predator-prey interactions elicit mutual adaptations that improve predator offenses, such as claws, teeth, and speed, as well as prey defenses, including crypsis, aposematism, and mimicry. Thus, predator-prey interactions resemble an evolutionary arms race.
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PIWI-interacting RNAs, or piRNAs, are the most abundant short non-coding RNAs. More than 20,000 genes have been found in humans that code for piRNAs while only 2000 genes have been found for miRNAs. piRNAs can act at the transcriptional and post-transcriptional levels and have a vital role in silencing transposable elements present in germ cells. They are also involved in epigenetic silencing and activation. Previously, they were thought to function only in germ cells but new evidence suggests...
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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
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Predicting synthetic lethal interactions using conserved patterns in protein interaction networks.

Graeme Benstead-Hume1, Xiangrong Chen1, Suzanna R Hopkins2

  • 1Bioinformatics Lab, School of Life Sciences, University of Sussex, Falmer, Brighton, United Kingdom.

Plos Computational Biology
|April 18, 2019
PubMed
Summary
This summary is machine-generated.

A new computational method, SLant (Synthetic Lethal analysis via Network topology), predicts human synthetic lethal interactions. This approach aids in developing targeted cancer therapies for personalized medicine by identifying potential drug targets.

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Area of Science:

  • Computational biology
  • Systems biology
  • Genomics

Background:

  • Targeted cancer therapies are advancing personalized medicine.
  • Synthetic lethal interactions offer a strategy for treating cancers with inactivated tumor suppressors.
  • Identifying human synthetic lethal interactions is challenging due to experimental limitations.

Purpose of the Study:

  • To develop a computational method for predicting human synthetic lethal interactions.
  • To improve the identification of potential targets for novel cancer therapies.

Main Methods:

  • Developed SLant (Synthetic Lethal analysis via Network topology), a computational systems approach.
  • Analyzed conserved patterns in protein interaction network topology within and across species.
  • Validated predictions through experimental methods.

Main Results:

  • SLant outperforms previous methods in classifying human synthetic lethal interactions.
  • Experimental validation supports the predictive power of SLant.
  • The method successfully identified novel synthetic lethal interactions.

Conclusions:

  • SLant provides a valuable computational tool for predicting human synthetic lethal interactions.
  • This approach can guide future screening efforts for synthetic lethal interactions.
  • SLant may accelerate the development of targeted cancer therapies.