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Related Concept Videos

Protein Networks02:26

Protein Networks

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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Viral Recombination00:57

Viral Recombination

Cells are sometimes infected by more than one virus at once. When two viruses disassemble to expose their genomes for replication in the same cell, similar regions of their genomes can pair together and exchange sequences in a process called recombination. Alternatively, viruses with segmented genomes can swap segments in a process called reassortment.
What are Viruses?00:50

What are Viruses?

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Viral Mutations00:36

Viral Mutations

A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material for adaptive...
Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...

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Identification of Functionally-Relevant Lentivirus Integration Sites in an Insertional Mutagenesis Cell Library
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Identification of Functionally-Relevant Lentivirus Integration Sites in an Insertional Mutagenesis Cell Library

Published on: January 10, 2025

Learning virulent proteins from integrated query networks.

Eithon Cadag1, Peter Tarczy-Hornoch, Peter J Myler

  • 1Ayasdi Inc, Palo Alto, CA 94301, USA. ecadag@uw.edu

BMC Bioinformatics
|December 4, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a computational method to identify proteins involved in pathogen virulence. The technique leverages integrated data to predict general and specific virulence functions, offering an alternative to broad-spectrum approaches.

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Published on: July 18, 2013

Area of Science:

  • Computational biology
  • Bioinformatics
  • Pathogen research

Background:

  • Broad-spectrum antimicrobial approaches face limitations.
  • Attenuating pathogen virulence offers an alternative strategy.
  • Identifying virulence factors is crucial for understanding host-pathogen interactions.

Purpose of the Study:

  • To develop a computational technique for identifying proteins associated with pathogen virulence.
  • To apply the method for predicting both general and specific virulence functions.
  • To assess the method's performance against existing techniques.

Main Methods:

  • Utilized a lightweight data integration method linking protein information via path-based query graphs.
  • Applied a weighting method to query graphs for statistical classification.
  • Tested combined data integration and learning methods for virulence function prediction.

Main Results:

  • The approach enhances functional data coverage for proteins.
  • The method successfully predicts generalized virulence functions.
  • It also predicts specific virulence categories effectively.

Conclusions:

  • The developed computational approach improves virulence factor identification.
  • It outperforms existing methods for general virulence factor identification.
  • It shows promise for specific virulence categories, even with noisy public data.