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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-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...
Conserved Binding Sites01:49

Conserved Binding Sites

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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...

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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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A dynamic Bayesian network for identifying protein-binding footprints from single molecule-based sequencing data.

Xiaoyu Chen1, Michael M Hoffman, Jeff A Bilmes

  • 1Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA.

Bioinformatics (Oxford, England)
|June 10, 2010
PubMed
Summary
This summary is machine-generated.

A new dynamic Bayesian network approach, DBFP, accurately identifies transcription factor binding sites using digital genomic footprinting. This method improves precision for mapping gene regulation across genomes.

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Understanding gene regulation requires mapping transcription factor binding sites (TFBSs).
  • Digital genomic footprinting offers high-resolution identification of protein-binding footprints genome-wide.
  • Accurate computational inference of these footprints remains a challenge.

Purpose of the Study:

  • To develop a robust computational method for identifying protein-binding footprints from digital genomic footprinting data.
  • To assign statistical confidence estimates to identified footprints.
  • To improve the accuracy and precision of TFBS mapping.

Main Methods:

  • A dynamic Bayesian network-based approach (DBFP) was developed.
  • The method probabilistically identifies protein-binding footprints.
  • DBFP was applied to digital footprinting data from Saccharomyces cerevisiae.

Main Results:

  • DBFP identified 4679 statistically significant footprints in yeast intergenic regions, primarily near transcription start sites.
  • These footprints were significantly enriched for known TFBSs.
  • DBFP also identified footprints in coding regions, some coinciding with antisense transcripts and enriched for chromatin-associated factors like Abf1 and Rap1.

Conclusions:

  • DBFP provides a powerful, probabilistic framework for identifying and assigning confidence to protein-binding footprints.
  • The method enhances the precision of TFBS mapping compared to previous algorithms.
  • The identified footprints offer insights into yeast gene regulation, including potential cooperative binding and roles in coding regions.