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Protein Networks02:26

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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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Two structural features of the DNA molecule provide a basis for the mechanisms of heredity: the four nucleotide bases and its double-stranded nature. The Watson-Crick model of double-helical DNA structure, proposed in 1952, drew heavily upon the X-ray crystallography work of researchers Rosalind Franklin and Maurice Wilkins. Watson, Crick, and Wilkins jointly received the Nobel Prize in Physiology or Medicine for their work in 1962. Franklin was, controversially, excluded from the prize for...
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Learning protein-DNA interaction landscapes by integrating experimental data through computational models.

Jianling Zhong1, Todd Wasson1, Alexander J Hartemink2

  • 1Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, Knowledge Systems and Informatics, Lawrence Livermore National Laboratory, Livermore, CA 94550 and Department of Computer Science, Duke University, Durham, NC 27708, USA.

Bioinformatics (Oxford, England)
|June 30, 2014
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Summary

We developed a new statistical framework to integrate diverse experimental data for a comprehensive understanding of protein-DNA interactions. This approach accurately models genomic occupancy and aids in studying transcriptional regulation.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Transcriptional regulation involves complex interactions between DNA and proteins like transcription factors.
  • Existing experimental methods provide only partial insights into the protein-DNA interaction landscape.
  • A holistic view requires integrating diverse data sources for accurate inference.

Purpose of the Study:

  • To develop a novel statistical framework for a holistic view of in vivo protein-DNA interactions.
  • To integrate diverse experimental data within a thermodynamic model of competitive binding.
  • To accurately predict protein-DNA interaction locations, strength, and frequency.

Main Methods:

  • Developed a statistical framework integrating multiple experimental data sources.
  • Employed a thermodynamic model of competitive binding.
  • Utilized MCMC-based inference for model parameter estimation.

Main Results:

  • The framework integrates diverse experimental data to provide a holistic view of protein-DNA interactions.
  • Achieved increased accuracy in modeling the interaction landscape.
  • Demonstrated consistency with thermodynamic principles of competitive DNA binding.

Conclusions:

  • The developed framework offers a precise mechanistic view of genomic occupancy.
  • Enables exploration of protein-DNA interactions in transcriptional regulation.
  • Provides a powerful tool for deciphering complex biological regulatory mechanisms.