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Related Concept Videos

Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form dimers that...
General Transcription Factors01:30

General Transcription Factors

Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...

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Related Experiment Video

Updated: May 20, 2026

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
06:38

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy

Published on: February 7, 2019

High performance transcription factor-DNA docking with GPU computing.

Jiadong Wu1, Bo Hong, Takako Takeda

  • 1School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, 30332, USA. bhong6@gatech.edu.

Proteome Science
|July 5, 2012
PubMed
Summary
This summary is machine-generated.

Accelerating protein-DNA docking with a graphics processing unit (GPU) algorithm significantly improves computational speed and the accuracy of predicting near-native complex structures. This advancement enhances structural bioinformatics applications like drug design.

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Related Experiment Videos

Last Updated: May 20, 2026

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
06:38

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy

Published on: February 7, 2019

DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation
09:26

DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation

Published on: December 29, 2021

Area of Science:

  • Structural Bioinformatics
  • Computational Biology
  • Biophysics

Background:

  • Protein-DNA docking is crucial for predicting transcription factor binding sites and drug design.
  • Current docking methods are computationally intensive, limiting sampling space and prediction accuracy.
  • Accelerating protein-DNA docking is essential for both reducing computation time and enhancing prediction quality.

Purpose of the Study:

  • To develop a graphics processing unit (GPU)-based algorithm for accelerating protein-DNA docking.
  • To improve the efficiency and scalability of protein-DNA docking simulations on high-performance computing systems.

Main Methods:

  • Developed a GPU-based protein-DNA docking algorithm utilizing a potential-based energy function.
  • Integrated Monte Carlo simulation and simulated annealing for conformational space searching.
  • Implemented algorithmic techniques to optimize computation efficiency and scalability on GPUs.

Main Results:

  • The GPU-based algorithm significantly accelerates protein-DNA docking simulations.
  • Demonstrated improved chances of identifying near-native protein-DNA complex structures.
  • Validated on a benchmark set of 75 TF-DNA complexes and a new docking benchmark.

Conclusions:

  • A high-performance computing approach using GPUs enhances protein-DNA docking prediction accuracy.
  • Accelerated conformational space searching increases the likelihood of finding near-native structures.
  • This represents a novel application of GPU computing to the protein-DNA docking problem.