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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...
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...
Transcription Factors02:16

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...
Transcription Factors02:16

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...
Master Transcription Regulators02:23

Master Transcription Regulators

Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
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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Related Experiment Video

Updated: May 10, 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

Stability selection for regression-based models of transcription factor-DNA binding specificity.

Fantine Mordelet1, John Horton, Alexander J Hartemink

  • 1Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA.

Bioinformatics (Oxford, England)
|July 2, 2013
PubMed
Summary

We developed new regression models to accurately predict transcription factor DNA binding specificity. These models capture complex sequence interactions, improving upon traditional methods and aiding in understanding gene regulation.

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Last Updated: May 10, 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

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
11:34

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Area of Science:

  • Computational Biology
  • Genomics
  • Molecular Biology

Background:

  • Traditional position weight matrix models for transcription factor (TF) DNA binding assume independent base contributions, which is often inaccurate.
  • More complex models exist but suffer from high parameter counts, hindering learning and interpretation.

Purpose of the Study:

  • To develop novel, interpretable regression-based models for TF-DNA binding specificity.
  • To improve upon existing models by incorporating higher-order sequence features.

Main Methods:

  • Utilized high-resolution in vitro protein-binding microarray (PBM) data for training.
  • Developed regression models incorporating di- and trinucleotide contributions.
  • Employed feature selection to maintain model interpretability.

Main Results:

  • Trained models on PBM data for yeast (Cbf1, Tye7) and human (c-Myc, Max, Mad2) TFs.
  • Demonstrated that models can accurately predict TF-DNA binding specificity, including distinguishing paralogous TFs.
  • Identified key sequence features for accurate binding prediction.

Conclusions:

  • The proposed regression models offer a more accurate and interpretable approach to modeling TF-DNA binding specificity.
  • This work advances sequence-based modeling for individual TF binding, crucial for understanding gene regulation.