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Related Concept Videos

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...
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...
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...
Chromatin Immunoprecipitation- ChIP02:36

Chromatin Immunoprecipitation- ChIP

Chromatin immunoprecipitation, or ChIP, is an antibody-based technique used to identify sites on DNA that bind to transcription factors of interest or histone proteins. It also helps determine the type of histone modifications such as acetylation, phosphorylation, or methylation.
Types of ChIP
ChIP can be divided into two types - X-ChIP and N-ChIP. X-ChIP involves in vivo cross-linking of histones and regulatory proteins to DNA, fragmenting the DNA by sonication, and isolating the protein-DNA...

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Related Experiment Video

Updated: Jun 27, 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

A novel computational approach to predict transcription factor DNA binding preference.

Yudong Cai1, Jianfeng He, Xinlei Li

  • 1Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China. cyd@picb.ac.cn

Journal of Proteome Research
|December 23, 2008
PubMed
Summary

Predicting transcription factor DNA binding preferences is crucial for understanding gene regulation. This study accurately predicts these preferences using protein features and machine learning, revealing key DNA motifs.

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Published on: February 7, 2019

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Transcription is a fundamental cellular process where DNA sequences are converted into RNA by transcription factors.
  • Understanding transcription factor DNA binding preferences is essential for deciphering gene regulatory networks and mechanisms.
  • Accurate prediction aids in elucidating complex cellular regulation.

Purpose of the Study:

  • To develop a computational method for predicting transcription factor DNA binding preferences.
  • To identify key protein and DNA features influencing transcription factor binding.
  • To validate the predictive model using cross-validation and database analysis.

Main Methods:

  • Utilized protein amino acid composition and physicochemical properties.
  • Employed a 0/1 encoding system for nucleotide sequences.
  • Applied Minimum Redundancy Maximum Relevance (mRMR) feature selection and the Nearest Neighbor Algorithm.

Main Results:

  • Achieved an overall prediction accuracy of 91.1% using Jackknife cross-validation.
  • Identified protein secondary structure and polarizability as major contributing factors in prediction.
  • Discovered a 7-nucleotide motif with an AT-rich region in DNA binding sites.

Conclusions:

  • The developed approach provides a valuable tool for exploring transcription factor-DNA binding site relationships.
  • The findings offer insights into the molecular determinants of transcription factor binding specificity.
  • The identified DNA motif is consistent with existing biological databases, supporting the method's validity.