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Related Concept Videos

Cooperative Binding of Transcription Regulators02:13

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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...
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Updated: Jan 13, 2026

Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis
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A structure-guided approach to noncoding variant evaluation for transcription factor binding using AlphaFold 3.

Lukas Gerasimavicius1, Simon C Biddie1,2, Joseph A Marsh1

  • 1MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, United Kingdom.

Nucleic Acids Research
|January 7, 2026
PubMed
Summary
This summary is machine-generated.

Structural modeling using AlphaFold 3 and FoldX offers insights into noncoding variants affecting transcription factor binding. This approach, evaluating interface-predicted template modeling (ipTM) scores, complements sequence-based methods for disease variant analysis.

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Area of Science:

  • Genomics
  • Structural Biology
  • Bioinformatics

Background:

  • Noncoding single-nucleotide variants (SNVs) can alter gene expression by affecting transcription factor (TF) binding, contributing to disease.
  • Current sequence-based prediction methods for TF binding have limitations, including reliance on training data and TF-specific biases.

Purpose of the Study:

  • To develop and evaluate a structure-guided approach for predicting the impact of noncoding SNVs on TF binding.
  • To assess the utility of AlphaFold 3 (AF3) and FoldX in modeling TF-DNA complexes and evaluating variant effects.

Main Methods:

  • Utilized AlphaFold 3 (AF3) to model transcription factor-DNA complexes.
  • Employed FoldX for physics-based assessment of variant effects on TF binding affinity.
  • Benchmarked predictions against experimental SNP-SELEX data for six transcription factors.

Main Results:

  • The FoldX-based strategy showed good agreement with experimental allele preferences.
  • AlphaFold 3's interface-predicted template modeling (ipTM) scores aligned closely with experimental data, often outperforming energy-based metrics.
  • The combined analysis of ΔipTM and FoldX energies improved reliability for disease-associated variants.

Conclusions:

  • Structural modeling provides interpretable insights into how noncoding variants influence TF binding.
  • The proposed structure-guided approach offers a complementary evaluation method for regulatory variants.
  • Highlights the potential and limitations of AF3 for analyzing noncoding variants impacting TF binding.