Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 10, 2025Nucleic Acids ResearchOpen Access

Predicting the DNA binding specificity of transcription factor mutants using family-level biophysically interpretable machine learning

View Full Paper
Ask AI
Bookmark
Share

Authors

SLShaoxun LiuPGPilar Gomez-AlcalaCLChrist Leemans

Discussion

Loading...

Member takes

Overview

This method predicts shifts in binding free energy due to mutations in transcription factors, suggesting new biophysical insights.

Key Points

  • The method accurately predicts shifts in binding free energy (ΔΔΔG/RT) in transcription factor mutants.
  • Using high-quality DNA binding models from homologous wild-type transcription factors validates the approach.
  • Analysis focused on basic helix-loop-helix (bHLH) and homeodomain (HD) structural families demonstrates feasibility.
  • Predicting effects of mutations on DNA binding can uncover links between transcription factors and disease.

Cite This Study

Liu et al. (2025) studied this question.

synapsesocial.com/papers/68c243acb210217d647a79cdhttps://doi.org/10.1093/nar/gkaf831
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1DNA Conformational Flexibility Descriptors Improve Transcription Factor Binding Prediction Across the Protein Families2025
  2. 2Integrating Protein and DNA Embeddings for Improving Genome-Wide Transcription Factor Binding Site Prediction2025
  3. 3Multiview Deep Learning Framework for Precise Prediction of Transcription Factor Binding Sites2025 · 1 citations
  4. 4The genetic architecture of the human bZIP interaction network2025 · 1 citations
  5. 5TF-MAPS: fast high-resolution functional and allosteric mapping of DNA-binding proteins2025 · 2 citations