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

Area of Science:

• Molecular Biology
• Genomics
• Biophysics

Background:

• Transcription factors (TFs) mediate crucial cellular processes through sequence-specific DNA binding.
• High-throughput assays and machine learning have advanced the definition of TF-DNA recognition.
• Understanding mutation effects on TF binding is vital for disease research.

Purpose of the Study:

• To develop a method for predicting the impact of mutations in TF DNA-binding domains on sequence preference.
• To accurately quantify shifts in binding free energy (ΔΔΔG/RT) for TF mutants.

Main Methods:

• Developed a reference-free tetrahedral representation for base preference variation within TF structural families.
• Utilized high-quality DNA binding models of wild-type TFs.
• Applied the method to basic helix-loop-helix (bHLH) and homeodomain (HD) TF families.

Main Results:

• Demonstrated the feasibility of accurately predicting mutation-induced shifts in TF binding free energy.
• Successfully predicted changes in binding energy for TF mutants using the developed method.
• Validated the approach on bHLH and HD TF families.

Conclusions:

• The developed method enables accurate prediction of mutation effects on TF-DNA binding affinity.
• This approach provides a powerful tool for interpreting disease-associated mutations in TFs.
• Leveraging existing TF binding data can significantly advance predictive capabilities in molecular recognition.