Liu Shaoxun, Gomez-Alcala Pilar, Leemans Christ, Glassford William J, Melo Lucas A N, Lu Xiang-Jun, Mann Richard S, Bussemaker Harmen J
Department of Biological Sciences, Columbia University, New York, NY 10027, United States.
Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, United States.
Nucleic Acids Res. 2025 Aug 27;53(16). doi: 10.1093/nar/gkaf831.
Sequence-specific interactions of transcription factors (TFs) with genomic DNA underlie many cellular processes. High-throughput in vitro binding assays coupled with machine learning have made it possible to accurately define such molecular recognition in a biophysically interpretable way for hundreds of TFs across many structural families, providing new avenues for predicting how the sequence preference of a TF is impacted by disease-associated mutations in its DNA binding domain. We developed a method based on a reference-free tetrahedral representation of variation in base preference within a given structural family that can be used to accurately predict the effect of mutations in the protein sequence of the TF. Using the basic helix-loop-helix (bHLH) and homeodomain (HD) families as test cases, our results demonstrate the feasibility of accurately predicting the shifts (ΔΔΔG/RT) in binding free energy associated with TF mutants by leveraging high-quality DNA binding models for sets of homologous wild-type TFs.
转录因子（TFs）与基因组DNA的序列特异性相互作用是许多细胞过程的基础。高通量体外结合测定与机器学习相结合，使得以生物物理可解释的方式准确界定许多结构家族中数百种TFs的这种分子识别成为可能，为预测TF的序列偏好如何受到其DNA结合结构域中疾病相关突变的影响提供了新途径。我们开发了一种基于给定结构家族内碱基偏好变化的无参考四面体表示法的方法，该方法可用于准确预测TF蛋白质序列中突变的影响。以基本螺旋-环-螺旋（bHLH）和同源结构域（HD）家族作为测试案例，我们的结果证明了通过利用同源野生型TFs集合的高质量DNA结合模型，准确预测与TF突变体相关的结合自由能变化（ΔΔΔG/RT）的可行性。
