Homeodomain transcription factors (TFs) recognize their DNA targets through both sequence-specific base contacts and readout of local DNA shape. While intrinsic DNA structure is encoded by nucleotide sequence, it also undergoes protein-induced structural deformation upon binding. Yet, the interplay between intrinsic and protein-induced DNA shape remains unclear. Here, we dissect how these two readout modes determine binding specificity in a trimeric complex composed of the Drosophila Hox TF Sex combs reduced (Scr) and its co-factors, Homothorax (Hth) and Extradenticle (Exd). Guided by SELEX-seq data, we performed molecular dynamics (MD) simulations of this complex bound to sequences of varying binding affinities. We find that minor groove width (MGW) reflects intrinsic DNA structure, whereas MGW fluctuations (MGW-FL) capture protein-induced stabilization and reshaping of the DNA. Within the trimeric complex, Hth reduces conformational fluctuations in an orientation- and sequence-dependent manner, with charged residues in its N-terminal arm playing key roles in DNA shape readout. This demonstrates that recognition involves a context-dependent balance between conformational selection and induced fit. We extend this analysis to two other homeodomain TFs, Distal-less (Dll) and Engrailed (En), revealing that even closely related proteins produce distinct DNA shape signatures depending on sequence context. To translate these insights to novel sequences or mutant proteins, we evaluate whether AlphaFold 3 (AF3) can capture mutation-sensitive DNA shape readout. While AF3 accurately reproduces wild-type structures, it struggles to predict how mutations or conformational dynamics alter DNA shape. To bridge this gap, we developed a hybrid pipeline integrating AlphaFold-based homology modeling, MD simulations, and DeepPBS, a deep learning method for binding specificity prediction. This multi-scale framework successfully captures the active modulation of DNA structure missed by AF3 alone, providing new insights into TF binding specificity and creating a roadmap for integrating deep learning and physics-based methods to study molecular mechanisms.
Jiang et al. (Sun,) studied this question.