Combining Physics-Based Protein–DNA Energetics with Machine Learning to Predict Interpretable Transcription Factor-DNA Binding

Transcription factors (TFs) are essential regulators of gene expression, and variations in their target DNA sequences due to altering TF-DNA binding affinity and specificity lead to diseases ranging from developmental disorders to cancer. Computational methods that integrate physics-based models with machine learning (ML) hold promise to accurately predict protein–DNA binding affinities while ensuring interpretability and generalizability. Here, we present an approach combining all-atom molecular dynamics (MD) simulations and Molecular Mechanics-Generalized Born Surface Area (MMGBSA) energy calculations with ML model constructions (neural networks, random forests, support vector machines) to predict DNA binding affinities and specificities for the dimeric TF Myc/Max. Using high-quality experimental data from genomic-context protein-binding microarrays (gcPBM), we constructed a balanced dataset of 168 DNA sequences reflecting physiologically relevant genomic environments. Multiple independent simulations were conducted per sequence for each TF-DNA complex to capture structural dynamic and interaction properties, with physically essential energetic descriptors extracted, including van der Waals, electrostatic, solvation, hydrogen bonding, and additional energy corrections. Our models achieved a Pearson correlation of ~0.73 and a mean absolute error of 0.4, substantially improving upon conventional MMGBSA prediction. Feature importance analyses highlighted TF-DNA interfacial complementarity and hydrophobic interactions as primary determinants of binding affinity and specificity, though TF-DNA interfacial hydrogen bonding contributions remain to be better characterized physically for sequence dependency. This physics-informed ML framework thus aims at both predictive accuracy and mechanistic interpretability, paving the way toward universal scalable prediction of interpretable protein–DNA interactions.