DNA sequence quantitatively encodes CTCF-binding affinity at genome scale

CTCF is a central architectural protein that shapes 3D genome organization through sequence-specific DNA binding, but how DNA sequence quantitatively determines CTCF-binding strength remains poorly understood. Progress has been hampered by the lack of large, high-quality measurements of binding affinity. Here, we experimentally determine in vitro CTCF-binding affinity for 276,765 DNA sequences derived from the human genome, generating a comprehensive quantitative landscape of CTCF-DNA interactions. Leveraging these data, we develop DeepCTCF, a deep learning model that predicts CTCF-binding strength directly from DNA sequence and enables quantitative interpretation of CTCF motif grammar. Using this framework, we systematically dissect how specific sequence features modulate CTCF-binding affinity and generate quantitative predictions for disease-associated variants that alter CTCF binding. Together, this study defines general principles by which DNA sequence encodes CTCF-binding affinity and provides a quantitative framework for interpreting regulatory sequence variation. TeaserMapping how DNA encodes CTCF binding reveals quantitative rules across over 1{per thousand} of the human genome.