Computational structural analyses in the Honig lab derived mechanisms of protein-DNA binding specificity from experimentally solved three-dimensional structures of transcription factor-DNA complexes. One such mechanism was DNA shape and its effect on minor groove width and electrostatic potential. With the availability of a large amount of structural data from the Protein Data Bank and DNA sequence data from high-throughput binding assays, statistical machine learning and deep learning methods could be developed that identify features of proteins and nucleic acids that are essential for recognition and binding specificity. This talk will introduce such approaches and the new possibilities that such computational approaches provide for answering biological questions that relate to molecular recognition and gene regulation.

Deep learning methods reveal mechanisms of protein-DNA binding

ACS Spring 2022

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