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Raktim Mitra, Jinsen Li, Jared Sagendorf, Yibei Jiang, Tsu-Pei Chiu, Cameron J. Glasscock, and Remo Rohs (University of Southern California Los Angeles, CA 90089, USA)
Predicting binding specificity in protein-DNA interactions is a challenging yet essential task for understanding gene regulation. Protein-DNA complexes exhibit binding to a selected target site whereas a protein binds with varying degrees of binding specificity to a range of sequences. This is information that is not directly accessible in a single structure. We present Deep Predictor of Binding Specificity (DeepPBS), a geometric deep learning model designed to predict binding specificity from protein-DNA structure. The DeepPBS architecture allows investigation of different family-specific recognition patterns. DeepPBS can be applied to predicted structures, molecular simulations, and can aid in the modeling of protein-DNA complexes. DeepPBS is interpretable and can be used to calculate protein heavy-atom importance scores, demonstrated on p53-DNA interface. When aggregated at the protein residue level, these scores conform well with alanine scanning mutagenesis experiments. Beyond predicted binding specificity for biological complexes, we present applications of DeepPBS on in-silico designed proteins targeting specific DNA sequences. DeepPBS offers a foundation for machine-aided p