Special Computational Biology Seminar

  • Gates Hillman Centers
  • ASA Conference Room 6115
  • Professor of Biological Sciences, Chemistry, Physics & Astronomy, and Computer Science Section
  • Head, Quantitative and Computational Biology
  • Program Director, Quantitative Biology, University of Southern California

Quantitative Modeling of Protein-DNA Binding Specificity

Many structures of protein-DNA complexes have been solved and high-throughput binding assays were developed because structural biology and genomics researchers were equally puzzled by the question of how proteins bind DNA with high specificity. However, there was little communication between these two fields of research. High-throughput DNA shape prediction established a cross talk between both fields. The primary goal of DNA shape analysis remains the quest for mechanistic insights into protein-DNA readout modes based on sequencing data without the need of structure determination. A plethora of high-throughput sequencing data is available from a variety of experimental approaches. In contrast, structural biology, albeit being an atomic-resolution approach, often reports the binding of a single protein to only a single DNA target. A number of studies incorporated DNA shape features in the quantitative modeling of binding specificities. These studies emphasized the importance of interactions between nucleotide positions within a binding site and its flanks, although the definitions of DNA sequence versus shape still differ in the structural biology and genomics fields. Next steps discussed in the talk focus on understanding biological phenomena such as purifying selection, additional layers of binding specificity determinants such as DNA methylation and histone modifications, and new computational approaches including deep learning and quantum computing.

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