Protein-DNA binding is at the core of gene regulatory processes. However, the underlying mechanisms of how proteins recognize their genomic target sites are not completely understood. Protein-DNA recognition involves chemical (base readout) and structural (shape readout) properties of DNA. To understand the mechanisms that allow proteins to recognize DNA and achieve their biological functions, it is important to analyze structures of protein-DNA complexes in detail. DNAproDB is a web-based interactive tool designed to help researchers study these complexes. DNAproDB provides an automated structure-processing pipeline that extracts structural features from protein-DNA complexes. Using structural analysis, we previously discovered that minor groove geometry offers an indirect representation of electrostatic potential in the minor groove, which is recognized by positively charged amino acids. We developed a methodology for predicting electrostatic potential in the minor groove in a computationally very efficient way. The DNAphi method uses a sliding-window approach to mine results from nonlinear Poisson–Boltzmann calculations on DNA structures derived from Monte Carlo simulations. Using statistical machine-learning approaches, we showed that adding electrostatic potential as a biophysical feature can improve the predictive power of quantitative binding specificity models across many transcription factor families.

Structural analysis and quantitative modeling of protein-DNA interactions

254th National Meeting (2017)

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