Predicting protein-DNA interactions by full search computational docking

Protein-DNA interactions are essential for many biological processes. X-ray crystallography can provide high-resolution structures, but protein-DNA complexes are difficult to crystallize and typically contain only small DNA fragments. Thus, there is a need for computational methods that can provide useful predictions to give insights into mechanisms and guide the design of new experiments. We used the program DOT, which performs an exhaustive, rigid-body search between two macromolecules, to investigate four diverse protein-DNA interactions. Here, we compare our computational results with subsequent experimental data on related systems. In all cases, the experimental data strongly supported our structural hypotheses from the docking calculations: a mechanism for weak, nonsequence-specific DNA binding by a transcription factor, a large DNA-binding footprint on the surface of the DNA-repair enzyme uracil-DNA glycosylase (UNG), viral and host DNA-binding sites on the catalytic domain of HIV integrase, and a three-DNA-contact model of the linker histone bound to the nucleosome. In the case of UNG, the experimental design was based on the DNA-binding surface found by docking, rather than the much smaller surface observed in the crystallographic structure. These comparisons demonstrate that the DOT electrostatic energy gives a good representation of the distinctive electrostatic properties of DNA and DNA-binding proteins. The large, favourably ranked clusters resulting from the dockings identify active sites, map out large DNA-binding sites, and reveal multiple DNA contacts with a protein. Thus, computational docking can not only help to identify protein-DNA interactions in the absence of a crystal structure, but also expand structural understanding beyond known crystallographic structures.