A biophysical approach to predicting protein-DNA binding energetics

Sequence-specific interactions between proteins and DNA play a central role in DNA replication, repair, recombination, and control of gene expression. These interactions can be studied in vitro using microfluidics, protein-binding microarrays (PBMs), and other high-throughput techniques. Here we develop a biophysical approach to predicting protein-DNA binding specificities from high-throughput in vitro data. Our algorithm, called BindSter, accommodates multiple protein species competing for access to DNA and alternative binding modes of the same protein, while rigorously taking into account all sterically allowed configurations of DNA-bound particles. BindSter can be used with a hierarchy of protein-DNA interaction models of increasing complexity. We observe that the quality of BindSter predictions does not change significantly as some of the energy parameters vary over a sizable range. To take this degeneracy into account, we have developed a graphical representation of parameter uncertainties, called IntervalLogo. We find that our simplest model, in which each nucleotide in the binding site is treated independently, performs better than previous biophysical approaches. The extensions of this model, in which contributions of longer words are also considered, result in further improvements, underscoring the importance of higherorder effects in protein-DNA energetics. In contrast, we find little evidence for multiple binding modes for the transcription factors (TFs) in our dataset. Furthermore, there is limited consistency in predictions for the same TF utilizing microfluidics and PBM experimental platforms.