An all‐atom knowledge‐based energy function for protein‐DNA threading, docking decoy discrimination, and prediction of transcription‐factor binding profiles

This is the authors' abstract. We don't add key points for this paper.

How to make an accurate representation of protein-DNA interaction by an energy function is a long-standing unsolved problem in structural biology. Here, we modified a statistical potential based on the distance-scaled, finite ideal-gas reference state so that it is optimized for protein-DNA interactions. The changes include a volume-fraction correction to account for unmixable atom types in proteins and DNA in addition to the usage of a low-count correction, residue/base-specific atom types, and a shorter cutoff distance for protein-DNA interactions. The new statistical energy functions are tested in threading and docking decoy discriminations and prediction of protein-DNA binding affinities and transcription-factor binding profiles. The results indicate that new proposed energy functions are among the best in existing energy functions for protein-DNA interactions. The new energy functions are available as a web-server called DDNA 2.0 at http://sparks.informatics.iupui.edu. The server version was trained by the entire 212 protein-DNA complexes.