Deficiencies in Molecular Dynamics Simulation-Based Prediction of Protein-DNA Binding Free Energy Landscapes

Transcriptional regulation allows cells to match their gene expression profiles to their current requirements based on environment, cellular physiological state, and extracellular signals. DNA binding transcription factors are major agents of transcriptional regulation, and bind to DNA with a factor-specific sequence preference to exert regulatory effects. A crucial step in unraveling the logic of a regulatory network is determining the sequence-specific binding affinity landscapes for the transcription factors in it. While such landscapes can be measured experimentally, the ability to predict them computationally would both reduce the effort required to obtain the needed data and provide additional insight into the key interactions shaping protein-DNA interactions. Here we apply free energy calculations based on all-atom molecular dynamics simulations to predict the changes in binding free energy for all single base pair perturbations of the binding sites for four eukaryotic transcription factors for which high-quality experimental data exist. We find that the simulated results both vastly overestimate the magnitude of changes in binding free energy, and frequently predict the incorrect signs. These simulations will nevertheless serve as a jumping-off point for refining our current representation of protein-DNA interactions to allow quantitative reproduction of experimental data on such systems in the future.