Understanding Protein-DNA Interactions through Dynamics

Abstract

Transcriptional regulation is a key factor in controlling proper cellular behavior. For this reason, so-called regulation networks (quantifying the molecular interactions controlling the transcription), have been heavily studied. One goal is to enrich these networks through in silico identification of DNA-binding proteins and their respective binding sites. Often such work assumes a specific distance between atoms as constituting an interaction and construct models based on this assumption. However, this ad hoc rule fails to account for many of the complexities that lie behind the physical nature of binding. We present a framework for studying these interactions in more realistic settings accounting for both overall energy and dynamics of protein-DNA complex. We demonstrate that short molecular dynamics simulations better characterize biomolecular interactions and that a better definition of interactions improves the prediction of protein-DNA docking. Specifically, interacting residues are identified through the analysis of MD energy functions and results are compared with published experimental results. Further, we show how our novel definition of DNA-binding can be used for constructing improved machine learning classifiers for automatic identification of DNA-binding residues.