The amount of structural information concerning protein–DNA complexes is increasing rapidly due to numerous efforts from the X-ray crystallography and NMR spectroscopy fields and to computational advances in analysis, modeling. In particular docking, a computational approach that models the unknown structure of a complex from its constituents, is a valuable tool to study complex formation in interaction networks. In the context of protein–DNA complexes, docking has been used for screening potential interaction partners, studying specific interactions and assisting at various stages of the experimental workflow.
As in many computational techniques, also in docking there is a tradeoff between available computational resources and the ability to answer scientific questions with sufficient details. With the focus on adequately and quickly sampling the relevant conformational space, docking is, in most cases, performed in vacuo, neglecting the physical, aqueous environment, where the biomolecules are functional. For protein–DNA interactions in particular, water molecules are involved in diverse tasks such as screening for favorable DNA interaction sites, stabilizing complex formation and facilitating specific interactions Despite increasing computational resources, it is surprising that water molecules are still mostly neglected in docking protocols. The first two applications model the water molecules after the complex has been formed, thus neglecting the possible effect water has on the complex formation process. For the purpose of docking this is irrelevant because the structure of the complex is not yet known. It is thus important that explicit water molecules are present during complex formation, an approach successfully applied to the docking of ligand molecules as mentioned above.
It is considered the identification of interacting protein-nucleic acid partners using the rigid body docking method FTdock, which is systematic and exhaustive in the exploration of docking conformations. The accuracy of rigid body docking methods is tested using known protein-DNA complexes for which the docked and undocked structures are both available. The CCP (Chemical Context Discrepancy) has been designed to capture the chemical complementarities of the interface and is well suited for machine learning techniques. CCP provides a useful scoring function when certain dimensions are properly weighted. Finally it is explored how the amino acids on a protein’s surface can help guide DNA binding, first through long-range interactions, followed by direct contacts, according to specific preferences for either the major or minor grooves of the DNA.
Biomolecular docking has become a mature discipline within structural biology. Docking aims at predicting the structure of a complex given the 3D structures of its components. The field of protein–protein docking in particular has seen extensive progress over the last decade as witnessed by recent CAPRI (Critical Assessment of Predicted Interactions) results, a community-wide blind docking experiment. For protein–DNA docking, however, progress lags behind. The scarcity of information for a proper identification of interaction surfaces on DNA and its inherent flexibility have hampered the development of effective docking methods. The field of protein–DNA docking is, however, receiving increased attention and efforts are put into the development of docking methods. Considering the importance of biomolecular interactions in system biology, gaining insight into the biochemistry of recognition and gene expression is highly relevant. New developments in protein–DNA docking approaches are therefore expected.