Free-energy component analysis of 40 protein-DNA complexes: a consensus view on the thermodynamics of binding at the molecular level.

Noncovalent association of proteins to specific target sites on DNA--a process central to gene expression and regulation--has thus far proven to be idiosyncratic and elusive to generalizations on the nature of the driving forces. The spate of structural information on protein--DNA complexes sets the stage for theoretical investigations on the molecular thermodynamics of binding aimed at identifying forces responsible for specific macromolecular recognition. Computation of absolute binding free energies for systems of this complexity transiting from structural information is a stupendous task. Adopting some recent progresses in treating atomic level interactions in proteins and nucleic acids including solvent and salt effects, we have put together an energy component methodology cast in a phenomenological mode and amenable to systematic improvements and developed a computational first atlas of the free energy contributors to binding in approximately 40 protein-DNA complexes representing a variety of structural motifs and functions. Illustrating vividly the compensatory nature of the free energy components contributing to the energetics of recognition for attaining optimal binding, our results highlight unambiguously the roles played by packing, electrostatics including hydrogen bonds, ion and water release (cavitation) in protein-DNA binding. Cavitation and van der Waals contributions without exception favor complexation. The electrostatics is marginally unfavorable in a consensus view. Basic residues on the protein contribute favorably to binding despite the desolvation expense. The electrostatics arising from the acidic and neutral residues proves unfavorable to binding. An enveloping mode of binding to short stretches of DNA makes for a strong unfavorable net electrostatics but a highly favorable van der Waals and cavitation contribution. Thus, noncovalent protein-DNA association is a system-specific fine balancing act of these diverse competing forces. With the advances in computational methods as applied to macromolecular recognition, the challenge now seems to be to correlate the differential (initial vs. final) energetics to substituent effects in drug design and to move from affinity to specificity.