Free energy landscape and characteristic forces for the initiation of DNA unzipping

DNA unzipping, the separation of its double helix into single strands, is crucial in modulating a host of genetic processes. Although the large-scale separation of double-stranded DNA has been studied with a variety of theoretical and experimental techniques, the minute details of the very first steps of unzipping are still unclear. Here, we use atomistic molecular dynamics (MD) simulations, coarse-grained simulations and a statistical-mechanical model to study the initiation of DNA unzipping by an external force. The calculation of the potential of mean force profiles for the initial separation of the first few terminal base pairs in a DNA oligomer reveal that forces ranging between 130 and 230 pN are needed to disrupt the first base pair, values of an order of magnitude larger than those needed to disrupt base pairs in partially unzipped DNA. The force peak has an "echo," of approximately 50 pN, at the distance that unzips the second base pair. We show that the high peak needed to initiate unzipping derives from a free energy basin that is distinct from the basins of subsequent base pairs because of entropic contributions and we highlight the microscopic origin of the peak. Our results suggest a new window of exploration for single molecule experiments.Comment: 25 pages, 6 figures , Accepted for publication in Biophysical Journa

Using Selectively Applied Accelerated Molecular Dynamics to Enhance Free Energy Calculations

Accelerated molecular dynamics (aMD) has been shown to enhance conformational space sampling relative to classical molecular dynamics; however, the exponential reweighting of aMD trajectories, which is necessary for the calculation of free energies relating to the classical system, is oftentimes problematic, especially for systems larger than small poly peptides. Here, we propose a method of accelerating only the degrees of freedom most pertinent to sampling, thereby reducing the total acceleration added to the system and improving the convergence of calculated ensemble averages, which we term selective aMD. Its application is highlighted in two biomolecular cases. First, the model system alanine dipeptide is simulated with classical MD, all-dihedral aMD, and selective aMD, and these results are compared to the infinite sampling limit as calculated with metadynamics. We show that both forms of aMD enhance the convergence of the underlying free energy landscape by 5-fold relative to classical MD; however, selective aMD can produce improved statistics over all-dihedral aMD due to the improved reweighting. Then we focus on the pharmaceutically relevant case of computing the free energy of the decoupling of oseltamivir in the active site of neuraminidase. Results show that selective aMD greatly reduces the cost of this alchemical free energy transformation, whereas all-dihedral aMD produces unreliable free energy estimates