Title: Understanding the Interaction between Low-Energy Electrons and DNA Nucleotides in Aqueous Solution
Author(s): McAllister M., Smyth, M., Gu B., Tribello G.A., Kohanoff J.,
Journal of Physical Chemistry Letters, 6, No. 15, pp. 3091-3097 (6 August 2015)
Reactions that can damage DNA have been simulated using a combination of molecular dynamics and density functional theory. In particular, the damage caused by the attachment of a low energy electron to the nucleobase. Simulations of anionic single nucleotides of DNA in an aqueous environment that was modeled explicitly have been performed. This has allowed us to examine the role played by the water molecules that surround the DNA in radiation damage mechanisms. Our simulations show that hydrogen bonding and protonation of the nucleotide by the water can have a significant effect on the barriers to strand breaking reactions. Furthermore, these effects are not the same for all four of the bases.
Title: Probing the Unfolded Configurations of a β-Hairpin Using Sketch-Map
Author(s): Ardevol A, Tribello G., Ceriotti M., Parrinello M.
Journal of Chemical Theory and Computation, 11, No. 3, pp. 1086-1093 (10 February 2015)
This work examines the conformational ensemble involved in β-hairpin folding by means of advanced molecular dynamics simulations and dimensionality reduction. A fully atomistic description of the protein and the surrounding solvent molecules is used, and this complex energy landscape is sampled by means of parallel tempering metadynamics simulations. The ensemble of configurations explored is analyzed using the recently proposed sketch-map algorithm. Further simulations allow us to probe how mutations affect the structures adopted by this protein. We find that many of the configurations adopted by a mutant are the same as those adopted by the wild-type protein. Furthermore, certain mutations destabilize secondary-structure-containing configurations by preventing the formation of hydrogen bonds or by promoting the formation of new intramolecular contacts. Our analysis demonstrates that machine-learning techniques can be used to study the energy landscapes of complex molecules and that the visualizations that are generated in this way provide a natural basis for examining how the stabilities of particular configurations of the molecule are affected by factors such as temperature or structural mutations.