Albany 2013: Book of Abstracts
June 11-15 2013
©Adenine Press (2012)
Structural articulation of biochemical reactions using altered bonds and iterative topology switching
Structural and dynamic trajectories of biochemical reactions catalyzed by large macromolecular complexes are difficult and expensive to characterize experimentally. Predicting them thoroughly using quantum mechanics/molecular mechanics (QM/MM) approaches is possible but requires significant computational resources. Atomic detail structural snapshots of individual states obtained through crystallography may also not always be in a conformation conducive to the reaction, presenting a hurdle for choosing the appropriate starting structures for the QM/MM calculations. The geometric displacements for nuclei that change their bonding during chemical reactions are usually not large when starting from appropriate reactive conformations. If we hypothesize that there is no need for these rearrangements to be represented in electronic detail to capture the response of the surrounding macromolecular environment, MM energy functions should provide a relatively accurate description of this structural reorganization. In the present study, a strategy called altered bonds and iterative topology swapping with MM models is used to obtain reasonable atomic detail trajectories for complex biochemical reactions. The implementation, advantages, and disadvantages of this strategy are illustrated using a symmetric model proton transfer reaction. This application is expanded to describe a similar proton transfer reaction in a complex macromolecular environment. The strategy is then utilized to visualize a complex biochemical reaction representing a critical step in the central dogma of biology, namely addition of a deoxyribonucleotide to a DNA strand by a DNA polymerase. This strategy reduces the computational cost of obtaining reasonable biochemical reaction trajectories within complex macromolecular environments by orders of magnitude as compared to QM/MM simulations, thus rapidly identifying good structural starting points for such simulations. Its easy implementation allows the direct application of most computational methods designed for MM models towards understanding reactions involving covalent changes. It also enables a reasonable structural prediction for all reactant, transition, intermediate, and product states for any biochemical reaction for which only one of these states is known. Its high-throughput application offers the promise of delineating, dynamically and in atomic detail, any biochemical reaction catalyzed by a macromolecular catalyst whose structure is either known or can be accurately modeled.
This research has been supported by new investigator funds from the Wadsworth Center.
Swati R. Manjari
Division of Genetics