ci7b00699_si_003.mpg (24.71 MB)

Structural Articulation of Biochemical Reactions Using Restrained Geometries and Topology Switching

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posted on 22.01.2018, 00:00 by Swati R. Manjari, Nilesh K. Banavali
A strategy named “restrained geometries and topology switching” (RGATS) is presented to obtain detailed trajectories for complex biochemical reactions using molecular mechanics (MM) methods. It enables prediction of realistic dynamical pathways for chemical reactions, especially for accurately characterizing the structural adjustments of highly complex environments to any proximal biochemical reaction. It can be used to generate reactive conformations, model stepwise or concerted reactions in complex environments, and probe the influence of changes in the environment. Its ability to take reactively nonoptimal conformations and generate favorable starting conformations for a biochemical reaction is illustrated for a proton transfer between two model compounds. Its ability to study concerted reactions in explicit solvent is illustrated using proton transfers between an ammonium ion and two conserved histidines in an ammonia transporter channel embedded in a lipid membrane. Its ability to characterize the changes induced by subtle differences in the active site environment is illustrated using nucleotide addition by a DNA polymerase in the presence of two versus three Mg2+ ions. RGATS can be employed within any MM program and requires no additional software implementation. This allows the full assortment of computational methods implemented in all available MM programs to be used to tackle virtually any question about biochemical reactions that is answerable without using a quantum mechanical (QM) model. It can also be applied to generate reasonable starting structures for more detailed and expensive QM or QM/MM methods. In particular, this strategy enables rapid prediction of reactant, intermediary, or product state structures in any macromolecular context, with the only requirement being that the structure in any one of these states is either known or can be accurately modeled.