Study of complex activated molecular transitions by molecular dynamics (MD) simulation can be a daunting task, especially when little knowledge is available on the reaction coordinate describing the mechanism of the process. Here, we assess the path-metadynamics enhanced sampling approach in combination with force field and [density functional theory (DFT)] MD simulations of conformational and chemical transitions that require three or more collective variables (CVs) to describe the processes. We show that the method efficiently localizes the average transition path of each process and simultaneously obtains the free energy profile along the path. The new multiple-walker implementation greatly speeds-up the calculation, with an almost trivial scaling of the number of parallel replicas. Increasing the dimensionality by expanding the set of CVs leads to a less than linear increase in the computational cost, as shown by applying the method to a conformational change in increasingly longer polyproline peptides. Combined with DFT-MD to model acid (de-)protonation in explicit water solvent, the transition path and associated free energy profile were obtained in less than 100 ps of simulation. A final application to hydrogen fuel production catalyzed by a hydrogenase enzyme showcases the unique mechanistic insight and chemical understanding that can be obtained from the average transition path.
Di-iron hydrogenases
are a class of enzymes that are capable of
reducing protons to form molecular hydrogen with high efficiency.
In addition to the catalytic site, these enzymes have evolved dedicated
pathways to transport protons and electrons to the reaction center.
Here, we present a detailed study of the most likely proton transfer
pathway in such an enzyme using QM/MM molecular dynamics simulations.
The protons are transported through a channel lined out from the protein
exterior to the di-iron active site, by a series of hydrogen-bonded,
weakly acidic or basic, amino acids and two incorporated water molecules.
The channel shows remarkable flexibility, which is an essential feature
to quickly reset the hydrogen-bond direction in the channel after
each proton passing. Proton transport takes place via a “hole”
mechanism, rather than an excess proton mechanism, the free energy
landscape of which is remarkably flat, with a highest transition state
barrier of only 5 kcal/mol. These results confirm our previous assumptions
that proton transport is not rate limiting in the H
2
formation
activity and that cysteine C299 may be considered protonated at physiological
pH conditions. Detailed understanding of this proton transport may
aid in the ongoing attempts to design artificial biomimetic hydrogenases
for hydrogen fuel production.
FeFe] hydrogenase enzymes can reversibly catalyze the conversion of protons into molecular hydrogen. The active site of the [FeFe] hydrogenase enzyme is buried inside the protein. The transport of electrons and protons to the active site of the protein is crucial for an efficient catalytic cycle. A chain of iron-sulfur cubane cofactors forms a pathway for the electron transfer in these [FeFe] hydrogenases. We have studied the electron transfer process via the iron-sulfur clusters in the enzyme using classical molecular dynamics simulations. Our simulations show that the protein matrix acts as a porous medium for the transport of water molecules in and out during the electron transfer process. When an electron is transferred through the pathway, solvent water molecules penetrate the protein, forming hydrogen bonded networks and hydrating the electron accepting cubane clusters. The reorganization of the protein and the penetrating water molecules have a large effect on the free energy landscape of the electron transfer, via the formation of favorable hydrogen bonds with the reduced ironsulfur cluster, thereby stabilizing the electron at the cofactors.
The Fe2(bdt)(CO)6 [bdt = benzenedithiolato] complex, a synthetic mimic of the [FeFe] hydrogenase enzyme can electrochemically convert protons into molecular hydrogen. The free energy landscape reveals a different mechanism for the biomimetic cycle.
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