Structural and dynamical properties of manganese catalase and the synthetic protein DF1 and their implication for reactivity from classical molecular dynamics calculations

Abstract

There is a pressing need for accurate force fields to assist in metalloprotein analysis and design. Recent work on the design of mimics of dimetal proteins highlights the requirements for activity. DF1 is a de novo designed protein, which mimics the overall fold and active site geometry of a series of diiron and dimanganese proteins. Specifically, the dimanganese form of DF1 is a mimic of the natural enzyme manganese catalase, which catalyzes the dismutation reaction of hydrogen peroxide into water and oxygen. During catalytic turnover, the active site has to accommodate both the reduced and the oxidized state of the dimanganese core. The biomimetic protein DF1 is only stable in the reduced form and thus not active. Furthermore, the synthetic protein features an additional bridging glutamate sidechain, which occupies the substrate binding site. The goal of this study is to develop classical force fields appropriate for design of such important dimanganese proteins. To this aim, we use a nonbonded model to represent the metal–ligand interactions, which implicitly takes into account charge transfer and local polarization effects between the metal and its ligands. To calibrate this approach, we compare and contrast geometric and dynamical properties of manganese catalase and DF1. Having demonstrated a good correspondence with experimental structural data, we examine the effect of mutating the bridging glutamate to aspartate (M1) and serine (M2). Classical MD based on the refined forcefield shows that these point mutations affect not only the immediate coordination sphere of the manganese ions, but also the relative position of the helices, improving the similarity to Mn-catalase, especially in case of M2. On the basis of these findings, classical molecular dynamics calculations with the active site parameterization scheme introduced herein seem to be a promising addition to the protein design toolbox. Proteins 2006;