With just a simple alphabet of natural amino acids and common metals, enzymes perform a spectacular number of reactions necessary for life on earth with enviable ease – with neither the extreme conditions nor chemical waste seen from many industrial reactors in human-developed catalysis. Principally, protein structure and related function is not well understood beyond being decisive for its unique selectivity and efficiency. A reliable treatment of larger protein movements/sampling coupled with precise quantum mechanical treatment of metals and bonds breaking/forming (multiscale) is still an open problem in Computational Chemistry. Metalloenzymes can be particularly challenging to model so we present some of the latest in multiscale modeling techniques. We have developed methods sensitive enough to study “selection” of similar metals in enzymes such as HDAC8, where previous literature failed to conclude which metal is active during in vivo catalysis, while fast enough to study larger protein movements including fold stability.

Eminently, multiscale modeling opens the discussion of engineering enzymes to cater to modern day needs in catalysis. Society has not only developed needs in catalysis outside the scope of natural evolution (e.g., drug synthesis, energy conversion) but also has access to more of the periodic table than nature has had a chance to explore. Thus, combining the efficacy of natural enzymes with modern catalytic processes has enormous potential. We have studied iridium, a rare metal with thermodynamic advantages over other metals in promoting catalysis of hydroamination and transfer hydrogenation. In the context of an enzyme we predict catalytic rates near and even exceeding existing organometallic catalysts with further design for specificity available. We are in collaboration with an experimental group in designing a protein fold which both directly accommodates and uses iridium in catalysis. We hope these foundations will support metalloenzyme design efforts towards novel chemical transformations performed as efficiently and environmentally soundly as nature has shown us is possible.

Molecular dynamics (MD) is a powerful tool to study atomic scale changes in proteins underpinning biological and catalytic pathways. It is routinely used to identify small molecule binding sites and study protein folding. MD simulations struggle to achieve quantitatively accurate energies and traditionally sample a fixed chemical state – they do not allow covalent bonds to form or break. However, chemical change underlies many important biochemical processes, including shifts in metal ligation that occur during metalloenzyme activity or when metal ions bind to metalloproteins, or the protonation and deprotonation of amino acids that dictate pH-dependent activity and stability. Understanding these processes at the atomic scale demands computational tools that operate in conjunction with MD to calculate more accurate energies.

Hybrid quantum mechanical/molecular mechanics approaches (QM/MM) can capture the dynamics of chemical change as they provide accurate energies for small regions of interest, such as the active site. We have developed one such hybrid method, QM/DMD, which combines density functional theory (DFT) calculations with discrete molecular dynamics simulations for rapid metalloprotein conformational sampling that can even capture shifts in metal ligation. With QM/DMD we identified the orientational preferences of a cofactor in phenylalanine hydroxylase that distinguishes the disease state of phenylketonuria. To study some forms of chemical change further theory even beyond QM/DMD is necessary. We created a competitive metal affinity (CMA) method, a semi-empirical thermodynamic cycle, to calculate relative metal binding affinities to metalloproteins. With our CMA method and QM/DMD we assessed the ability of human serum transferrin to transport non-native metals and therefore its role in metal cytotoxicity. We also identified the different mechanisms by which Li⁺ and Be�⁺ inhibit glycogen synthase kinase 3β, which could inform future drug design targeting that protein.

In contrast to the detailed QM treatment of a small region needed to study metal behavior, pH-dependent behavior requires rapid sampling of chemical changes across the whole protein in the form of protonation and deprotonation reactions. We therefore developed a constant-pH molecular dynamics method, called titr-DMD, that stochastically updates protonation states based on the efficient DMD method and the semi-empirical electrostatic method Propka. We successfully benchmarked titr-DMD on experimentally verified pH dependent conformational changes in a staphylococcal nuclease mutant. Our work demonstrates the utility of properly modified molecular dynamics, and QM/DMD in particular, to study many forms of chemical change in proteins with good accuracy and speed.