UCLA

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.