Role of substrate dynamics in protein prenylation reactions

CONSPECTUS: The role dynamics plays in proteins is of intense contemporary interest. Fundamental insights into how dynamics affects reactivity and product distributions will facilitate the design of novel catalysts that can produce high quality compounds that can be employed, for example, as fuels and life saving drugs. We have used molecular dynamics (MD) methods and combined quantum mechanical/molecular mechanical (QM/MM) methods to study a series of proteins either whose substrates are too far away from the catalytic center or whose experimentally resolved substrate binding modes cannot explain the observed product distribution. In particular, we describe studies of farnesyl transferase (FTase) where the farnesyl pyrophosphate (FPP) substrate is ∼8 Å from the zinc-bound peptide in the active site of FTase. Using MD and QM/MM studies, we explain how the FPP substrate spans the gulf between it and the active site, and we have elucidated the nature of the transition state (TS) and offered an alternate explanation of experimentally observed kinetic isotope effects (KIEs). Our second story focuses on the nature of substrate dynamics in the aromatic prenyltransferase (APTase) protein NphB and how substrate dynamics affects the observed product distribution. Through the examples chosen we show the power of MD and QM/MM methods to provide unique insights into how protein substrate dynamics affects catalytic efficiency. We also illustrate how complex these reactions are and highlight the challenges faced when attempting to design de novo catalysts. While the methods used in our previous studies provided useful insights, several clear challenges still remain. In particular, we have utilized a semiempirical QM model (self-consistent charge density functional tight binding, SCC-DFTB) in our QM/MM studies since the problems we were addressing required extensive sampling. For the problems illustrated, this approach performed admirably (we estimate for these systems an uncertainty of ∼2 kcal/mol), but it is still a semiempirical model, and studies of this type would benefit greatly from more accurate ab initio or DFT models. However, the challenge with these methods is to reach the level of sampling needed to study systems where large conformational changes happen in the many nanoseconds to microsecond time regimes. Hence, how to couple expensive and accurate QM methods with sophisticated sampling algorithms is an important future challenge especially when large-scale studies of catalyst design become of interest. The use of MD and QM/MM models to elucidate enzyme catalytic pathways and to design novel catalytic agents is in its infancy but shows tremendous promise. While this Account summarizes where we have been, we also discuss briefly future directions that improve our fundamental ability to understand enzyme catalysis.