Selecting DFT methods for use in optimizations of enzyme active sites: applications to ONIOM treatments of DNA glycosylases

Abstract

When using a hybrid methodology to treat an enzymatic reaction, many factors contribute to selecting the method for the high-level region, which can be complicated by the presence of dispersion-driven interactions such as π–π stacking. In addition, the proper treatment of the reaction center often requires a large number of heavy atoms to be included in the high-level region, precluding the use of ab initio methods such as MP2 as well as large basis sets, in the optimization step. In the present work, popular DFT methods were tested to identify an appropriate functional for treating the high-level region in ONIOM optimizations of reactions catalyzed by nonmetalloenzymes. Eight different DFT methods (B3LYP, B97-2, MPW1K, MPWB1K, BB1K, B1B95, M06-2X, and ωB97X-D) in combination with four double-ζ quality Pople basis sets were tested for their ability to optimize noncovalent interactions (hydrogen bonding and π–π) and characterize reactions (proton transfer, SN2 hydrolysis, and unimolecular cleavage). Although the primary focus of this study is accurate structure determination, energetics were also examined at both the optimization level of theory, and with triple-ζ quality basis set and select (M06-2X or ωB97X-D) methods. If dispersion-driven interactions exist within the active site, then MPWB1K/6-31G(d,p) or M06-2X/6-31+G(d,p) are recommended for the optimization step with subsequent triple-ζ quality single-point energies. However, since dispersion-corrected functionals (M06-2X and ωB97X-D) generally require diffuse functions to yield appropriate geometries, the possible size of the high-level region is greatly limited with these methods. In contrast, if the model is large enough to recover steric constraints on π–π interactions, then B3LYP with a small basis set performs comparatively well for the optimization step and is significantly less computationally expensive. Interestingly, the functionals that afford the best geometries often do not yield the best energetics, which emphasizes the importance of structural benchmark studies.