Convergence in Determining Enzyme Functional Descriptors across Kemp Eliminase Variants

Abstract

Molecular simulations have been extensively employed to accelerate biocatalytic discoveries. Enzyme functional descriptors derived from molecular simulations have been leveraged to guide the search for beneficial enzyme mutants. However, the ideal active-site region size for computing the descriptors over multiple enzyme variants remains untested. Here, we conducted convergence tests for dynamics-derived and electrostatic descriptors on eighteen Kemp eliminase variants across six active-site regions with various boundary distances to the substrate. The tested descriptors include the root-mean-square deviation of the active-site region, the solvent accessible surface area ratio between the substrate and active site, and the projection of the electric field on the breaking C–H bond. All descriptors were evaluated using molecular mechanics methods. To understand the effects of electronic structure, the electric field was also evaluated using quantum mechanics/molecular mechanics methods. The descriptor values were computed for eighteen KE variants combined with six active-site regions. Spearman correlation matrices were used to determine the region size condition under which further expansion of the region boundary does not substantially change the ranking of descriptor values. We observed that protein dynamics-derived descriptors, including RMSDactive_site and SASAratio, converge at a distance cutoff of 5 Å from the substrate. The electrostatic descriptor, EFC–H, converges at 6 Å using molecular mechanics methods and 7 Å using quantum mechanics/molecular mechanics methods. This study serves as a future reference to determine descriptors for predictive modeling of enzyme engineering.