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 Kemp eliminase variants. 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 with truncated enzyme models and 4 Å using quantum mechanics/molecular mechanics methods with whole enzyme model. This study serves as a future reference to determine descriptors for predictive modeling of enzyme engineering.

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.

Substrate positioning dynamics (SPD), which orients the substrate to a reactive conformation in the active site, is critical in mediating enzyme catalysis. However, given that conformational changes often accompany variations in the enzyme interior electrostatics, it remains unknown whether SPD contains a non-electrostatic component that independently mediates catalysis, or originates primarily from perturbation of enzyme interior electrostatics. This study integrated computational and experimental approaches to investigate the non-electrostatic component of SPD using Kemp eliminase (KE) as a model enzyme. A molecular dynamics-derived descriptor, substrate positioning index (SPI), was used to quantify the impact of protein dynamics on substrate positioning. Using high throughput enzyme modeling, we selected 7 KE variants for kinetic assessment – these variants involved significantly different SPD but similar interior enzyme electrostatics. We observed a valley-shaped, two-segment piecewise linear correlation between the experimentally characterized activation free energies and SPI values. The trend is further validated using previously reported kinetic data. An optimal SPI value, corresponding to the lowest activation free energy, was observed for R154W, a surface mutation located distantly from the active site. Compared to the wild type, R154W involves favorable SPD that increases the proportion of reactive conformations for substrate deprotonation. These results indicate the presence of the non-electrostatic component of SPD, a concrete factor that mediates catalysis by tuning the population of reactive conformation.

Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers can seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins by interacting with a computational machine, similar to how we use Amazon Alexa in these days. The technical foundation of Mutexa has been established through the development of database that integrates enzyme structures with their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of non-electrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and challenges in our endeavor to develop new Mutexa applications that facilitate the selection of beneficial mutants in enzyme engineering.

Substrate positioning dynamics (SPD), which orients the substrate to a reactive conformation in the active site, is critical in mediating enzyme catalysis. However, given that conformational changes often accompany variations in the enzyme interior electrostatics, it remains unknown whether SPD contains a non-electrostatic component that independently mediates catalysis, or originates primarily from perturbation of enzyme interior electrostatics. This study integrated computational and experimental approaches to investigate the non-electrostatic component of SPD using Kemp eliminase (KE) as a model enzyme. A molecular dynamics-derived descriptor, substrate positioning index (SPI), was used to quantify the impact of protein dynamics on substrate positioning. Using high throughput enzyme modeling, we selected 7 KE variants for kinetic assessment – these variants involved significantly different SPD but similar interior enzyme electrostatics. We observed a valley-shaped, two-segment piecewise linear correlation between the experimentally characterized activation free energies and SPI values. The trend is further validated using previously reported kinetic data. An optimal SPI value, corresponding to the lowest activation free energy, was observed for R154W, a surface mutation located distantly from the active site. Compared to the wild type, R154W involves favorable SPD that increases the proportion of reactive conformations for substrate deprotonation. These results indicate the presence of the non-electrostatic component of SPD, a concrete factor that mediates catalysis by tuning the population of reactive conformation.