S‐adenosyl methionine (SAM)–dependent methyl transferases (MTases) are a ubiquitous class of enzymes catalyzing dozens of essential life processes. Despite targeting a large space of substrates with diverse intrinsic reactivity, SAM MTases have similar catalytic efficiency. While understanding of MTase mechanism has grown tremendously through the integration of structural characterization, kinetic assays, and multiscale simulations, it remains elusive how these enzymes have evolved to fit the diverse chemical needs of their respective substrates. In this work, we performed a high‐throughput molecular modeling analysis of 91 SAM MTases to better understand how their properties (i.e., electric field [EF] strength and active site volumes) help achieve similar catalytic efficiency toward substrates of different reactivity. We found that EF strengths have largely adjusted to make the target atom a better methyl acceptor. For MTases that target RNA/DNA and histone proteins, our results suggest that EF strength accommodates formal hybridization state and variation in cavity volume trends with diversity of substrate classes. Metal ions in SAM MTases contribute negatively to EF strength for methyl donation and enzyme scaffolds tend to offset these contributions.

S-adenosyl methionine (SAM) -dependent methyl transferases (MTases) are aubiquitous class of enzymes catalyzing dozens of essential life processes. Despite targeting a largespace of substrates with diverse intrinsic reactivity, SAM MTases have similar catalytic efficiency.While understanding of MTase mechanism has grown tremendously through integration ofstructural characterization, kinetic assays, and multiscale simulations, it remains elusive regardinghow these enzymes have evolved to fit the diverse chemical needs of their respective substrates.In this work, we performed a high-throughput computational analysis of 91 SAM MTases to betterunderstand how the properties (i.e., electric field strength and active site volumes) of SAM MTaseshave been evolved to achieve similar catalytic efficiency towards substrates of different reactivity.We found that electric field strengths have largely evolved to make the target atom a better methylacceptor. For MTases that target RNA/DNA- and histone protein, our results suggest that electricfield strength accommodates hybridization state and variation in cavity volume trends withdiversity of substrate classes. Metal ions in SAM MTases contribute negatively to electric fieldstrength for methyl donation and enzyme scaffolds have evolved to offset these contributions.

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.