Mechanistic Insights into SAM-Dependent Methyltransferases: A Review of Computational Approaches

Methylation reactions catalyzed by S-adenosylmethionine (SAM)-dependent methyltransferases are essential to numerous biological functions, including gene expression regulation, epigenetic modifications, and biosynthesis of natural products. Dysregulation of these enzymes is associated with diseases, including cancer and neurodevelopmental disorders, making them attractive drug targets. This review explores the contribution of computational methods, particularly quantum chemical calculations and molecular dynamics (MD) simulations, in elucidating the mechanisms of SAM-dependent methyltransferases. These techniques enable detailed characterization of transition states and reaction pathways, often inaccessible by experimental methods. The review discusses molecular modeling approaches such as the quantum chemical cluster approach (QM-cluster) and hybrid QM/MM methods, emphasizing their applications in studying methyl group transfer, substrate specificity, and the roles of water molecules and metal ions in catalysis. Additionally, dynamic aspects of enzyme function are addressed using classical MD and QM/MM MD simulations. Case studies demonstrate how computational predictions align with experimental data and enable rational design of selective inhibitors and engineered enzymes with altered specificity. Overall, computational chemistry offers a powerful, atomistic view of SAM-dependent methyltransferases, not only complementing experimental studies but also providing a foundation for the design of future experiments in this field.