Computational Approaches: An Underutilized Tool in the Quest to Elucidate Radical SAM Dynamics
Abstract
Enzymes are biological catalysts whose dynamics enable their reactivity. Visualizing conformational changes, in particular, is technically challenging, and little is known about these crucial atomic motions. This is especially problematic for understanding the functional diversity associated with the radical S-adenosyl-L-methionine (SAM) superfamily whose members share a common radical mechanism but ultimately catalyze a broad range of challenging reactions. Computational chemistry approaches provide a readily accessible alternative to exploring the time-resolved behavior of these enzymes that is not limited by experimental logistics. Here, we review the application of molecular docking, molecular dynamics, and density functional theory, as well as hybrid quantum mechanics/molecular mechanics methods to the study of these enzymes, with a focus on understanding the mechanistic dynamics associated with turnover.
How strong is the evidence?
Evidence Hub reads this as a review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
Read the paper
Related studies
- Quantum dots: synthesis, bioapplications, and toxicity
- A Guide to In Silico Drug Design
- In silico toxicology protocols
- Automated in Silico Design of Homogeneous Catalysts
- In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications
- mRNA codon optimization with quantum computers
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.