Dynamic energy conversion in protein catalysis: From brownian motion to enzymatic function.

Abstract

Recent advances in computational biology and experimental techniques reveal that enzymatic catalysis fundamentally depends on proteins' ability to harness thermal energy through conformational fluctuations. Rather than functioning as rigid molecular locks, proteins operate as dynamic machines that continuously sample different structural states, with α-helices and β-sheets acting as sophisticated energy transduction elements that capture Brownian motion and channel it toward productive chemical transformations. Molecular dynamics simulations, combined with machine learning tools such as AlphaFold, demonstrate that these conformational dynamics directly modulate substrate binding affinity and reaction pathway selection, suggesting that proteins actively convert environmental thermal noise into catalytic work rather than merely stabilizing transition states. This dynamic energy conversion paradigm fundamentally reshapes our approach to pharmaceutical design and enzyme engineering by emphasizing the targeting of conformational ensembles rather than static structures, while also raising important questions about the universal applicability of this mechanism across all enzyme classes and the experimental methodologies needed to validate dynamic catalytic models. The shift from viewing proteins as passive structural scaffolds to active energy converters represents a transformative reconceptualization of biological catalysis with far-reaching implications for our understanding of life's molecular machinery.