Ramin Golestanian FRS

Abstract:

Enzymes are nano-scale machines that have evolved to drive chemical reactions out of equilibrium in the right place at the right time. Given the complexity and specificity of enzymatic function, the bottom-up design of enzymes presents a daunting task that is far more challenging than making passive molecules with specific binding affinities or building nano-scale mechanically active devices. We present a thermodynamically consistent model for the operation of such a fueled enzyme, which uses the energy from a favorable reaction to undergo non-equilibrium conformational changes that in turn catalyze a chemical reaction on an attached substrate molecule. We show that enzymatic function can emerge through a bifurcation upon appropriate implementation of momentum conservation on the effective reaction coordinates of the low-dimensional description of the enzyme, and thanks to a generically present dissipative coupling. Our results can complement the recently developed strategies for de novo enzyme design based on machine learning approaches