Biology subjects

Urbakh, M.

Publications and source records attributed to Urbakh, M..

3 recordsLinked to original sources

bioRxiv2025

Inferring binding rates from enzymatic turnover time statistics

We present a method to extract enzyme-substrate binding rates from observations of enzymatic turnover times. Our approach involves segregating the short-time statistics of the turnover time distribution and inferring from it the binding rate in a general and robust manner. Beyond determining binding rates, the approach developed herein also allows us to infer: (i) the fertile collision probability p, i.e., the probability that a collision between an enzyme and a substrate will result in the formation of an enzyme-substrate complex; and (ii) the fertile catalysis probability{phi} cat, i.e., the probability that the enzyme-substrate complex will lead to the formation of a product. It has long been known that p{phi}cat << 1, indicating that most enzymes operate far from maximal efficiency, yet separating the contributions of fertile binding and catalysis was not possible in lieu of direct binding rate measurements. Our method overcomes this limitation by enabling precise inference of binding rates from turnover times, which in turn opens the door for a more detailed understanding of enzymatic efficiency.

biophysics↗

bioRxiv2024

High-order Michaelis-Menten equations allow inference of hidden kinetic parameters in enzyme catalysis

Single-molecule measurements provide a platform for investigating the dynamical properties of enzymatic reactions. To this end, the single-molecule Michaelis-Menten equation was instrumental as it asserts that the first moment of the enzymatic turnover time depends linearly on the reciprocal of the substrate concentration. This, in turn, provides robust and convenient means to determine the maximal turnover rate and the Michaelis-Menten constant. Yet, the information provided by these parameters is incomplete and does not allow full characterization of enzyme kinetics at the single molecule level. Here we show that the missing kinetic information can be accessed via a set of high-order Michaelis-Menten equations that we derive. These equations capture universal linear relations between the reciprocal of the substrate concentration and distinguished combinations of turnover time moments, essentially generalizing the Michaelis-Menten equation to moments of any order. We demonstrate how key observables such as the lifetime of the enzyme-substrate complex, the rate of substrate-enzyme binding, and the probability of successful product formation, can all be inferred using these high-order Michaelis-Menten equations.

biophysics↗

bioRxiv2016

Single-enzyme approach predicts natural emergence of inhibitor-activator duality

The classical theory of enzymatic inhibition aims to quantitatively describe the effect of certain molecules--called inhibitors--on the progression of enzymatic reactions, but growing signs indicate that it must be revised to keep pace with the single-molecule revolution that is sweeping through the sciences. Here, we take the single enzyme perspective and rebuild the theory of enzymatic inhibition from the bottom up. We find that accounting for multi-conformational enzyme structure and intrinsic randomness cannot undermine the validity of classical results in the case of competitive inhibition; but that it should strongly change our view on the uncompetitive and mixed modes of inhibition. There, stochastic fluctuations on the single-enzyme level could give rise to inhibitor-activator duality--a phenomenon in which, under some conditions, the introduction of a molecule whose binding shuts down enzymatic catalysis will counter intuitively work to facilitate product formation. We state--in terms of experimentally measurable quantities--a mathematical condition for the emergence of inhibitor-activator duality, and propose that it could explain why certain molecules that act as inhibitors when substrate concentrations are high elicit a non-monotonic dose response when substrate concentrations are low. The fundamental and practical implications of our findings are thoroughly discussed.

biophysics↗