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Anyone who has ever taken an introductory biochemistry course probably learned the Michaelis-Menten equation in class and then tested it in the laboratory with a classic enzymatic assay. First discovered in 1913, this equation has been rigorously applied and has proven to accurately describe the kinetic behavior of ensembles of thousands of different enzymes.

With recent advances in protein dynamics technologies, researchers have discovered that proteins are not static entities but are actually in constant motion. For enzymes, these fluctuations can result in active site conformational changes, potentially hindering or abetting catalysis. Armed with this understanding, Sunney Xie and lead authors Brian English and Wei Min of Harvard University set out to answer the question, 'Does the Michaelis-Menten equation hold true for single enzyme molecules?'

Xie and coworkers first had to modify the Michaelis-Menten equation to describe stochastic single-molecule behavior, and then carefully construct an experiment to measure the catalytic turnovers of their test enzyme, β-galactosidase. Though many groups including Xie's had attempted to use fluorescence to study single enzyme molecules, this new experiment took the technique to the next level, as Xie explains, “The key was to use a fluorescent product instead of a fluorescent substrate. The fluorescent product allows continuous replenishing; it is generated and detected in the probe volume and then it quickly diffuses away.” They monitored the turnover of a single immobilized β-galactosidase molecule by measuring the photon 'burst' of each product molecule generated from the fluorogenic substrate, resorufin–β-D-galactopyranoside. By constructing and comparing Lineweaver-Burke linear plots of the traditional ensemble data and the single-molecule data, they found excellent agreement, demonstrating that the Michaelis-Menten equation holds true for single enzyme molecules.

Yet Xie and coworkers discovered that the enzymatic rate constant is not actually constant, but fluctuates broadly on a single molecule basis. This finding is masked in ensemble-averaged experiments because slower individual enzyme molecules yield a long tail generally ignored in kinetics analyses. “If you only care about 90% of the population, it's not even important,” explains Xie. “But inside a live cell you might only have one or a few copies of a particular enzyme, so then these fluctuations become very important.” Moreover, Xie believes that this work really explains why the classic Michaelis-Menten is so accurate, as he says, “It works so well because it works even in the presence of these fluctuations.”