Decomposing bulk signals to reveal hidden information in processive enzyme reactions: A case study in mRNA translation

Abstract

Processive enzymes, such as polymerases or ribosomes, are often studied in en-semble or bulk experiments through the monitoring of time-dependent signals, such as fluorescence time traces. However, ensemble signals are a superposition of the time traces of all molecules in the reaction and can exhibit less distinct features than individual single-molecule signals due to the stochasticity of biomolecular processes. Here, we demonstrate that under certain conditions, bulk signals from processive reactions can still be decomposed to reveal hidden information about individual reaction steps. Using mRNA translation as a case study for processive biochemical reactions, we explore the limits of least-squares approaches for ensemble fluorescence signal decomposition. Specifically, we show that decomposing a noisy ensemble signal generated by the translation of mRNAs containing more than a few codons represents an ill-posed problem, which can be addressed through Tikhonov regularization. Our findings can help to increase the information content extracted from bulk experiments, thereby expanding the range of these time- and cost-efficient methods.