Decomposing bulk signals to reveal hidden information in processive enzyme reactions

A case study in mRNA translation

authored by: Nadin Haase, Wolf Holtkamp, Simon Christ, Dag Heinemann, Marina V. Rodnina, Sophia Rudorf
Abstract: Processive enzymes like polymerases or ribosomes are often studied in bulk experiments by monitoring time-dependent signals, such as fluorescence time traces. However, due to biomolecular process stochasticity, ensemble signals may lack the distinct features of single-molecule signals. Here, we demonstrate that, under certain conditions, bulk signals from processive reactions can be decomposed to unveil hidden information about individual reaction steps. Using mRNA translation as a case study, we show that decomposing a noisy ensemble signal generated by the translation of mRNAs with more than a few codons is an ill-posed problem, addressable through Tikhonov regularization. We apply our method to the fluorescence signatures of in-vitro translated LepB mRNA and determine codon-position dependent translation rates and corresponding state-specific fluorescence intensities. We find a significant change in fluorescence intensity after the fourth and the fifth peptide bond formation, and show that both codon position and encoded amino acid have an effect on the elongation rate. This demonstrates that our approach enhances the information content extracted from bulk experiments, thereby expanding the range of these time- and cost-efficient methods.
Organisation(s): Institute of Cell Biology and Biophysics
Department of Computational Biology
Institute of Horticultural Production Systems
External Organisation(s): Max-Planck Institute for Multidisciplinary Sciences
Paul-Ehrlich-Institut (PEI) - Federal Institute for Vaccines and Biomedicines
Type: Article
Journal: PLoS Computational Biology
Volume: 20
ISSN: 1553-734X
Publication date: 05.03.2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Ecology, Evolution, Behavior and Systematics, Modelling and Simulation, Ecology, Molecular Biology, Genetics, Cellular and Molecular Neuroscience, Computational Theory and Mathematics
Electronic version(s): https://doi.org/10.1101/2023.05.17.541147 (Access: Open)
https://doi.org/10.1371/journal.pcbi.1011918 (Access: Open)

BibTeX

@article{ea949df36332402dab7361657785b400,
title = "Decomposing bulk signals to reveal hidden information in processive enzyme reactions: A case study in mRNA translation",
abstract = "Processive enzymes like polymerases or ribosomes are often studied in bulk experiments by monitoring time-dependent signals, such as fluorescence time traces. However, due to biomolecular process stochasticity, ensemble signals may lack the distinct features of single-molecule signals. Here, we demonstrate that, under certain conditions, bulk signals from processive reactions can be decomposed to unveil hidden information about individual reaction steps. Using mRNA translation as a case study, we show that decomposing a noisy ensemble signal generated by the translation of mRNAs with more than a few codons is an ill-posed problem, addressable through Tikhonov regularization. We apply our method to the fluorescence signatures of in-vitro translated LepB mRNA and determine codon-position dependent translation rates and corresponding state-specific fluorescence intensities. We find a significant change in fluorescence intensity after the fourth and the fifth peptide bond formation, and show that both codon position and encoded amino acid have an effect on the elongation rate. This demonstrates that our approach enhances the information content extracted from bulk experiments, thereby expanding the range of these time- and cost-efficient methods.",
author = "Nadin Haase and Wolf Holtkamp and Simon Christ and Dag Heinemann and Rodnina, {Marina V.} and Sophia Rudorf",
note = "Publisher Copyright: Copyright: {\textcopyright} 2024 Haase et al.",
year = "2024",
month = mar,
day = "5",
doi = "10.1101/2023.05.17.541147",
language = "English",
volume = "20",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "3",
}