The individual and combined benefits of differentnon-equilibrium proofreading mechanisms
Mohr, A. A.; Busiello, D. M.; Zamuner, S.; De Los Rios, P.
Show abstract
Genome duplication, transcription and translation are among many crucial cellular processes that need to be performed with high fidelity. However those extremely low error rates cannot be explained with simple equilibrium thermodynamic considerations. They instead require considering irreversible, energy consuming reactions in the overall mechanism. We develop here a model of substrates selection comprising energy consuming steps and which aims at selecting right substrates among wrong ones. With this model, we investigate the impact of energy consumption on the accuracy and the speed of the selection, as well as different selection strategies. The model presented here encompasses the classic kinetic proofreading scheme and a different mechanism whereby the rates of the energy consuming step are modulated by the nature of the substrate. We show that, in our framework, the fastest and most accurate selection strategy relies on a combination of both mechanisms. A structurally and biochemically informed coarse-grained description of real biological processes such as DNA replication and protein translation, traditionally used as examples of kinetic proofreading at work, shows that, as a matter of fact, a combination of both mechanisms explored here is exploited.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Biomathematical enzyme kinetics model of prebiotic autocatalytic RNA networks: degenerating parasite-specific hyperparasite catalysts confer parasite resistance and herald the birth of molecular immunity 97%
- Reliable ligand discrimination in stochastic multistep kinetic proofreading: First passage time vs. product counting strategies 96%
- Modular assembly of dynamic models in systems biology 96%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.