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Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.DNA.29.5
URN: urn:nbn:de:0030-drops-187889
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18788/
Lovrod, Jordan ;
Beronov, Boyan ;
Zhang, Chenwei ;
Winfree, Erik ;
Condon, Anne
Revisiting Hybridization Kinetics with Improved Elementary Step Simulation
Abstract
Nucleic acid strands, which react by forming and breaking Watson-Crick base pairs, can be designed to form complex nanoscale structures or devices. Controlling such systems requires accurate predictions of the reaction rate and of the folding pathways of interacting strands. Simulators such as Multistrand model these kinetic properties using continuous-time Markov chains (CTMCs), whose states and transitions correspond to secondary structures and elementary base pair changes, respectively. The transient dynamics of a CTMC are determined by a kinetic model, which assigns transition rates to pairs of states, and the rate of a reaction can be estimated using the mean first passage time (MFPT) of its CTMC. However, use of Multistrand is limited by its slow runtime, particularly on rare events, and the quality of its rate predictions is compromised by a poorly-calibrated and simplistic kinetic model. The former limitation can be addressed by constructing truncated CTMCs, which only include a small subset of states and transitions, selected either manually or through simulation. As a first step to address the latter limitation, Bayesian posterior inference in an Arrhenius-type kinetic model was performed in earlier work, using a small experimental dataset of DNA reaction rates and a fixed set of manually truncated CTMCs, which we refer to as Assumed Pathway (AP) state spaces. In this work we extend this approach, by introducing a new prior model that is directly motivated by the physical meaning of the parameters and that is compatible with experimental measurements of elementary rates, and by using a larger dataset of 1105 reactions as well as larger truncated state spaces obtained from the recently introduced stochastic Pathway Elaboration (PE) method. We assess the quality of the resulting posterior distribution over kinetic parameters, as well as the quality of the posterior reaction rates predicted using AP and PE state spaces. Finally, we use the newly parameterised PE state spaces and Multistrand simulations to investigate the strong variation of helix hybridization reaction rates in a dataset of Hata et al. While we find strong evidence for the nucleation-zippering model of hybridization, in the classical sense that the rate-limiting phase is composed of elementary steps reaching a small "nucleus" of critical stability, the strongly sequence-dependent structure of the trajectory ensemble up to nucleation appears to be much richer than assumed in the model by Hata et al. In particular, rather than being dominated by the collision probability of nucleation sites, the trajectory segment between first binding and nucleation tends to visit numerous secondary structures involving misnucleation and hairpins, and has a sizeable effect on the probability of overcoming the nucleation barrier.
BibTeX - Entry
@InProceedings{lovrod_et_al:LIPIcs.DNA.29.5,
author = {Lovrod, Jordan and Beronov, Boyan and Zhang, Chenwei and Winfree, Erik and Condon, Anne},
title = {{Revisiting Hybridization Kinetics with Improved Elementary Step Simulation}},
booktitle = {29th International Conference on DNA Computing and Molecular Programming (DNA 29)},
pages = {5:1--5:24},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-297-6},
ISSN = {1868-8969},
year = {2023},
volume = {276},
editor = {Chen, Ho-Lin and Evans, Constantine G.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2023/18788},
URN = {urn:nbn:de:0030-drops-187889},
doi = {10.4230/LIPIcs.DNA.29.5},
annote = {Keywords: DNA reaction kinetics, kinetic model calibration, simulation-based Bayesian inference, continuous-time Markov chains}
}
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Keywords: |
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DNA reaction kinetics, kinetic model calibration, simulation-based Bayesian inference, continuous-time Markov chains |
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Collection: |
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29th International Conference on DNA Computing and Molecular Programming (DNA 29) |
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Issue Date: |
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2023 |
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Date of publication: |
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04.09.2023 |
