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Nonparametric Bayesian inference for meta-stable conformational dynamics

Köhs, Lukas ; Kukovetz, Kerri ; Rauh, Oliver ; Koeppl, Heinz (2022)
Nonparametric Bayesian inference for meta-stable conformational dynamics.
In: Physical Biology, 2022, 19 (5)
doi: 10.26083/tuprints-00022096
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Nonparametric Bayesian inference for meta-stable conformational dynamics
Language: English
Date: 31 August 2022
Place of Publication: Darmstadt
Year of primary publication: 2022
Publisher: IOP Publishing
Journal or Publication Title: Physical Biology
Volume of the journal: 19
Issue Number: 5
Collation: 15 Seiten
DOI: 10.26083/tuprints-00022096
Corresponding Links:
Origin: Secondary publication DeepGreen

Analyses of structural dynamics of biomolecules hold great promise to deepen the understanding of and ability to construct complex molecular systems. To this end, both experimental and computational means are available, such as fluorescence quenching experiments or molecular dynamics simulations, respectively. We argue that while seemingly disparate, both fields of study have to deal with the same type of data about the same underlying phenomenon of conformational switching. Two central challenges typically arise in both contexts: (i) the amount of obtained data is large, and (ii) it is often unknown how many distinct molecular states underlie these data. In this study, we build on the established idea of Markov state modeling and propose a generative, Bayesian nonparametric hidden Markov state model that addresses these challenges. Utilizing hierarchical Dirichlet processes, we treat different meta-stable molecule conformations as distinct Markov states, the number of which we then do not have to set a priori. In contrast to existing approaches to both experimental as well as simulation data that are based on the same idea, we leverage a mean-field variational inference approach, enabling scalable inference on large amounts of data. Furthermore, we specify the model also for the important case of angular data, which however proves to be computationally intractable. Addressing this issue, we propose a computationally tractable approximation to the angular model. We demonstrate the method on synthetic ground truth data and apply it to known benchmark problems as well as electrophysiological experimental data from a conformation-switching ion channel to highlight its practical utility.

Uncontrolled Keywords: Bayesian nonparametrics, conformational switching, molecular dynamics, variational inference
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-220969
Classification DDC: 500 Science and mathematics > 530 Physics
500 Science and mathematics > 570 Life sciences, biology
Divisions: 10 Department of Biology > Plant Membrane Biophyscis (20.12.23 renamed in Biology of Algae and Protozoa)
Interdisziplinäre Forschungsprojekte > Centre for Synthetic Biology
Date Deposited: 31 Aug 2022 11:05
Last Modified: 06 Dec 2023 08:36
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/22096
PPN: 498752119
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