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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, 19 (5)
doi: 10.1088/1478-3975/ac885e
Article, Bibliographie

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Abstract

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.

Item Type: Article
Erschienen: 2022
Creators: Köhs, Lukas ; Kukovetz, Kerri ; Rauh, Oliver ; Koeppl, Heinz
Type of entry: Bibliographie
Title: Nonparametric Bayesian inference for meta-stable conformational dynamics
Language: English
Date: 2022
Place of Publication: Darmstadt
Publisher: IOP Publishing
Journal or Publication Title: Physical Biology
Volume of the journal: 19
Issue Number: 5
Collation: 15 Seiten
DOI: 10.1088/1478-3975/ac885e
Corresponding Links:
Abstract:

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
Classification DDC: 500 Science and mathematics > 530 Physics
500 Science and mathematics > 570 Life sciences, biology
Divisions: 10 Department of Biology
10 Department of Biology > Plant Membrane Biophyscis (20.12.23 renamed in Biology of Algae and Protozoa)
18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
18 Department of Electrical Engineering and Information Technology > Self-Organizing Systems Lab
Interdisziplinäre Forschungsprojekte
Interdisziplinäre Forschungsprojekte > Centre for Synthetic Biology
Date Deposited: 06 Dec 2023 08:37
Last Modified: 23 Feb 2024 11:31
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