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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
Artikel, Bibliographie

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Kurzbeschreibung (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.

Typ des Eintrags: Artikel
Erschienen: 2022
Autor(en): Köhs, Lukas ; Kukovetz, Kerri ; Rauh, Oliver ; Koeppl, Heinz
Art des Eintrags: Bibliographie
Titel: Nonparametric Bayesian inference for meta-stable conformational dynamics
Sprache: Englisch
Publikationsjahr: 2022
Ort: Darmstadt
Verlag: IOP Publishing
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Physical Biology
Jahrgang/Volume einer Zeitschrift: 19
(Heft-)Nummer: 5
Kollation: 15 Seiten
DOI: 10.1088/1478-3975/ac885e
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Kurzbeschreibung (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.

Freie Schlagworte: Bayesian nonparametrics, conformational switching, molecular dynamics, variational inference
Sachgruppe der Dewey Dezimalklassifikatin (DDC): 500 Naturwissenschaften und Mathematik > 530 Physik
500 Naturwissenschaften und Mathematik > 570 Biowissenschaften, Biologie
Fachbereich(e)/-gebiet(e): 10 Fachbereich Biologie
10 Fachbereich Biologie > Plant Membrane Biophyscis (am 20.12.23 umbenannt in Biologie der Algen und Protozoen)
18 Fachbereich Elektrotechnik und Informationstechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Bioinspirierte Kommunikationssysteme
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Self-Organizing Systems Lab
Interdisziplinäre Forschungsprojekte
Interdisziplinäre Forschungsprojekte > Centre for Synthetic Biology
Hinterlegungsdatum: 06 Dez 2023 08:37
Letzte Änderung: 23 Feb 2024 11:31
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