Decoding Single Molecule Time Traces with Dynamic Disorder

Wonseok Hwang, Il Buem Lee, Seok Cheol Hong, Changbong Hyeon

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Single molecule time trajectories of biomolecules provide glimpses into complex folding landscapes that are difficult to visualize using conventional ensemble measurements. Recent experiments and theoretical analyses have highlighted dynamic disorder in certain classes of biomolecules, whose dynamic pattern of conformational transitions is affected by slower transition dynamics of internal state hidden in a low dimensional projection. A systematic means to analyze such data is, however, currently not well developed. Here we report a new algorithm—Variational Bayes-double chain Markov model (VB-DCMM)—to analyze single molecule time trajectories that display dynamic disorder. The proposed analysis employing VB-DCMM allows us to detect the presence of dynamic disorder, if any, in each trajectory, identify the number of internal states, and estimate transition rates between the internal states as well as the rates of conformational transition within each internal state. Applying VB-DCMM algorithm to single molecule FRET data of H-DNA in 100 mM-Na+solution, followed by data clustering, we show that at least 6 kinetic paths linking 4 distinct internal states are required to correctly interpret the duplex-triplex transitions of H-DNA.

Original languageEnglish
Article numbere1005286
JournalPLoS Computational Biology
Volume12
Issue number12
DOIs
Publication statusPublished - 2016 Dec 1

Fingerprint

Decoding
Disorder
trajectory
Trace
Internal
Markov Chains
Molecules
trajectories
Biomolecules
Cluster Analysis
Trajectories
DNA
Trajectory
duplex
Markov chain
folding
Markov Chain Model
Data Clustering
Bayes
kinetics

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modelling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

Cite this

Decoding Single Molecule Time Traces with Dynamic Disorder. / Hwang, Wonseok; Lee, Il Buem; Hong, Seok Cheol; Hyeon, Changbong.

In: PLoS Computational Biology, Vol. 12, No. 12, e1005286, 01.12.2016.

Research output: Contribution to journalArticle

Hwang, Wonseok ; Lee, Il Buem ; Hong, Seok Cheol ; Hyeon, Changbong. / Decoding Single Molecule Time Traces with Dynamic Disorder. In: PLoS Computational Biology. 2016 ; Vol. 12, No. 12.
@article{ded3fdce35a14009b4341b6100022f51,
title = "Decoding Single Molecule Time Traces with Dynamic Disorder",
abstract = "Single molecule time trajectories of biomolecules provide glimpses into complex folding landscapes that are difficult to visualize using conventional ensemble measurements. Recent experiments and theoretical analyses have highlighted dynamic disorder in certain classes of biomolecules, whose dynamic pattern of conformational transitions is affected by slower transition dynamics of internal state hidden in a low dimensional projection. A systematic means to analyze such data is, however, currently not well developed. Here we report a new algorithm—Variational Bayes-double chain Markov model (VB-DCMM)—to analyze single molecule time trajectories that display dynamic disorder. The proposed analysis employing VB-DCMM allows us to detect the presence of dynamic disorder, if any, in each trajectory, identify the number of internal states, and estimate transition rates between the internal states as well as the rates of conformational transition within each internal state. Applying VB-DCMM algorithm to single molecule FRET data of H-DNA in 100 mM-Na+solution, followed by data clustering, we show that at least 6 kinetic paths linking 4 distinct internal states are required to correctly interpret the duplex-triplex transitions of H-DNA.",
author = "Wonseok Hwang and Lee, {Il Buem} and Hong, {Seok Cheol} and Changbong Hyeon",
year = "2016",
month = "12",
day = "1",
doi = "10.1371/journal.pcbi.1005286",
language = "English",
volume = "12",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "12",

}

TY - JOUR

T1 - Decoding Single Molecule Time Traces with Dynamic Disorder

AU - Hwang, Wonseok

AU - Lee, Il Buem

AU - Hong, Seok Cheol

AU - Hyeon, Changbong

PY - 2016/12/1

Y1 - 2016/12/1

N2 - Single molecule time trajectories of biomolecules provide glimpses into complex folding landscapes that are difficult to visualize using conventional ensemble measurements. Recent experiments and theoretical analyses have highlighted dynamic disorder in certain classes of biomolecules, whose dynamic pattern of conformational transitions is affected by slower transition dynamics of internal state hidden in a low dimensional projection. A systematic means to analyze such data is, however, currently not well developed. Here we report a new algorithm—Variational Bayes-double chain Markov model (VB-DCMM)—to analyze single molecule time trajectories that display dynamic disorder. The proposed analysis employing VB-DCMM allows us to detect the presence of dynamic disorder, if any, in each trajectory, identify the number of internal states, and estimate transition rates between the internal states as well as the rates of conformational transition within each internal state. Applying VB-DCMM algorithm to single molecule FRET data of H-DNA in 100 mM-Na+solution, followed by data clustering, we show that at least 6 kinetic paths linking 4 distinct internal states are required to correctly interpret the duplex-triplex transitions of H-DNA.

AB - Single molecule time trajectories of biomolecules provide glimpses into complex folding landscapes that are difficult to visualize using conventional ensemble measurements. Recent experiments and theoretical analyses have highlighted dynamic disorder in certain classes of biomolecules, whose dynamic pattern of conformational transitions is affected by slower transition dynamics of internal state hidden in a low dimensional projection. A systematic means to analyze such data is, however, currently not well developed. Here we report a new algorithm—Variational Bayes-double chain Markov model (VB-DCMM)—to analyze single molecule time trajectories that display dynamic disorder. The proposed analysis employing VB-DCMM allows us to detect the presence of dynamic disorder, if any, in each trajectory, identify the number of internal states, and estimate transition rates between the internal states as well as the rates of conformational transition within each internal state. Applying VB-DCMM algorithm to single molecule FRET data of H-DNA in 100 mM-Na+solution, followed by data clustering, we show that at least 6 kinetic paths linking 4 distinct internal states are required to correctly interpret the duplex-triplex transitions of H-DNA.

UR - http://www.scopus.com/inward/record.url?scp=85007569589&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85007569589&partnerID=8YFLogxK

U2 - 10.1371/journal.pcbi.1005286

DO - 10.1371/journal.pcbi.1005286

M3 - Article

VL - 12

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 12

M1 - e1005286

ER -