Decoding Single Molecule Time Traces with Dynamic Disorder
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hwang, Wonseok | - |
dc.contributor.author | Lee, Il-Buem | - |
dc.contributor.author | Hong, Seok-Cheol | - |
dc.contributor.author | Hyeon, Changbong | - |
dc.date.accessioned | 2021-09-03T16:19:00Z | - |
dc.date.available | 2021-09-03T16:19:00Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2016-12 | - |
dc.identifier.issn | 1553-734X | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/86684 | - |
dc.description.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 D Variational Bayes-double chain Markov model (VB-DCMM) D 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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | PUBLIC LIBRARY SCIENCE | - |
dc.subject | AGGREGATED MARKOV-MODELS | - |
dc.subject | ENZYME MOLECULES | - |
dc.subject | TRANSITION RATES | - |
dc.subject | RNA DYNAMICS | - |
dc.subject | CHANNEL | - |
dc.subject | STATES | - |
dc.subject | KINETICS | - |
dc.subject | HETEROGENEITY | - |
dc.subject | MCMC | - |
dc.subject | TRAJECTORIES | - |
dc.title | Decoding Single Molecule Time Traces with Dynamic Disorder | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hong, Seok-Cheol | - |
dc.identifier.doi | 10.1371/journal.pcbi.1005286 | - |
dc.identifier.scopusid | 2-s2.0-85007569589 | - |
dc.identifier.wosid | 000392126000048 | - |
dc.identifier.bibliographicCitation | PLOS COMPUTATIONAL BIOLOGY, v.12, no.12 | - |
dc.relation.isPartOf | PLOS COMPUTATIONAL BIOLOGY | - |
dc.citation.title | PLOS COMPUTATIONAL BIOLOGY | - |
dc.citation.volume | 12 | - |
dc.citation.number | 12 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Biochemical Research Methods | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.subject.keywordPlus | AGGREGATED MARKOV-MODELS | - |
dc.subject.keywordPlus | ENZYME MOLECULES | - |
dc.subject.keywordPlus | TRANSITION RATES | - |
dc.subject.keywordPlus | RNA DYNAMICS | - |
dc.subject.keywordPlus | CHANNEL | - |
dc.subject.keywordPlus | STATES | - |
dc.subject.keywordPlus | KINETICS | - |
dc.subject.keywordPlus | HETEROGENEITY | - |
dc.subject.keywordPlus | MCMC | - |
dc.subject.keywordPlus | TRAJECTORIES | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.