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Decoding Single Molecule Time Traces with Dynamic Disorder

Author

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  • Wonseok Hwang
  • Il-Buem Lee
  • Seok-Cheol Hong
  • Changbong Hyeon

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 Summary: We have developed a new algorithm to better decode single molecule data with dynamic disorder. Our new algorithm, which represents a substantial improvement over other methodologies, can detect the presence of dynamic disorder in each trajectory and quantify the kinetic characteristics of underlying energy landscape. As a model system, we applied our algorithm to the single molecule FRET time traces of H-DNA. While duplex-triplex transitions of H-DNA are conventionally interpreted in terms of two-state kinetics, slowly varying dynamic patterns corresponding to hidden internal states can also be identified from the individual time traces. Our algorithm reveals that at least 4 distinct internal states are required to correctly interpret the data.

Suggested Citation

  • Wonseok Hwang & Il-Buem Lee & Seok-Cheol Hong & Changbong Hyeon, 2016. "Decoding Single Molecule Time Traces with Dynamic Disorder," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-29, December.
    • Handle: RePEc:plo:pcbi00:1005286
    DOI: 10.1371/journal.pcbi.1005286
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    References listed on IDEAS

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    1. Sergey V. Solomatin & Max Greenfeld & Steven Chu & Daniel Herschlag, 2010. "Multiple native states reveal persistent ruggedness of an RNA folding landscape," Nature, Nature, vol. 463(7281), pages 681-684, February.
    2. Bian Liu & Ronald J. Baskin & Stephen C. Kowalczykowski, 2013. "DNA unwinding heterogeneity by RecBCD results from static molecules able to equilibrate," Nature, Nature, vol. 500(7463), pages 482-485, August.
    3. Hari Bhaskaran & Rick Russell, 2007. "Kinetic redistribution of native and misfolded RNAs by a DEAD-box chaperone," Nature, Nature, vol. 449(7165), pages 1014-1018, October.
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