2016
DOI: 10.1016/j.bpj.2016.09.045
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Hidden Markov Modeling with Detailed Balance and Its Application to Single Protein Folding

Abstract: Hidden Markov modeling (HMM) has revolutionized kinetic studies of macromolecules. However, results from HMM often violate detailed balance when applied to the transitions under thermodynamic equilibrium, and the consequence of such violation has not been well understood. Here, to our knowledge, we developed a new HMM method that satisfies detailed balance (HMM-DB) and optimizes model parameters by gradient search. We used free energy of stable and transition states as independent fitting parameters and consid… Show more

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Cited by 38 publications
(49 citation statements)
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References 45 publications
(109 reference statements)
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“…We found that the t-SNARE complex folded and unfolded among states 2, 3, and 4 with distinct average extensions. We analyzed the extension trajectories using three-state hidden-Markov modeling (HMM) (8,26,27), which revealed idealized extension transitions ( Fig. 2A) and best-fit model parameters, including state probabilities and transition rates (Fig.…”
Section: Resultsmentioning
confidence: 99%
See 2 more Smart Citations
“…We found that the t-SNARE complex folded and unfolded among states 2, 3, and 4 with distinct average extensions. We analyzed the extension trajectories using three-state hidden-Markov modeling (HMM) (8,26,27), which revealed idealized extension transitions ( Fig. 2A) and best-fit model parameters, including state probabilities and transition rates (Fig.…”
Section: Resultsmentioning
confidence: 99%
“…Our methods are described in detail elsewhere (9,26,27). Briefly, the extension trajectories were analyzed by two-or three-state HMM, which yielded the probability, extension, force, lifetime, and transition rates for each state (27). To relate the experimental measurements to the conformations and energy (or the energy landscape) of the t-SNARE complex at zero force, we constructed a structural model for t-SNARE folding (26).…”
Section: Methodsmentioning
confidence: 99%
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“…Hidden-Markov modeling (HMM) and derivations of the energy and kinetics at zero force Methods and algorithms of HMM and energy and structural modeling are detailed elsewhere (Zhang et al, 2016b;Jiao et al, 2017;Rebane et al, 2016). The MATLAB codes used for these calculations can be found in Ref.…”
Section: Membrane Coating On Silica Beadsmentioning
confidence: 99%
“…To quantify the kinetics of C2 domain-membrane binding, we fit the extension trajectories using two-state hidden-Markov modeling (Zhang et al, 2016b) ( these observations are characteristic of two-state transitions (Bustamante et al, 2004;Rebane et al, 2016). We simultaneously fit unbinding probabilities, transition rates, and extension changes using a nonlinear model similar to the force-dependent protein folding transitions (Rebane et al, 2016), which included effects of the polypeptide linker and the DNA handles on the observed binding and unbinding transitions (see Materials and methods).…”
Section: Energetics and Kinetics Of C2 Domain-membrane Bindingmentioning
confidence: 99%