TY - JOUR
T1 - Extraction of rapid kinetics from smFRET measurements using integrative detectors
AU - Kilic, Zeliha
AU - Sgouralis, Ioannis
AU - Heo, Wooseok
AU - Ishii, Kunihiko
AU - Tahara, Tahei
AU - Pressé, Steve
N1 - Funding Information:
This work was supported in part by Japan Science and Technology Agency (JST) ACT-I under Grant JPMJPR18UU, in part by JSPS KAKENHI under Grant 20H04249 and Grant 20H04208.
Publisher Copyright:
© 2021
PY - 2021/5/19
Y1 - 2021/5/19
N2 - Hidden Markov models (HMMs) are used to learn single-molecule kinetics across a range of experimental techniques. By their construction, HMMs assume that single-molecule events occur on slower timescales than those of data acquisition. To move beyond that HMM limitation and allow for single-molecule events to occur on any timescale, we must treat single-molecule events in continuous time as they occur in nature. We propose a method to learn kinetic rates from single-molecule Förster resonance energy transfer (smFRET) data collected by integrative detectors, even if those rates exceed data acquisition rates. To achieve that, we exploit our recently proposed “hidden Markov jump process” (HMJP), with which we learn transition kinetics from parallel measurements in donor and acceptor channels. HMJPs generalize the HMM paradigm in two critical ways: (1) they deal with physical smFRET systems as they switch between conformational states in continuous time, and (2) they estimate transition rates between conformational states directly without having recourse to transition probabilities or assuming slow dynamics. Our continuous-time treatment learns the transition kinetics and photon emission rates for dynamic regimes that are inaccessible to HMMs, which treat system kinetics in discrete time. We validate our framework's robustness on simulated data and demonstrate its performance on experimental data from FRET-labeled Holliday junctions.
AB - Hidden Markov models (HMMs) are used to learn single-molecule kinetics across a range of experimental techniques. By their construction, HMMs assume that single-molecule events occur on slower timescales than those of data acquisition. To move beyond that HMM limitation and allow for single-molecule events to occur on any timescale, we must treat single-molecule events in continuous time as they occur in nature. We propose a method to learn kinetic rates from single-molecule Förster resonance energy transfer (smFRET) data collected by integrative detectors, even if those rates exceed data acquisition rates. To achieve that, we exploit our recently proposed “hidden Markov jump process” (HMJP), with which we learn transition kinetics from parallel measurements in donor and acceptor channels. HMJPs generalize the HMM paradigm in two critical ways: (1) they deal with physical smFRET systems as they switch between conformational states in continuous time, and (2) they estimate transition rates between conformational states directly without having recourse to transition probabilities or assuming slow dynamics. Our continuous-time treatment learns the transition kinetics and photon emission rates for dynamic regimes that are inaccessible to HMMs, which treat system kinetics in discrete time. We validate our framework's robustness on simulated data and demonstrate its performance on experimental data from FRET-labeled Holliday junctions.
KW - fluorescence
KW - hidden Markov jump process
KW - single molecule data analysis
KW - smFRET
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U2 - 10.1016/j.xcrp.2021.100409
DO - 10.1016/j.xcrp.2021.100409
M3 - Article
AN - SCOPUS:85107384799
SN - 2666-3864
VL - 2
JO - Cell Reports Physical Science
JF - Cell Reports Physical Science
IS - 5
M1 - 100409
ER -