TY - JOUR
T1 - A Two-Step Method for smFRET Data Analysis
AU - Chen, Jixin
AU - Pyle, Joseph R.
AU - Sy Piecco, Kurt Waldo
AU - Kolomeisky, Anatoly B.
AU - Landes, Christy F.
N1 - Funding Information:
J.C. thanks the Ohio University faculty startup funding, OURC award, CMSS, and NQPI. J.C. thanks Dr. Hugh H. Richardson and Dr. Alexander Govorov for beneficial discussions. C.F.L. thanks Welch Foundation (Grant C-1787). A.B.K. acknowledges Welch Foundation (Grant C-1559), NSF (Grant CHE- 1360979), and the Center for Theoretical Biological Physics sponsored by the NSF (Grant PHY-1427654).
Publisher Copyright:
© 2016 American Chemical Society.
PY - 2016/7/28
Y1 - 2016/7/28
N2 - We demonstrate a two-step data analysis method to increase the accuracy of single-molecule Förster Resonance Energy Transfer (smFRET) experiments. Most current smFRET studies are at a time resolution on the millisecond level. When the system also contains molecular dynamics on the millisecond level, simulations show that large errors are present (e.g., > 40%) because false state assignment becomes significant during data analysis. We introduce and confirm an additional step after normal smFRET data analysis that is able to reduce the error (e.g., < 10%). The idea is to use Monte Carlo simulation to search ideal smFRET trajectories and compare them to the experimental data. Using a mathematical model, we are able to find the matches between these two sets, and back guess the hidden rate constants in the experimental results.
AB - We demonstrate a two-step data analysis method to increase the accuracy of single-molecule Förster Resonance Energy Transfer (smFRET) experiments. Most current smFRET studies are at a time resolution on the millisecond level. When the system also contains molecular dynamics on the millisecond level, simulations show that large errors are present (e.g., > 40%) because false state assignment becomes significant during data analysis. We introduce and confirm an additional step after normal smFRET data analysis that is able to reduce the error (e.g., < 10%). The idea is to use Monte Carlo simulation to search ideal smFRET trajectories and compare them to the experimental data. Using a mathematical model, we are able to find the matches between these two sets, and back guess the hidden rate constants in the experimental results.
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U2 - 10.1021/acs.jpcb.6b05697
DO - 10.1021/acs.jpcb.6b05697
M3 - Article
AN - SCOPUS:84979903042
SN - 1520-6106
VL - 120
SP - 7128
EP - 7132
JO - Journal of Physical Chemistry B
JF - Journal of Physical Chemistry B
IS - 29
ER -