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Automated Two-dimensional Spatiotemporal Analysis of Mobile Single-molecule FRET Probes

Automated Two-dimensional Spatiotemporal Analysis of Mobile Single-molecule FRET Probes

Published on: November 23, 2021

Sequential data assimilation for single-molecule FRET photon-counting data.

Yasuhiro Matsunaga¹, Akinori Kidera², Yuji Sugita¹

¹Advanced Institute for Computational Science, RIKEN, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.

The Journal of Chemical Physics

|June 8, 2015

Summary

This summary is machine-generated.

We developed a new data assimilation method using single-molecule Förster resonance energy transfer (smFRET) photon-counting data to improve biomolecular simulations. This approach refines conformational ensembles and corrects model parameters in molecular dynamics simulations.

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High Precision FRET at Single-molecule Level for Biomolecule Structure Determination

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Area of Science:

Computational Biology
Biophysics
Statistical Mechanics

Background:

Numerical simulations are crucial for understanding biomolecular dynamics.
Integrating experimental data with simulations enhances accuracy.
Single-molecule Förster resonance energy transfer (smFRET) provides time-series conformational data.

Purpose of the Study:

To develop a sequential data assimilation method for biomolecular conformational analysis.
To integrate one-dimensional smFRET photon-counting time-series data with molecular dynamics (MD) simulations.
To improve the accuracy of conformational ensembles and correct model parameters.

Main Methods:

A particle filter was employed to screen conformational ensembles.
The method incorporates replicated molecular dynamics simulations.
A likelihood function for smFRET photon-counting data was utilized.

Main Results:

The method successfully screened conformational ensembles against emulated smFRET data.
It estimated end-to-end distance dynamics and latent conformational variables.
The particle filter corrected model parameter dependence in coarse-grained MD simulations.

Conclusions:

The developed data assimilation method enhances biomolecular simulations using smFRET data.
It offers a robust approach for analyzing conformational dynamics.
The method shows applicability to real experimental data for biomolecules.