# Integrating Molecular Dynamics Simulations and Single-molecule FRET Spectroscopy: From Computational FRET Estimation to Experimental Data Interpretation

**Authors:** Stephanie Sauve, Ehsaneh Khodadadi, Ahmed Shubbar, Ehsan Khodadadi, Mahmoud Moradi

PMC · DOI: 10.1021/acs.jpcb.5c05660 · 2026-01-05

## TL;DR

This paper reviews how combining molecular dynamics simulations with single-molecule FRET spectroscopy helps study biomolecular structures and dynamics at an atomic level.

## Contribution

The paper provides a comprehensive review of recent advancements in integrating MD simulations and smFRET for studying biomolecular dynamics.

## Key findings

- Combining MD simulations and smFRET improves understanding of conformational dynamics and structural changes in biomolecules.
- MD simulations help estimate FRET efficiencies and interpret experimental data from smFRET.
- The integration has revealed insights into binding mechanisms, allosteric effects, and structural dynamics.

## Abstract

Molecular dynamics
(MD) simulations can characterize biomolecular
processes at an exceptional spatiotemporal resolution not able to
be accessed experimentally. As the limitations associated with MD
simulations lessen and the method advances toward greater capabilities,
the simulations are being applied to a wide array of new applications.
For example, the integration of MD simulations and single-molecule
Förster resonance energy transfer (smFRET) spectroscopy is
a newly developing and growing application combining experimental
and computational approaches. The integration of these techniques
provides valuable insight into the conformational dynamics of biomolecules
on an atomic-level, thereby enhancing the understanding of complex
biological processes. This review compiles information on simulating
FRET dyes and estimating FRET efficiencies from MD simulations and
using MD simulations to gain insight into experimental data to shine
light on the recent advancements in joining computational and experimental
techniques. We discuss notable studies that incorporate the use of
both MD simulations and smFRET as well as discuss the challenges that
have been faced regarding their integration. The joining of these
approaches have provided valuable insights into conformational sampling,
binding mechanisms, structural dynamics, and allosteric effects thus
far and will continue to advance the understanding of biomolecular
dynamics in the future.

## Full-text entities

- **Genes:** HSP82 (Hsp90 family chaperone HSP82) [NCBI Gene 855836] {aka HSP90}
- **Diseases:** amyloid-related diseases (MESH:C000718787)
- **Chemicals:** Alexa488 (-), proline (MESH:D011392), acid (MESH:D000143), trifluoroethanol (MESH:D014270), polyproline (MESH:C011083), salt (MESH:D012492), glycerol (MESH:D005990), oxygen (MESH:D010100), Na + (MESH:D012964), Alexa 594 (MESH:C417664), Alexa 647 (MESH:C569686), uracil (MESH:D014498), thiol (MESH:D013438), urea (MESH:D014508), carbon (MESH:D002244), Alexa Fluor 488 (MESH:C000711379), thymine (MESH:D013941), maleimide (MESH:C043592), Leu (MESH:D007930), water (MESH:D014867), ATP (MESH:D000255), Cys (MESH:D003545), Alexa Fluor 546 (MESH:C481052), Cy5 (MESH:C085321)
- **Species:** Saccharomyces cerevisiae (baker's yeast, species) [taxon 4932]
- **Mutations:** MET in 2024, Pro)20 Cys, A 2A, E484K, Ala mutations at positions 205

## Figures

33 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12814536/full.md

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Source: https://tomesphere.com/paper/PMC12814536