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Related Experiment Video

Updated: Apr 6, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

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Estimating Protein Conformational States from High-Speed AFM Images with Molecular Dynamics and Deep Learning.

Katsuki Sato¹, Yui Kanaoka², Tomoya Tsukazaki³

¹Department of Chemistry, Faculty of Science, Tokyo University of Science, Shinjuku-ku, Tokyo 162-8601, Japan.

Journal of Chemical Information and Modeling

|April 4, 2026

Summary

DeepAFM, a novel deep learning framework, enhances high-speed atomic force microscopy (HS-AFM) by denoising images and accurately identifying protein conformational states. This method improves the analysis of complex biomolecular dynamics.

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Last Updated: Apr 6, 2026

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Force Spectroscopy of Single Protein Molecules Using an Atomic Force Microscope

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Published on: February 28, 2019

Area of Science:

Biophysics
Structural Biology
Computational Biology

Background:

High-speed atomic force microscopy (HS-AFM) visualizes real-time protein dynamics at the single-molecule level.
HS-AFM imaging is often limited by noise and low spatial resolution, hindering detailed conformational state analysis.

Purpose of the Study:

To develop a computational framework, DeepAFM, integrating deep learning and molecular dynamics (MD) simulations.
To improve the denoising of HS-AFM images and the estimation of protein conformational states.

Main Methods:

Trained a deep learning model on simulated HS-AFM images derived from MD snapshots with realistic noise.
Incorporated temporal lag effects and noise mimicking experimental conditions.
Applied DeepAFM to the SecYAEG-nanodisc complex to analyze SecA conformational transitions.

Main Results:

DeepAFM effectively denoises experimental HS-AFM images, revealing dominant protein conformational states.
The model demonstrated robustness to noise and outperformed conventional fitting methods.
Analysis of SecA conformational transitions between closed and wide-open states was achieved.

Conclusions:

DeepAFM provides a robust deep-learning-assisted strategy for interpreting noisy HS-AFM data.
The framework enables more reliable identification of protein conformational dynamics.
This approach advances the analysis of single-molecule biophysical measurements.