AFM-Fold: Rapid Reconstruction of Protein Conformations from AFM Images.

Authors

Tsuyoshi Kawai, Yasuhiro Matsunaga

Published in

Biophysical journal. Jun 19, 2026. Epub Jun 19, 2026.

Abstract

High-speed atomic force microscopy (HS-AFM) enables direct visualization of protein dynamics under near-physiological conditions, yet its intrinsic limitation to surface topography prevents atomic-level structural characterization. We present AFM-Fold, a generative AI-based framework that reconstructs three-dimensional protein conformations directly from AFM images. AFM-Fold combines a group-equivariant convolutional neural network, which extracts low-dimensional collective variables (CVs) from AFM images, with a guided diffusion process that generates conformations consistent with the inferred CVs. Using pseudo-AFM images of Adenylate kinase, AFM-Fold accurately reproduced not only the open and closed conformations, but also a continuous range of intermediate conformations spanning the open-closed transition. Application to 159 experimental HS-AFM frames of the flagellar protein FlhA_C further demonstrated that AFM-Fold yields conformations more consistent with experimental images than rigid-body fitting of the crystal structure, and captures time-correlated domain motions that reflect underlying conformational dynamics. AFM-Fold enables rapid, physically plausible structure estimation from individual AFM images, typically within one minute per frame, without relying on molecular dynamics simulations. This unified and computationally efficient pipeline opens a route to high-throughput structural analysis of HS-AFM movies.

PMID:
42322049
Bibliographic data and abstract were imported from PubMed on 20 Jun 2026.

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