ArticlePLoS computational biology2022
Development of hidden Markov modeling method for molecular orientations and structure estimation from high-speed atomic force microscopy time-series images.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Reconstruction of the Flexible IgM Fc Core Using Atomic Force Microscopy Topography and the AFM-Assembly Pipeline.Journal of molecular recognition : JMR · 2026Article
- Deciphering conformational dynamics in AFM data using fast nonlinear NMA and FFT-based search with AFMFit.Communications biology · 2025Article
- A structural biology compatible file format for atomic force microscopy.Nature communications · 2025Article
- BioAFMviewer software for simulation atomic force microscopy of molecular structures and conformational dynamics.Journal of structural biology: X · 2023Review
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6 authors.
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Abstract
High-speed atomic force microscopy (HS-AFM) is a powerful technique for capturing the time-resolved behavior of biomolecules. However, structural information in HS-AFM images is limited to the surface geometry of a sample molecule. Inferring latent three-dimensional structures from the surface geometry is thus important for getting more insights into conformational dynamics of a target biomolecule. Existing methods for estimating the structures are based on the rigid-body fitting of candidate structures to each frame of HS-AFM images. Here, we extend the existing frame-by-frame rigid-body fitting analysis to multiple frames to exploit orientational correlations of a sample molecule between adjacent frames in HS-AFM data due to the interaction with the stage. In the method, we treat HS-AFM data as time-series data, and they are analyzed with the hidden Markov modeling. Using simulated HS-AFM images of the taste receptor type 1 as a test case, the proposed method shows a more robust estimation of molecular orientations than the frame-by-frame analysis. The method is applicable in integrative modeling of conformational dynamics using HS-AFM data.
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