ArticleFrontiers in molecular biosciences2021
Case Report: Bayesian Statistical Inference of Experimental Parameters via Biomolecular Simulations: Atomic Force Microscopy.
Article in Frontiers in molecular biosciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Synthetic data-driven deep learning for label-free autonomous atomic force microscopy.Nature communications · 2026Article
- Modeling Conformational Transitions of Biomolecules from Atomic Force Microscopy Images using Normal Mode Analysis.The journal of physical chemistry. B · 2024Article
- Spatiotemporal resolution in high-speed atomic force microscopy for studying biological macromolecules in action.Microscopy (Oxford, England) · 2023Review
- Simulation atomic force microscopy for atomic reconstruction of biomolecular structures from resolution-limited experimental images.PLoS computational biology · 2022Article
- Inferring Conformational State of Myosin Motor in an Atomic Force Microscopy ImageFrontiers in molecular biosciences · 2022Article
- Recombinant protein linker production as a basis for non-invasive determination of single-cell yeast age in heterogeneous yeast populations.RSC advances · 2021Article
- Rigid-body fitting to atomic force microscopy images for inferring probe shape and biomolecular structure.PLoS computational biology · 2021Article
- Reconstruction of Three-Dimensional Conformations of Bacterial ClpB from High-Speed Atomic-Force-Microscopy Images.Frontiers in molecular biosciences · 2021Article
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3 authors.
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Abstract
The atomic force microscopy (AFM) is a powerful tool for imaging structures of molecules bound on surfaces. To gain high-resolution structural information, one often superimposes structure models on the measured images. Motivated by high flexibility of biomolecules, we previously developed a flexible-fitting molecular dynamics (MD) method that allows protein structural changes upon superimposing. Since the AFM image largely depends on the AFM probe tip geometry, the fitting process requires accurate estimation of the parameters related to the tip geometry. Here, we performed a Bayesian statistical inference to estimate a tip radius of the AFM probe from a given AFM image via flexible-fitting molecular dynamics (MD) simulations. We first sampled conformations of the nucleosome that fit well the reference AFM image by the flexible-fitting with various tip radii. We then estimated an optimal tip parameter by maximizing the conditional probability density of the AFM image produced from the fitted structure.
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