Dynamic 3D structure extraction from HS-AFM images

Computational modeling allows to infer atomistic information from AFM images
Credit: Kanazawa University

An intrinsic limitation in AFM imaging is that only the surface topography can be acquired, and the AFM tip is too large to resolve details below the nanometer scale. To facilitate the interpretation and understanding of HS-AFM observations, post-experimental analysis and computational methods play an increasingly important role.

In their review paper published in the Current Opinion in Structural Biology journal Holger Flechsig (NanoLSI, Computational Science), and Toshio Ando (Distinguished Professor at NanoLSI) provide an overview of developments in this topical field of interdisciplinary research.

omputational modeling and simulations already allow to reconstruct 3D conformations with atomistic resolution from topographic resolution-limited AFM images. Furthermore, quantitative analysis methods allow for example automated recognition of biomolecular shape changes from topographic images, or feature assignment including the identification of amino acid residues on the molecular surface.

The developed computational methods are often implemented in open-access software, allowing for convenient applications by the broad Bio-AFM community to complement experimental observations. In that regard, the BioAFMviewer software project initiated at Kanazawa university in 2020 has gained significant attention and plays an important role in a plethora of collaboration projects.

Combining high-speed AFM and computational modeling will elevate the understanding of how proteins function in atomistic detail. An ambitious future goal is the application of molecular modeling to reconstruct atomistic-level 3D molecular movies from high-speed AFM topographic movies.

Contact: Holger Flechsig (flechsig@staff.kanazawa-u.ac.jp),Toshio Ando (tando@staff.kanazawa-u.ac.jp)

Journal: Current Opinion in Structural Biology
DOI: 10.1016/j.sbi.2023.102591
Article Title: Protein dynamics by the combination of high-speed AFM and computational modeling
Article Publication Date: 17-Apr-2023

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Kanazawa University Nano Life Science Institute
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Media Contact

Hiroe Yoneda
Kanazawa University Nano Life Science Institute

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