Atomic force microscopy (AFM) can visualize the dynamics of single biomolecules under near-physiological conditions. However, the scanning tip probes only the molecular surface with limited resolution, missing details required to fully deduce functional mechanisms from imaging alone. To overcome such drawbacks, we developed a computational framework to reconstruct 3D atomistic structures from AFM surface scans, employing simulation AFM and automatized fitting to experimental images. We provide applications to AFM images ranging from single molecular machines, protein filaments, to large-scale assemblies of 2D protein lattices, and demonstrate how the obtained full atomistic information advances the molecular understanding beyond the original topographic AFM image. We show that simulation AFM further allows for quantitative molecular feature assignment within measured AFM topographies. Implementation of the developed methods into the versatile interactive interface of the BioAFMviewer software, freely available at www.bioafmviewer.com, presents the opportunity for the broad Bio-AFM community to employ the enormous amount of existing structural and modeling data to facilitate the interpretation of resolution-limited AFM images.

High-speed atomic force microscopy (HS-AFM) experiments allow direct observation of biomolecular dynamics at the single-molecule level, acquiring a large amount of topographic imaging data that visualizes changes in the molecular surface during functional activity over an extended period of time. Since images have no atomistic resolution, a major challenge has been to develop post-experimental computational methods to infer atomistic information from measurements. The recently developed NMFF-AFM flexible fitting method provides a computationally efficient approach promising to infer atomistic-precision models of conformational dynamics from resolution-limited AFM imaging data. We report the software integration of this method into the well-established BioAFMviewer platform and demonstrate its first applications to experimental HS-AFM imaging data. To facilitate applications, we developed a direct workflow from raw experimental AFM data to the visualization and analysis of fitting results. The presented applications to experimental data of a single protein domain, a protein complex, and a megadalton-sized protein filament demonstrate the versatility of NMFF-AFM modeling to reproduce large-amplitude conformational motions of biomolecular dynamics from HS-AFM imaging. As a first step toward automated large-scale analysis of AFM imaging data, we furthermore demonstrate reconstruction of an atomistic molecular movie of protein dynamics, involving large-amplitude conformational transitions, from a measured HS-AFM movie sequence. Implementation of flexible fitting within the stand-alone user-friendly interactive BioAFMviewer software provides the opportunity for a broad range of applications to facilitate the understanding of resolution-limited HS-AFM measurements.

Ubiquitin (Ub) ligases E3 are important factors in selecting target proteins for ubiquitination and determining the type of polyubiquitin chains on the target proteins. In the HECT (homologous to E6AP C-terminus)-type E3 ligases, the HECT domain is composed of an N-lobe and a C-lobe that are connected by a flexible hinge loop. The large conformational rearrangement of the HECT domain via the flexible hinge loop is essential for the HECT-type E3-mediated Ub transfer from E2 to a target protein. However, detailed insights into the structural dynamics of the HECT domain remain unclear. Here, we provide the first direct demonstration of the structural dynamics of the HECT domain using high-speed atomic force microscopy at the nanoscale. We also found that the flexibility of the hinge loop has a great impact not only on its structural dynamics but also on the formation mechanism of free Ub chains.

The ATP-binding cassette protein A1 (ABCA1) is crucial for high-density lipoprotein (HDL) formation, maintaining cholesterol homeostasis. However, the mechanism of ABCA1-mediated HDL formation remains unclear. Here, we reconstituted this process and directly visualized it using high-speed atomic force microscopy by employing a novel linker-based immobilization strategy for side-view imaging of ABCA1 nanodiscs. Our data revealed an approximately 1.6-fold volumetric expansion of the ABCA1 extracellular domain (ECD), coupled with a contraction of the ABCA1-embedded nanodisc, suggesting ATP-dependent transfer of over 300 lipids to the ECD for transient storage. Subsequent addition of apolipoprotein A-I, a major HDL protein, allowed ABCA1 to facilitate nascent HDL particle formation, as it does at the cell membrane. Notably, this study provides the first direct observation of a single ABC protein executing its unique functional cycle with submolecular spatial and subsecond temporal resolution, offering critical insights into "HDL biogenesis", with potential implications for ABCA1-targeted therapeutics for dyslipidemia.

Simulation of atomic force microscopy (AFM) computationally emulates experimental scanning of a biomolecular structure to produce topographic images that can be correlated with measured images. Its application to the enormous amount of available high-resolution structures, as well as to molecular dynamics modelling data, facilitates the quantitative interpretation of experimental observations by inferring atomistic information from resolution-limited measured topographies. The computation required to generate a simulated AFM image generally includes the calculation of contacts between the scanning tip and all atoms from the biomolecular structure. However, since only contacts with surface atoms are relevant, a filtering method shall highly improve the efficiency of simulated AFM computations. In this report, we address this issue and present an elegant solution based on graphics processing unit (GPU) computations that significantly accelerates the computation of simulated AFM images. This method not only allows for the visualization of biomolecular structures combined with ultra-fast synchronized calculation and graphical representation of corresponding simulated AFM images (live simulation AFM), but, as we demonstrate, it can also reduce the computational effort during the automatized fitting of atomistic structures into measured AFM topographies by orders of magnitude. Hence, the developed method will play an important role in post-experimental computational analysis involving simulated AFM, including expected applications in machine learning approaches. The implementation is realized in our BioAFMviewer software (ver. 3) package for simulated AFM of biomolecular structures and dynamics.