ACS NanoBiophysicsPeer-reviewed journal article
Flexible fitting to infer atomistic-precision models of large-amplitude conformational dynamics in biomolecules from high-speed atomic force microscopy imaging
Romain AmyotiD, Osamu MiyashitaiD, Xuan WuiD, Kazusa Takeda, Noriyuki KoderaiD, Hiroki KonnoiD, Florence TamaiD, Holger FlechsigiD
Abstract
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 of 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 first applications to experimental HS-AFM imaging data. To facilitate applications, we developed a direct workflow from raw experimental AFM data to visualization and analysis of fitting results. The presented applications to experimental data of a single protein domain, a protein complex, and a megadalton size protein filament demonstrate versatility of NMFF-AFM modelling to reproduce large-amplitude conformational motions of biomolecular dynamics from HS-AFM imaging. As a first step towards 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 standalone user-friendly interactive BioAFMviewer software opens the opportunity for broad range applications to facilitate the understanding of resolution-limited HS-AFM measurements.
- assay: imaging
- assay: fluorescence / live-cell microscopy
- research focus: protein structure prediction
- research focus: trajectory inference
- research focus: dimensionality reduction
- research focus: software / tool
- approach: deep learning
- approach: statistical model
- Citations
- 13
- FWCI
- 4.70
- Field percentile
- top 3%
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