Atomic reconstruction of biomolecular structures from AFM images and quantitative validation of experimental data using simulated AFM scanning
Amyot, R.; Marchesi, A.; Franz, C. M.; Casuso, I.; Flechsig, H.
Show abstract
We provide the BioAFMviewer-Toolbox, an extension of our previously developed software platform for simulated AFM scanning of biomolecular structures and dynamics. The focus was on developing a toolbox of methods which employ simulated AFM scanning combined with quantitative analysis to facilitate the interpretation of resolution-limited AFM images. The key advancement is the automatized fitting of biomolecular structures to experimental AFM images, which allows to reconstruct 3D atomistic structures from AFM surface scans. Moreover, several methods for detailed analysis and comparison of surface topographies in simulated and experimental AFM images are provided. We demonstrate the applicability of the developed tools in the interpretation of high-speed AFM observations of proteins. The toolbox is implemented into the versatile interactive interface of the BioAFMviewer, which is a free software package available at www.bioafmviewer.com.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Reliable, standardized measurements for cell mechanical properties 92%
- Modulation of specific interactions within a viral fusion protein predicted from machine learning blocks membrane fusion 92%
- Co2+-mediated adsorption facilitates atomic force microscopy of DNA molecules at double-helix resolution 92%
Similar papers in this journal
Similar papers in this journal
- The Particle Filter Method to Integrate High-Speed Atomic Force Microscopy Measurement with Biomolecular Simulations 95%
- Flexible Fitting of Biomolecular Structures to Atomic Force Microscopy Images via Biased Molecular Simulations 94%
- Coupling of conformational switches in calcium sensor unraveled with local Markov models and transfer entropy 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.