Simulation atomic force microscopy for atomic reconstruction of biomolecular structures from resolution-limited experimental images

Amyot, Romain; Marchesi, Arin; Franz, Clemens M.; Casuso, Ignacio; Flechsig, Holger

doi:10.1371/journal.pcbi.1009970

Cited by 38 publications

(41 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this approach, the parameters used to generate the pseudo-AFM image with the highest similarity are considered as the best estimate. The similarity measures include pixel-RMSD [22], cross correlation [18][20], cosine similarity [19], and structural similarity index measure (SSIM) [17]. For example, pixel-RMSD is defined as follows, essentially the same as peak signal-to-noise ratio (PSNR): where and are the heights at pixel i of the experimental AFM and the pseudo-AFM images, respectively.…”

Section: Methodsmentioning

confidence: 99%

Hidden Markov Modeling of Molecular Orientations and Structures from High-Speed Atomic Force Microscopy Time-Series Images

Ogane

Noshiro

Ando

et al. 2022

Preprint

View full text Add to dashboard Cite

High-speed atomic force microscopy (HS-AFM) is a powerful technique for capturing the time-resolved behavior of biomolecules. However, structural information in HS-AFM images is limited to the surface geometry of a sample molecule. Inferring latent three-dimensional structures from the surface geometry is thus important for getting more insights into conformational dynamics of a target biomolecule. Existing methods for estimating the structures are based on the rigid-body fitting of candidate structures to each frame of HS-AFM images. Here, we extend the existing frame-by-frame rigid-body fitting analysis to multiple frames to exploit orientational correlations of a sample molecule between adjacent frames in HS-AFM data due to the interaction with the stage. In the method, we treat HS-AFM data as time-series data, and they are analyzed with the hidden Markov modeling. Using simulated HS-AFM images of the taste receptor type 1 as a test case, the proposed method shows a more robust estimation of molecular orientations than the frame-by-frame analysis. The method is widely applicable in integrative modeling of conformational dynamics using real experimental HS-AFM data.Author summaryBiomolecules dynamically change their structures in cells and perform various functions that are important for life. It is difficult to observe such dynamic structural changes using conventional experimental techniques, such as X-ray crystallography, that mainly observe static atomic structures. Alternative approach is the atomic force microscopy (AFM), a technique to measure the surface geometry of a sample molecule by scanning with an acute tip. Recently, by increasing the imaging rate of the AFM, it has become possible to observe the dynamics of biomolecules at work. However, the information measured with AFM is limited to the surface geometry of a molecule, so it is important to extract three-dimensional structural information from those data. In this study, we develop a method to estimate structural information with higher accuracy.

show abstract

Section: Methodsmentioning

confidence: 99%

Hidden Markov Modeling of Molecular Orientations and Structures from High-Speed Atomic Force Microscopy Time-Series Images

Ogane

Noshiro

Ando

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Simulated scanning was based on the non-elastic collisions of a cone-shaped scanning tip with a rigid-sphere atomic model of the PDB protein structure (see (25) for details). The automatized fitting procedure identified the optimal match of simulated AFM image and the target experimental HS-AFM image (26). Optimal fitting results were obtained with a tip having 3nm probe-sphere radius and a cone half-angle of 10°.…”

Section: Automatized Fitting Of Pdb Structures Into Experimental Afm ...mentioning

confidence: 99%

Structural dynamics of E6AP E3 ligase HECT domain and involvement of flexible hinge loop in ubiquitin chain synthesis mechanism

Takeda

Muro

Kobayashi

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Ubiquitin (Ub) ligases E3 are an important factor 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 containing the E2-binding site and a C-lobe containing the catalytic Cys residue that forms a thioester bond with Ub. These two lobes are connected by a flexible hinge loop. The large conformational rearrangement of the HECT domain via the flexible hinge loop is essential for HECT-type E3-mediated Ub transfer from E2 to a target protein. However, detailed insights into the structural dynamics of this type of E3 ligases remain unclear. Here, we provide the first direct demonstration of structural dynamics of the E6AP HECT domain using high-speed atomic force microscopy. We also investigated structural dynamics of hinge loop flexibility restricted HECT domain, and we found that flexibility of the E6AP hinge loop has a great impact not only on its structural dynamics but also on the formation of free Ub chains mediated by E3-E3 interactions.

show abstract

“…Tip shape estimation is also important for inferring 3D molecular structures from AFM images. In the recent analysis of AFM images, pseudo-AFM images are emulated from 3D molecular structures (obtained with different experimental or computational techniques, e.g., X-ray crystallography or molecular dynamics simulations) and an assumed tip shape and then compared with the experimental AFM image 12 – 21 . In the analysis, the 3D structure which generates the pseudo-AFM images most similar to the experimental AFM image is selected as the best estimate for 3D molecular structure.…”

Section: Introductionmentioning

confidence: 99%

End-to-end differentiable blind tip reconstruction for noisy atomic force microscopy images

Matsunaga

Fuchigami

Ogane

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Observing the structural dynamics of biomolecules is vital to deepening our understanding of biomolecular functions. High-speed (HS) atomic force microscopy (AFM) is a powerful method to measure biomolecular behavior at near physiological conditions. In the AFM, measured image profiles on a molecular surface are distorted by the tip shape through the interactions between the tip and molecule. Once the tip shape is known, AFM images can be approximately deconvolved to reconstruct the surface geometry of the sample molecule. Thus, knowing the correct tip shape is an important issue in the AFM image analysis. The blind tip reconstruction (BTR) method developed by Villarrubia (J Res Natl Inst Stand Technol 102:425, 1997) is an algorithm that estimates tip shape only from AFM images using mathematical morphology operators. While the BTR works perfectly for noise-free AFM images, the algorithm is susceptible to noise. To overcome this issue, we here propose an alternative BTR method, called end-to-end differentiable BTR, based on a modern machine learning approach. In the method, we introduce a loss function including a regularization term to prevent overfitting to noise, and the tip shape is optimized with automatic differentiation and backpropagations developed in deep learning frameworks. Using noisy pseudo-AFM images of myosin V motor domain as test cases, we show that our end-to-end differentiable BTR is robust against noise in AFM images. The method can also detect a double-tip shape and deconvolve doubled molecular images. Finally, application to real HS-AFM data of myosin V walking on an actin filament shows that the method can reconstruct the accurate surface geometry of actomyosin consistent with the structural model. Our method serves as a general post-processing for reconstructing hidden molecular surfaces from any AFM images. Codes are available at https://github.com/matsunagalab/differentiable_BTR.

show abstract

Simulation atomic force microscopy for atomic reconstruction of biomolecular structures from resolution-limited experimental images

Cited by 38 publications

References 36 publications

Hidden Markov Modeling of Molecular Orientations and Structures from High-Speed Atomic Force Microscopy Time-Series Images

Hidden Markov Modeling of Molecular Orientations and Structures from High-Speed Atomic Force Microscopy Time-Series Images

Structural dynamics of E6AP E3 ligase HECT domain and involvement of flexible hinge loop in ubiquitin chain synthesis mechanism

End-to-end differentiable blind tip reconstruction for noisy atomic force microscopy images

Contact Info

Product

Resources

About