Investigation of blind tip estimation

Bakucz, Peter; Krüger-Sehm, Rolf; Koenders, Ludger

doi:10.1063/1.2901616

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…In reality, however, the probe shape could have much more complicated shape. There is a classic method to estimate the probe shape from an AFM image without any prior knowledge, called the blind tip reconstruction method [38][39][40]. Since the method does not postulates the shape of the probe, this is much more ambitious and thus a potentially more powerful method.…”

Section: Plos Computational Biologymentioning

confidence: 99%

Rigid-body fitting to atomic force microscopy images for inferring probe shape and biomolecular structure

2021

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Atomic force microscopy (AFM) can visualize functional biomolecules near the physiological condition, but the observed data are limited to the surface height of specimens. Since the AFM images highly depend on the probe tip shape, for successful inference of molecular structures from the measurement, the knowledge of the probe shape is required, but is often missing. Here, we developed a method of the rigid-body fitting to AFM images, which simultaneously finds the shape of the probe tip and the placement of the molecular structure via an exhaustive search. First, we examined four similarity scores via twin-experiments for four test proteins, finding that the cosine similarity score generally worked best, whereas the pixel-RMSD and the correlation coefficient were also useful. We then applied the method to two experimental high-speed-AFM images inferring the probe shape and the molecular placement. The results suggest that the appropriate similarity score can differ between target systems. For an actin filament image, the cosine similarity apparently worked best. For an image of the flagellar protein FlhAC, we found the correlation coefficient gave better results. This difference may partly be attributed to the flexibility in the target molecule, ignored in the rigid-body fitting. The inferred tip shape and placement results can be further refined by other methods, such as the flexible fitting molecular dynamics simulations. The developed software is publicly available.

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Section: Plos Computational Biologymentioning

confidence: 99%

Rigid-body fitting to atomic force microscopy images for inferring probe shape and biomolecular structure

2021

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“…Flater et al proposed a systematic way to determine the threshold parameter against noisy AFM images 22 . By approximating mathematical morphology operators by linear operators, Bakucz et al proposed a reconstruction method based on the Expectation–Maximization (EM) algorithm with the tip shape represented as a hidden variable 29 . Despite these studies, the original BTR is not routinely utilized in the analysis of noisy AFM data, especially for the analysis of HS-AFM data, due to its susceptibility to noise and difficulty in tuning the parameter.…”

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

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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.

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“…Another group uses tip characterizer without calibration. This method can extract tip shape from an AFM image by various mathematical algorithms without calibration of the tip characterizer, such as the deconvolution algorithm [ 26 ], expectation maximization (EM) algorithm [ 27 ] and morphological estimation algorithm [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Investigation on Blind Tip Reconstruction Errors Caused by Sample Features

Wan

2014

Sensors

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Precision measurements of a nanoscale sample surface using an atomic force microscope (AFM) require a precise quantitative knowledge of the 3D tip shape. Blind tip reconstruction (BTR), established by Villarrubia, gives an outer bound with larger errors if the tip characterizer is not appropriate. In order to explore the errors of BTR, a series of simulation experiments based on a conical model were carried out. The results show that, to reconstruct the tip precisely, the cone angle of the tip characterizer must be smaller than that of the tip. Furthermore, the errors decrease as a function of the tip cone angle and increase linearly with the sample radius of curvature, irrespective of the tip radius of curvature. In particular, for sharp (20 nm radius) and blunt (80 nm radius) tips, the radius of curvature of the tip characterizer must be smaller than 5 nm. Based on these simulation results, a local error model of BTR was established. The maximum deviation between the errors derived from the model and the simulated experiments is 1.22 nm. Compared with the lateral resolution used in the above simulated experiments (4 nm/pixel), it is valid to ignore the deviations and consider the local error model of BTR is indeed in quantitative agreement with the simulation results. Finally, two simulated ideal structures are proposed here, together with their corresponding real samples. The simulation results show they are suitable for BTR.

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Investigation of blind tip estimation

Cited by 8 publications

References 14 publications

Rigid-body fitting to atomic force microscopy images for inferring probe shape and biomolecular structure

Rigid-body fitting to atomic force microscopy images for inferring probe shape and biomolecular structure

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

Investigation on Blind Tip Reconstruction Errors Caused by Sample Features

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