Resolving the data asynchronicity in high-speed atomic force microscopy measurement via the Kalman Smoother

Simulating biological surface dynamics in high-speed atomic force microscopy experiments

High-speed atomic force microscopy (HSAFM) is an important tool for studying the dynamic behavior of large biomolecular assemblies at surfaces. However, unlike light microscopy techniques, which visualize each point in the field of view at the same time, in HSAFM, the surface is literally imaged pixel-by-pixel with a variable extent of time separation existing between recordings made at one pixel and all others within the surface image. Such "temporal asynchronicity" in the recording of the spatial information can introduce distortions into the image when the surface components move at a rate comparable to that at which the surface is imaged. This Letter describes recently released software developments that are able to predict the likely form of these distortions and estimate confidence levels when assigning the identity of observed structures. These described approaches may facilitate both the design and optimization of future HSAFM experimental protocols. Further to this, they may assist in the interpretation of results from already published HSAFM studies.

HSAFM-MIREBA - Methodology for Inferring REsolution in biological applications

Due to a lack of requirement for any direct labelling of the target molecule, high speed atomic force microscopy (HS-AFM) is a potentially powerful procedure for the assessment of biological processes involving macromolecules. When the sample is static the AFM device can be purposefully setup to recover high-resolution information about the feature in question. However, when the feature to be studied moves an appreciable amount during the course of the measurement, the obtained image will be blurred. Encountering such blurred observations prompts the experimenter to sacrifice higher resolution images for higher scanning speeds by tuning available experimental parameters (such as the scanned image area, the image pixel size, the resonance frequency of the cantilever and/or the diameter of the AFM tip). The present work describes a software tool, HSAFM-MIREBA (High Speed Atomic Force Microscopy - Methodology for Inferring REsolution in Biological Applications) that allows for pre-experimental optimization of such parameters through iterative rounds of simulation of both the dynamic surface process and the HS-AFM measurement (based on the particular set of governing parameters). A representative set of five dynamic biological processes that describe a range of diffusive and directed motions (which can themselves be tuned by altering characteristic governing parameter sets) are provided.

Particle Smoother to Assimilate Asynchronous Movie Data of High-Speed AFM with MD Simulations

High-speed (HS) atomic force microscopy (AFM) can be used to observe structural dynamics of biomolecules under near-physiological conditions. In the AFM measurement, the probe tip scans an area of interest and acquires height data pixel by pixel so that the obtained AFM image contains a measurement time difference. In this study, to integrate molecular dynamics simulations with asynchronous HS-AFM movie data, we developed a particle smoother (PS) method for Bayesian data assimilation, one of the machine learning approaches, by extending the previous particle filter method. With a twin experiment with an asynchronous pseudo HS-AFM movie of a nucleosome, we found that the PS method with the pixel-by-pixel data acquisition reproduced the dynamic behavior of a nucleosome better than the previous particle filter method that ignored the data asynchronicity. We examined several frequencies of particle resampling in the PS method, and found that resampling once per one frame was optimal for reproducing the dynamic behavior. Thus, we found that the PS method with an appropriate resampling frequency is a powerful method for estimating the dynamic behavior of a target molecule from HS-AFM data with low spatiotemporal resolution.

Protein dynamics by the combination of high-speed AFM and computational modeling

High-speed atomic force microscopy (HS-AFM) allows direct observation of biological molecules in dynamic action. However, HS-AFM has no atomic resolution. This article reviews recent progress of computational methods to infer high-resolution information, including the construction of 3D atomistic structures, from experimentally acquired resolution-limited HS-AFM images.

Inferring Conformational State of Myosin Motor in an Atomic Force Microscopy Image via Flexible Fitting Molecular Simulations

High-speed atomic force microscopy (HS-AFM) is a powerful technique to image the structural dynamics of biomolecules. We can obtain atomic-resolution structural information from the measured AFM image by superimposing a structural model on the image. We previously developed a flexible fitting molecular dynamics (MD) simulation method that allows for modest conformational changes when superimposed on an AFM image. In this study, for a molecular motor, myosin V (which changes its chemical state), we examined whether the conformationally distinct state in each HS-AFM image could be inferred via flexible fitting MD simulation. We first built models of myosin V bound to the actin filament in two conformational states, the “down-up” and “down-down” states. Then, for the previously obtained HS-AFM image of myosin bound to the actin filament, we performed flexible-fitting MD simulations using the two states. By comparing the fitting results, we inferred the conformational and chemical states from the AFM image.

Highly Reliable Method to Obtain a Correlation Coefficient Unaffected by the SEM Noise Component for Examining the Degree of Specimen Damage due to Electron Beam Irradiation

A correlation coefficient is often used as a measure of the strength of a linear relationship (i.e., the degree of similarity) between two sets of data in a variety of fields. However, in the field of scanning electron microscopy (SEM), it is frequently difficult to properly use the correlation coefficient because SEM images generally include severe noise, which affects the measurement of this coefficient. The current study describes a method of obtaining a correlation coefficient that is unaffected by SEM noise in principle. This correlation coefficient is obtained from a total of four SEM images, comprising two sets of two images with identical views, by calculating several covariance values. Numerical experiments confirm that the measured correlation coefficients obtained using the proposed method for noisy images are equal to those for noise-free images. Furthermore, the present method can be combined with a highly accurate and noise-robust position alignment as needed. As one application, we show that it is possible to immediately examine the degree of specimen damage due to electron beam irradiation during a certain SEM observation, which has been difficult until now.