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

A structural biology compatible file format for atomic force microscopy

Cryogenic electron microscopy (cryo-EM), X-ray crystallography, and nuclear magnetic resonance (NMR) contribute structural data that are interchangeable, cross-verifiable, and visualizable on common platforms, making them powerful tools for our understanding of protein structures. Unfortunately, atomic force microscopy (AFM) has so far failed to interface with these structural biology methods, despite the recent development of localization AFM (LAFM) that allows extracting high-resolution structural information from AFM data. Here, we build on LAFM and develop a pipeline that transforms AFM data into 3D-density files (.afm) that are readable by programs commonly used to visualize, analyze, and interpret structural data. We show that 3D-LAFM densities can serve as force fields to steer molecular dynamics flexible fitting (MDFF) to obtain structural models of previously unresolved states based on AFM observations in close-to-native environment. Besides, the .afm format enables direct 3D or 2D visualization and analysis of conventional AFM images. We anticipate that the file format will find wide usage and embed AFM in the repertoire of methods routinely used by the structural biology community, allowing AFM researchers to deposit data in repositories in a format that allows comparison and cross-verification with data from other techniques.

Finite element modelling of atomic force microscopy imaging on deformable surfaces

Atomic force microscopy (AFM) provides a three-dimensional topographic representation of a sample surface, at nanometre resolution. Computational simulations can aid the interpretation of such representations, but have mostly been limited to cases where both the AFM probe and the sample are hard and not compressible. In many applications, however, the sample is soft and therefore deformed due to the force exerted by the AFM tip. Here we use finite element modelling (FEM) to study how the measured AFM topography relates to the surface structures of soft and compressible materials. Consistent with previous analytical studies, the measured elastic modulus in AFM is generally found to deviate from the elastic modulus of the sample material. By the analysis of simple surface geometries, the FEM modelling shows how measured mechanical and topographic features in AFM images depend on a combination of tip-sample geometry and indentation of the tip into the sample. Importantly for the interpretation of AFM data, nanoparticles may appear larger or smaller by a factor of two depending on tip size and indentation force; and a higher spatial resolution in AFM images does not necessarily coincide with a more accurate representation of the sample surface. These observations on simple surface geometries also extend to molecular-resolution AFM, as illustrated by comparing FEM results with experimental data acquired on DNA. Taken together, the FEM results provide a framework that aids the interpretation of surface topography and local mechanics as measured by AFM.

Perspectives Toward an Integrative Structural Biology Pipeline With Atomic Force Microscopy Topographic Images

After the recent double revolutions in structural biology, which include the use of direct detectors for cryo-electron microscopy resulting in a significant improvement in the expected resolution of large macromolecule structures, and the advent of AlphaFold which allows for near-accurate prediction of any protein structures, the field of structural biology is now pursuing more ambitious targets, including several MDa assemblies. But complex target systems cannot be tackled using a single biophysical technique. The field of integrative structural biology has emerged as a global solution. The aim is to integrate data from multiple complementary techniques to produce a final three-dimensional model that cannot be obtained from any single technique. The absence of atomic force microscopy data from integrative structural biology platforms is not necessarily due to its nm resolution, as opposed to Å resolution for x-ray crystallography, nuclear magnetic resonance, or electron microscopy. Rather a significant issue was that the AFM topographic data lacked interpretability. Fortunately, with the introduction of the AFM-Assembly pipeline and other similar tools, it is now possible to integrate AFM topographic data into integrative modeling platforms. The advantages of single molecule techniques, such as AFM, include the ability to confirm experimentally any assembled molecular models or to produce alternative conformations that mimic the inherent flexibility of large proteins or complexes. The review begins with a brief overview of the historical developments of AFM data in structural biology, followed by an examination of the strengths and limitations of AFM imaging, which have hindered its integration into modern modeling platforms. This review discusses the correction and improvement of AFM topographic images, as well as the principles behind the AFM-Assembly pipeline. It also presents and discusses a series of challenges that need to be addressed in order to improve the incorporation of AFM data into integrative modeling platform.

Modeling Conformational Transitions of Biomolecules from Atomic Force Microscopy Images using Normal Mode Analysis

Observing a single biomolecule performing its function is fundamental in biophysics as it provides important information for elucidating the mechanism. High-speed atomic force microscopy (HS-AFM) is a unique and powerful technique that allows the observation of biomolecular motion in a near-native environment. However, the spatial resolution of HS-AFM is limited by the physical size of the cantilever tip, which restricts the ability to obtain atomic details of molecules. In this study, we propose a novel computational algorithm designed to derive atomistic models of conformational dynamics from AFM images. Our method uses normal-mode analysis to describe the expected motions of the molecule, allowing these motions to be represented with a limited number of coordinates. This approach mitigates the problem of overinterpretation inherent in the analysis of AFM images with limited resolution. We demonstrate the effectiveness of our algorithm, NMFF-AFM, using synthetic data sets for three proteins that undergo significant conformational changes. NMFF-AFM is a fast and user-friendly program that requires minimal setup and has the potential to be a valuable tool for biophysical studies using HS-AFM.

Deciphering the actin structure-dependent preferential cooperative binding of cofilin

The mechanism underlying the preferential and cooperative binding of cofilin and the expansion of clusters toward the pointed-end side of actin filaments remains poorly understood. To address this, we conducted a principal component analysis based on available filamentous actin (F-actin) and C-actin (cofilins were excluded from cofilactin) structures and compared to monomeric G-actin. The results strongly suggest that C-actin, rather than F-ADP-actin, represented the favourable structure for binding preference of cofilin. High-speed atomic force microscopy explored that the shortened bare half helix adjacent to the cofilin clusters on the pointed end side included fewer actin protomers than normal helices. The mean axial distance (MAD) between two adjacent actin protomers along the same long-pitch strand within shortened bare half helices was longer (5.0-6.3 nm) than the MAD within typical helices (4.3-5.6 nm). The inhibition of torsional motion during helical twisting, achieved through stronger attachment to the lipid membrane, led to more pronounced inhibition of cofilin binding and cluster formation than the presence of inorganic phosphate (Pi) in solution. F-ADP-actin exhibited more naturally supertwisted half helices than F-ADP.Pi-actin, explaining how Pi inhibits cofilin binding to F-actin with variable helical twists. We propose that protomers within the shorter bare helical twists, either influenced by thermal fluctuation or induced allosterically by cofilin clusters, exhibit characteristics of C-actin-like structures with an elongated MAD, leading to preferential and cooperative binding of cofilin.

Four Parallel Pathways in T4 Ligase-Catalyzed Repair of Nicked DNA with Diverse Bending Angles

The structural diversity of biological macromolecules in different environments contributes complexity to enzymological processes vital for cellular functions. Fluorescence resonance energy transfer and electron microscopy are used to investigate the enzymatic reaction of T4 DNA ligase catalyzing the ligation of nicked DNA. The data show that both the ligase-AMP complex and the ligase-AMP-DNA complex can have four conformations. This finding suggests the parallel occurrence of four ligation reaction pathways, each characterized by specific conformations of the ligase-AMP complex that persist in the ligase-AMP-DNA complex. Notably, these complexes have DNA bending angles of ≈0°, 20°, 60°, or 100°. The mechanism of parallel reactions challenges the conventional notion of simple sequential reaction steps occurring among multiple conformations. The results provide insights into the dynamic conformational changes and the versatile attributes of T4 DNA ligase and suggest that the parallel multiple reaction pathways may correspond to diverse T4 DNA ligase functions. This mechanism may potentially have evolved as an adaptive strategy across evolutionary history to navigate complex environments.

Evaluation in mice of cell-free produced CT584 as a Chlamydia vaccine antigen

Chlamydia trachomatis is the most prevalent bacterial sexually transmitted pathogen worldwide. Since chlamydial infection is largely asymptomatic with the potential for serious complications, a preventative vaccine is likely the most viable long-term answer to this public health threat. Cell-free protein synthesis (CFPS) utilizes the cellular protein manufacturing machinery decoupled from the requirement for maintaining cellular viability, offering the potential for flexible, rapid, and de-centralized production of recombinant protein vaccine antigens. Here, we use CFPS to produce the putative chlamydial type three secretion system (T3SS) needle-tip protein, CT584, for use as a vaccine antigen in mouse models. High-speed atomic force microscopy (HS-AFM) imaging and computer simulations confirm that CFPS-produced CT584 retains a native-like structure prior to immunization. Female mice were primed with CT584 adjuvanted with CpG-1826 intranasally (i.n.) or CpG-1826 + Montanide ISA 720 intramuscularly (i.m.), followed four-weeks later by an i.m. boost before respiratory challenge with 104 inclusion forming units (IFU) of Chlamydia muridarum. Immunization with CT584 generated robust antibody responses but weak cell mediated immunity and failed to protect against i.n. challenge as demonstrated by body weight loss, increased lungs' weights and the presence of high numbers of IFUs in the lungs. While CT584 alone may not be the ideal vaccine candidate, the speed and flexibility with which CFPS can be used to produce other potential chlamydial antigens makes it an attractive technique for antigen production.

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.

Technical advances in high-speed atomic force microscopy

It has been 30 years since the outset of developing high-speed atomic force microscopy (HS-AFM), and 15 years have passed since its establishment in 2008. This advanced microscopy is capable of directly visualizing individual biological macromolecules in dynamic action and has been widely used to answer important questions that are inaccessible by other approaches. The number of publications on the bioapplications of HS-AFM has rapidly increased in recent years and has already exceeded 350. Although less visible than these biological studies, efforts have been made for further technical developments aimed at enhancing the fundamental performance of HS-AFM, such as imaging speed, low sample disturbance, and scan size, as well as expanding its functionalities, such as correlative microscopy, temperature control, buffer exchange, and sample manipulations. These techniques can expand the range of HS-AFM applications. After summarizing the key technologies underlying HS-AFM, this article focuses on recent technical advances and discusses next-generation HS-AFM.

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.

BioAFMviewer software for simulation atomic force microscopy of molecular structures and conformational dynamics

Atomic force microscopy (AFM) and high-speed scanning have significantly advanced real time observation of biomolecular dynamics, with applications ranging from single molecules to the cellular level. To facilitate the interpretation of resolution-limited imaging, post-experimental computational analysis plays an increasingly important role to understand AFM measurements. Data-driven simulation of AFM, computationally emulating experimental scanning, and automatized fitting has recently elevated the understanding of measured AFM topographies by inferring the underlying full 3D atomistic structures. Providing an interactive user-friendly interface for simulation AFM, the BioAFMviewer software has become an established tool within the Bio-AFM community, with a plethora of applications demonstrating how the obtained full atomistic information advances molecular understanding beyond topographic imaging. This graphical review illustrates the BioAFMviewer capacities and further emphasizes the importance of simulation AFM to complement experimental observations.

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

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 .

Development of hidden Markov modeling method for molecular orientations and structure estimation from high-speed atomic force microscopy time-series images

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 applicable in integrative modeling of conformational dynamics using HS-AFM data.

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.

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

Atomic force microscopy (AFM) can visualize the dynamics of single biomolecules under near-physiological conditions. However, the scanning tip probes only the molecular surface with limited resolution, missing details required to fully deduce functional mechanisms from imaging alone. To overcome such drawbacks, we developed a computational framework to reconstruct 3D atomistic structures from AFM surface scans, employing simulation AFM and automatized fitting to experimental images. We provide applications to AFM images ranging from single molecular machines, protein filaments, to large-scale assemblies of 2D protein lattices, and demonstrate how the obtained full atomistic information advances the molecular understanding beyond the original topographic AFM image. We show that simulation AFM further allows for quantitative molecular feature assignment within measured AFM topographies. Implementation of the developed methods into the versatile interactive interface of the BioAFMviewer software, freely available at www.bioafmviewer.com, presents the opportunity for the broad Bio-AFM community to employ the enormous amount of existing structural and modeling data to facilitate the interpretation of resolution-limited AFM images.