Computational reconstruction of multidomain proteins using atomic force microscopy data

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.

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.

Recent advances in RNA structurome

RNA structures are essential to support RNA functions and regulation in various biological processes. Recently, a range of novel technologies have been developed to decode genome-wide RNA structures and novel modes of functionality across a wide range of species. In this review, we summarize key strategies for probing the RNA structurome and discuss the pros and cons of representative technologies. In particular, these new technologies have been applied to dissect the structural landscape of the SARS-CoV-2 RNA genome. We also summarize the functionalities of RNA structures discovered in different regulatory layers-including RNA processing, transport, localization, and mRNA translation-across viruses, bacteria, animals, and plants. We review many versatile RNA structural elements in the context of different physiological and pathological processes (e.g., cell differentiation, stress response, and viral replication). Finally, we discuss future prospects for RNA structural studies to map the RNA structurome at higher resolution and at the single-molecule and single-cell level, and to decipher novel modes of RNA structures and functions for innovative applications.

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.

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

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 FlhA_C, 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.

Observation of functional dynamics of individual biomolecules is important to understand molecular mechanisms of cellular phenomena. High-speed (HS) atomic force microscopy (AFM) is a powerful tool that enables us to visualize the real-time dynamics of working biomolecules under near-physiological conditions. However, the information available by the AFM images is limited to the two-dimensional surface shape detected via the force to the probe. While the surface information is affected by the shape of the probe tip, the probe shape itself cannot be directly measured before each AFM measurement. To overcome this problem, we have developed a computational method to simultaneously infer the probe tip shape and the molecular placement from an AFM image. We show that our method successfully estimates the effective AFM tip shape and visualizes a structure with a more accurate placement. The estimation of a molecular placement with the correct probe tip shape enables us to obtain more insights into functional dynamics of the molecule from HS-AFM images.

The molecular structure of long non-coding RNAs: emerging patterns and functional implications

Long non-coding RNAs (lncRNAs) are recently-discovered transcripts that regulate vital cellular processes and are crucially connected to diseases. Despite their unprecedented molecular complexity, it is emerging that lncRNAs possess distinct structural motifs. Remarkably, the 3D shape and topology of full-length, native lncRNAs have been visualized for the first time in the last year. These studies reveal that lncRNA structures dictate lncRNA functions. Here, we review experimentally determined lncRNA structures and emphasize that lncRNA structural characterization requires synergistic integration of computational, biochemical and biophysical approaches. Based on these emerging paradigms, we discuss how to overcome the challenges posed by the complex molecular architecture of lncRNAs, with the goal of obtaining a detailed understanding of lncRNA functions and molecular mechanisms in the future.

Visualizing the functional 3D shape and topography of long noncoding RNAs by single-particle atomic force microscopy and in-solution hydrodynamic techniques

Long noncoding RNAs (lncRNAs) are recently discovered transcripts that regulate vital cellular processes, such as cellular differentiation and DNA replication, and are crucially connected to diseases. Although the 3D structures of lncRNAs are key determinants of their function, the unprecedented molecular complexity of lncRNAs has so far precluded their 3D structural characterization at high resolution. It is thus paramount to develop novel approaches for biochemical and biophysical characterization of these challenging targets. Here, we present a protocol that integrates non-denaturing lncRNA purification with in-solution hydrodynamic analysis and single-particle atomic force microscopy (AFM) imaging to produce highly homogeneous lncRNA preparations and visualize their 3D topology at ~15-Å resolution. Our protocol is suitable for imaging lncRNAs in biologically active conformations and for measuring structural defects of functionally inactive mutants that have been identified by cell-based functional assays. Once optimized for the specific target lncRNA of choice, our protocol leads from cloning to AFM imaging within 3-4 weeks and can be implemented using state-of-the-art biochemical and biophysical instrumentation by trained researchers familiar with RNA handling and supported by AFM and small-angle X-ray scattering (SAXS) experts.

Flexible Fitting of Biomolecular Structures to Atomic Force Microscopy Images via Biased Molecular Simulations

High-speed (HS) atomic force microscopy (AFM) is a prominent imaging technology that observes large-scale structural dynamics of biomolecules near the physiological condition, but the AFM data are limited to the surface shape of specimens. Rigid-body fitting methods were developed to obtain molecular structures that fit to an AFM image, without accounting for conformational changes. Here, we developed a method to fit flexibly a three-dimensional (3D) biomolecular structure into an AFM image. First, we describe a method to produce a pseudo-AFM image from a given 3D structure in a differentiable form. Then, using a correlation function between the experimental AFM image and the computational pseudo-AFM image, we developed a flexible fitting molecular dynamics (MD) simulation method by which we obtain protein structures that well fit to the given AFM image. We first test it with a twin experiment; using an AFM image produced from a protein structure different from its native conformation as a reference, we performed the flexible fitting MD simulations to sample conformations that fit well the reference AFM image, and the method was confirmed to work well. Then, parameter dependence in the protocol was discussed. Finally, we applied the method to a real experimental HS-AFM image for a flagellar protein FlhA, demonstrating its applicability. We also test the rigid-body fitting of a molecular structure to an AFM image. Our method will be a general tool for dynamic structure modeling based on HS-AFM images and is publicly available through the CafeMol software.

Reconstruction of low-resolution molecular structures from simulated atomic force microscopy images

BACKGROUND

Atomic Force Microscopy (AFM) is an experimental technique to study structure-function relationship of biomolecules. AFM provides images of biomolecules at nanometer resolution. High-speed AFM experiments produce a series of images following dynamics of biomolecules. To further understand biomolecular functions, information on three-dimensional (3D) structures is beneficial.

METHOD

We aim to recover 3D information from an AFM image by computational modeling. The AFM image includes only low-resolution representation of a molecule; therefore we represent the structures by a coarse grained model (Gaussian mixture model). Using Monte-Carlo sampling, candidate models are generated to increase similarity between AFM images simulated from the models and target AFM image.

RESULTS

The algorithm was tested on two proteins to model their conformational transitions. Using a simulated AFM image as reference, the algorithm can produce a low-resolution 3D model of the target molecule. Effect of molecular orientations captured in AFM images on the 3D modeling performance was also examined and it is shown that similar accuracy can be obtained for many orientations.

CONCLUSIONS

The proposed algorithm can generate 3D low-resolution protein models, from which conformational transitions observed in AFM images can be interpreted in more detail.

GENERAL SIGNIFICANCE

High-speed AFM experiments allow us to directly observe biomolecules in action, which provides insights on biomolecular function through dynamics. However, as only partial structural information can be obtained from AFM data, this new AFM based hybrid modeling method would be useful to retrieve 3D information of the entire biomolecule.

Factor Va alternative conformation reconstruction using atomic force microscopy

Protein conformational variability (or dynamics) for large macromolecules and its implication for their biological function attracts more and more attention. Collective motions of domains increase the ability of a protein to bind to partner molecules. Using atomic force microscopy (AFM) topographic images, it is possible to take snapshots of large multi-component macromolecules at the single molecule level and to reconstruct complete molecular conformations. Here, we report the application of a reconstruction protocol, named AFM-assembly, to characterise the conformational variability of the two C domains of human coagulation factor Va (FVa). Using AFM topographic surfaces obtained in liquid environment, it is shown that the angle between C1 and C2 domains of FVa can vary between 40° and 166°. Such dynamical variation in C1 and C2 domain arrangement may have important implications regarding the binding of FVa to phospholipid membranes.

Conditions to minimize soft single biomolecule deformation when imaging with atomic force microscopy

A recurrent interrogation when imaging soft biomolecules using atomic force microscopy (AFM) is the putative deformation of molecules leading to a bias in recording true topographical surfaces. Deformation of biomolecules comes from three sources: sample instability, adsorption to the imaging substrate, and crushing under tip pressure. To disentangle these causes, we measured the maximum height of a well-known biomolecule, the tobacco mosaic virus (TMV), under eight different experimental conditions positing that the maximum height value is a specific indicator of sample deformations. Six basic AFM experimental factors were tested: imaging in air (AIR) versus in liquid (LIQ), imaging with flat minerals (MICA) versus flat organic surfaces (self-assembled monolayers, SAM), and imaging forces with oscillating tapping mode (TAP) versus PeakForce tapping (PFT). The results show that the most critical parameter in accurately measuring the height of TMV in air is the substrate. In a liquid environment, regardless of the substrate, the most critical parameter is the imaging mode. Most importantly, the expected TMV height values were obtained with both imaging with the PeakForce tapping mode either in liquid or in air at the condition of using self-assembled monolayers as substrate. This study unambiguously explains previous poor results of imaging biomolecules on mica in air and suggests alternative methodologies for depositing soft biomolecules on well organized self-assembled monolayers.

Visualizing the Path of DNA through Proteins Using DREEM Imaging

Many cellular functions require the assembly of multiprotein-DNA complexes. A growing area of structural biology aims to characterize these dynamic structures by combining atomic-resolution crystal structures with lower-resolution data from techniques that provide distributions of species, such as small-angle X-ray scattering, electron microscopy, and atomic force microscopy (AFM). A significant limitation in these combinatorial methods is localization of the DNA within the multiprotein complex. Here, we combine AFM with an electrostatic force microscopy (EFM) method to develop an exquisitely sensitive dual-resonance-frequency-enhanced EFM (DREEM) capable of resolving DNA within protein-DNA complexes. Imaging of nucleosomes and DNA mismatch repair complexes demonstrates that DREEM can reveal both the path of the DNA wrapping around histones and the path of DNA as it passes through both single proteins and multiprotein complexes. Finally, DREEM imaging requires only minor modifications of many existing commercial AFMs, making the technique readily available.

Conformational dynamics of individual antibodies using computational docking and AFM

Molecular recognition between a receptor and a ligand requires a certain level of flexibility in macromolecules. In this study, we aimed at analyzing the conformational variability of receptors portrayed by monoclonal antibodies that have been individually imaged using atomic force microscopy (AFM). Individual antibodies were chemically coupled to activated mica surface, and they have been imaged using AFM in ambient conditions. The resulting topographical surface of antibodies was used to assemble the three subunits constituting antibodies: two antigen-binding fragments and one crystallizable fragment using a surface-constrained computational docking approach. Reconstructed structures based on 10 individual topographical surfaces of antibodies are presented for which separation and relative orientation of the subunits were measured. When compared with three X-ray structures of antibodies present in the protein data bank database, results indicate that several arrangements of the reconstructed subunits are comparable with those of known structures. Nevertheless, no reconstructed structure superimposes adequately to any particular X-ray structure consequence of the antibody flexibility. We conclude that high-resolution AFM imaging with appropriate computational reconstruction tools is adapted to study the conformational dynamics of large individual macromolecules deposited on mica.

Ultrastable atomic force microscopy: improved force and positional stability

Atomic force microscopy (AFM) is an exciting technique for biophysical studies of single molecules, but its usefulness is limited by instrumental drift. We dramatically reduced positional drift by adding two lasers to track and thereby actively stabilize the tip and the surface. These lasers also enabled label-free optical images that were spatially aligned to the tip position. Finally, sub-pN force stability over 100 s was achieved by removing the gold coating from soft cantilevers. These enhancements to AFM instrumentation can immediately benefit research in biophysics and nanoscience.

Nanoscale structural features determined by AFM for single virus particles

In this work, we propose "single-image analysis", as opposed to multi-image averaging, for extracting valuable information from AFM images of single bio-particles. This approach allows us to study molecular systems imaged by AFM under general circumstances without restrictions on their structural forms. As feature exhibition is a resolution correlation, we have performed AFM imaging on surfaces of tobacco mosaic virus (TMV) to demonstrate variations of structural patterns with probing resolution. Two AFM images were acquired with the same tip at different probing resolutions in terms of pixel width, i.e., 1.95 and 0.49 nm per pixel. For assessment, we have constructed an in silico topograph based on the three-dimensional crystal structure of TMV as a reference. The prominent artifacts observed in the AFM-determined shape of TMV were attributed to tip convolutions. The width of TMV rod was systematically overestimated by ~10 nm at both probing resolutions of AFM. Nevertheless, the effects of tip convolution were less severe in vertical orientation so that the estimated height of TMV by AFM imaging was in close agreement with the in silico X-ray topograph. Using dedicated image processing algorithms, we found that at low resolution (i.e., 1.95 nm per pixel), the extracted surface features of TMV can be interpreted as a partial or full helical repeat (three complete turns with ~7.0 nm in length), while individual protein subunits (~2.5 nm) were perceivable only at high resolution. The present study shows that the scales of revealed structural features in AFM images are subject to both probing resolution and processing algorithms for image analysis.

DockAFM: benchmarking protein structures by docking under AFM topographs

Proteins can adopt a variety of conformations. We present a simple server for scoring the agreement between 3D atomic structures and experimental envelopes obtained by atomic force microscopy. Three different structures of immunoglobulins (IgG) or blood coagulation factor V activated were tested and their agreement with several topographical surfaces was computed. This approach can be used to test structural variability within a family of proteins.

AVAILABILITY AND IMPLEMENTATION

DockAFM is available at http://biodev.cea.fr/dockafm.

DOT2: Macromolecular docking with improved biophysical models

Computational docking is a useful tool for predicting macromolecular complexes, which are often difficult to determine experimentally. Here, we present the DOT2 software suite, an updated version of the DOT intermolecular docking program. DOT2 provides straightforward, automated construction of improved biophysical models based on molecular coordinates, offering checkpoints that guide the user to include critical features. DOT has been updated to run more quickly, allow flexibility in grid size and spacing, and generate an infinitive complete list of favorable candidate configurations. Output can be filtered by experimental data and rescored by the sum of electrostatic and atomic desolvation energies. We show that this rescoring method improves the ranking of correct complexes for a wide range of macromolecular interactions and demonstrate that biologically relevant models are essential for biologically relevant results. The flexibility and versatility of DOT2 accommodate realistic models of complex biological systems, improving the likelihood of a successful docking outcome.

Adepth: New Representation and its implications for atomic depths of macromolecules

We applied the signed distance function (SDF) for representing the depths of atoms in a macromolecule. The calculations of SDF values were performed on grid points in a rectangular box that accommodates the macromolecule. The depth for an atom inside the molecule was then obtained as a result of tri-linear interpolation of SDF values at the nearest grid points surrounding the atom. For testing the performance of present program Adepth, we have constructed an artificial molecule whose atomic depths are known as the gold standard for accuracy assessments. On average, our results showed that Adepth reached an accuracy of 1.6% at 0.5 Å of grid spacing, whereas the current reference server DEPTH reached 7.5%. The Adepth program provides both depth and height representations; it is capable of computing iso-surfaces for atomic depths and presenting graphical view of macromolecular shape at some distance away from the surface. Web interface is available at http://biodev.cea.fr/adepth.

A method for integrative structure determination of protein-protein complexes

MOTIVATION

Structural characterization of protein interactions is necessary for understanding and modulating biological processes. On one hand, X-ray crystallography or NMR spectroscopy provide atomic resolution structures but the data collection process is typically long and the success rate is low. On the other hand, computational methods for modeling assembly structures from individual components frequently suffer from high false-positive rate, rarely resulting in a unique solution.

RESULTS

Here, we present a combined approach that computationally integrates data from a variety of fast and accessible experimental techniques for rapid and accurate structure determination of protein-protein complexes. The integrative method uses atomistic models of two interacting proteins and one or more datasets from five accessible experimental techniques: a small-angle X-ray scattering (SAXS) profile, 2D class average images from negative-stain electron microscopy micrographs (EM), a 3D density map from single-particle negative-stain EM, residue type content of the protein-protein interface from NMR spectroscopy and chemical cross-linking detected by mass spectrometry. The method is tested on a docking benchmark consisting of 176 known complex structures and simulated experimental data. The near-native model is the top scoring one for up to 61% of benchmark cases depending on the included experimental datasets; in comparison to 10% for standard computational docking. We also collected SAXS, 2D class average images and 3D density map from negative-stain EM to model the PCSK9 antigen-J16 Fab antibody complex, followed by validation of the model by a subsequently available X-ray crystallographic structure.