DockAFM: benchmarking protein structures by docking under AFM topographs

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.

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.

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.

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.

Nano-Surveillance: Tracking Individual Molecules in a Sea of Chromatin

Chromatin is the epigenomic platform for diverse nuclear processes such as DNA repair, replication, transcription, telomere, and centromere function. In cancer cells, mutations in key processes result in DNA amplification, chromosome translocations, and chromothripsis, severely distorting the natural chromatin state. In normal and diseased states, dozens of chromatin effectors alter the physical integrity and dynamics of chromatin at the level of both single nucleosomes and arrays of nucleosomes folded into 3-dimensional shapes. Integrating these length scales, from the 10 nm sized nucleosome to mitotic chromosomes, whilst jostling within the crowded environment of the cell, cannot yet be achieved by a single technology. In this review, we discuss tools that have proven powerful in the investigation of nucleosome and chromatin fiber dynamics. We also provide a deeper focus into atomic force microscopy (AFM) applications that can bridge diverse length and time scales. Using time course AFM, we observe that chromatin condensation by H1.5 is dynamic, whereas using nano-indentation force spectroscopy we observe that both histone variants and nucleosome binding partners alter material properties of individual nucleosomes. Finally, we demonstrate how high-speed AFM can visualize plasmid DNA dynamics, intermittent nucleosome-nucleosome contacts, and changes in nucleosome phasing along a contiguous chromatin fiber. Altogether, the development of innovative technologies holds the promise of revealing the secret lives of nucleosomes, potentially bridging the gaps in our understanding of how chromatin works within living cells and tissues.

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.

The Plasma Factor XIII Heterotetrameric Complex Structure: Unexpected Unequal Pairing within a Symmetric Complex

Factor XIII (FXIII) is a predominant determinant of clot stability, strength, and composition. Plasma FXIII circulates as a pro-transglutaminase with two catalytic A subunits and two carrier-protective B subunits in a heterotetramer (FXIII-A₂B₂). FXIII-A₂ and -B₂ subunits are synthesized separately and then assembled in plasma. Following proteolytic activation by thrombin and calcium-mediated dissociation of the B subunits, activated FXIII (FXIIIa) covalently cross links fibrin, promoting clot stability. The zymogen and active states of the FXIII-A subunits have been structurally characterized; however, the structure of FXIII-B subunits and the FXIII-A₂B₂ complex have remained elusive. Using integrative hybrid approaches including atomic force microscopy, cross-linking mass spectrometry, and computational approaches, we have constructed the first all-atom model of the FXIII-A₂B₂ complex. We also used molecular dynamics simulations in combination with isothermal titration calorimetry to characterize FXIII-A₂B₂ assembly, activation, and dissociation. Our data reveal unequal pairing of individual subunit monomers in an otherwise symmetric complex, and suggest this unusual structure is critical for both assembly and activation of this complex. Our findings enhance understanding of mechanisms associating FXIII-A₂B₂ mutations with disease and have important implications for the rational design of molecules to alter FXIII assembly or activity to reduce bleeding and thrombotic complications.

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.

Molecular Docking at a Glance

The current chapter introduces different aspects of molecular docking technique in order to give an overview to the readers about the topics which will be dealt with throughout this volume. Like many other fields of science, molecular docking studies has experienced a lagging period of slow and steady increase in terms of acquiring attention of scientific community as well as its frequency of application, followed by a pronounced era of exponential expansion in theory, methodology, areas of application and performance due to developments in related technologies such as computational resources and theoretical as well as experimental biophysical methods. In the following sections the evolution of molecular docking will be reviewed and its different components including methods, search algorithms, scoring functions, validation of the methods, and area of applications plus few case studies will be touched briefly.

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.

Adsorption orientations and immunological recognition of antibodies on graphene

Large-scale molecular dynamics (MD) simulations and atomic force microscopy (AFM) in liquid are combined to characterize the adsorption of Immunoglobulin G (IgG) antibodies over a hydrophobic surface modeled with a three-layer graphene slab. We consider explicitly the water solvent, simulating systems with massive sizes (up to 770 000 atoms), for four different adsorption orientations. Protocols based on steered MD to speed up the protein diffusion stage and to enhance the dehydration process are combined with long simulation times (>150 ns) in order to make sure that the final adsorption states correspond to actual stable configurations. Our MD results and the AFM images demonstrate that the IgG antibodies are strongly adsorbed, do not unfold, and retain their secondary and tertiary structures upon deposition. Statistical analysis of the AFM images shows that many of the antibodies adopt vertical orientations, even at very small coverages, which expose at least one Fab binding site for recognition events. Single molecule force spectroscopy experiments demonstrate the immunological response of the deposited antibodies by recognizing its specific antigens. The above properties together with the strong anchoring and preservation of the secondary structure, make graphene an excellent candidate for the development of immunosensors.