Browse
Submit Data
Databases
API
Help

Dataset Information

24 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Case Report: Bayesian Statistical Inference of Experimental Parameters via Biomolecular Simulations: Atomic Force Microscopy.

ABSTRACT: The atomic force microscopy (AFM) is a powerful tool for imaging structures of molecules bound on surfaces. To gain high-resolution structural information, one often superimposes structure models on the measured images. Motivated by high flexibility of biomolecules, we previously developed a flexible-fitting molecular dynamics (MD) method that allows protein structural changes upon superimposing. Since the AFM image largely depends on the AFM probe tip geometry, the fitting process requires accurate estimation of the parameters related to the tip geometry. Here, we performed a Bayesian statistical inference to estimate a tip radius of the AFM probe from a given AFM image via flexible-fitting molecular dynamics (MD) simulations. We first sampled conformations of the nucleosome that fit well the reference AFM image by the flexible-fitting with various tip radii. We then estimated an optimal tip parameter by maximizing the conditional probability density of the AFM image produced from the fitted structure.

SUBMITTER: Fuchigami S

PROVIDER: S-EPMC7987833 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

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

Project description: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.

| S-EPMC8959186 | biostudies-literature

Streptavidin 2D crystal substrates for visualizing biomolecular processes by atomic force microscopy.

Project description:Flat substrate surfaces are a key to successful imaging of biological macromolecules by atomic force microscopy (AFM). Although usable substrate surfaces have been prepared for still imaging of immobilized molecules, surfaces that are more suitable have recently been required for dynamic imaging to accompany the progress of the scan speed of AFM. In fact, the state-of-the-art high-speed AFM has achieved temporal resolution of 30 ms, a capacity allowing us to trace molecular processes played by biological macromolecules. Here, we characterize three types of streptavidin two-dimensional crystals as substrates, concerning their qualities of surface roughness, uniformity, stability, and resistance to nonspecific protein adsorption. These crystal surfaces are commonly resistant to nonspecific protein adsorption, but exhibit differences in other properties to some extent. These differences must be taken into consideration, but these crystal surfaces are still useful for dynamic AFM imaging, as demonstrated by observation of calcium-induced changes in calmodulin, GroES binding to GroEL, and actin polymerization on the surfaces.

| S-EPMC2764062 | biostudies-literature

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

Project description: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.

| S-EPMC8323932 | biostudies-literature

Localization atomic force microscopy.

Project description:Understanding structural dynamics of biomolecules at the single-molecule level is vital to advancing our knowledge of molecular mechanisms. Currently, there are few techniques that can capture dynamics at the sub-nanometre scale and in physiologically relevant conditions. Atomic force microscopy (AFM)1 has the advantage of analysing unlabelled single molecules in physiological buffer and at ambient temperature and pressure, but its resolution limits the assessment of conformational details of biomolecules2. Here we present localization AFM (LAFM), a technique developed to overcome current resolution limitations. By applying localization image reconstruction algorithms3 to peak positions in high-speed AFM and conventional AFM data, we increase the resolution beyond the limits set by the tip radius, and resolve single amino acid residues on soft protein surfaces in native and dynamic conditions. LAFM enables the calculation of high-resolution maps from either images of many molecules or many images of a single molecule acquired over time, facilitating single-molecule structural analysis. LAFM is a post-acquisition image reconstruction method that can be applied to any biomolecular AFM dataset.

| S-EPMC8697813 | biostudies-literature

Back to BaySICS: a user-friendly program for Bayesian Statistical Inference from Coalescent Simulations.

Project description:Inference of population demographic history has vastly improved in recent years due to a number of technological and theoretical advances including the use of ancient DNA. Approximate Bayesian computation (ABC) stands among the most promising methods due to its simple theoretical fundament and exceptional flexibility. However, limited availability of user-friendly programs that perform ABC analysis renders it difficult to implement, and hence programming skills are frequently required. In addition, there is limited availability of programs able to deal with heterochronous data. Here we present the software BaySICS: Bayesian Statistical Inference of Coalescent Simulations. BaySICS provides an integrated and user-friendly platform that performs ABC analyses by means of coalescent simulations from DNA sequence data. It estimates historical demographic population parameters and performs hypothesis testing by means of Bayes factors obtained from model comparisons. Although providing specific features that improve inference from datasets with heterochronous data, BaySICS also has several capabilities making it a suitable tool for analysing contemporary genetic datasets. Those capabilities include joint analysis of independent tables, a graphical interface and the implementation of Markov-chain Monte Carlo without likelihoods.

| S-EPMC4035278 | biostudies-literature

Electrostatic Discovery Atomic Force Microscopy.

Project description:While offering high resolution atomic and electronic structure, scanning probe microscopy techniques have found greater challenges in providing reliable electrostatic characterization on the same scale. In this work, we offer electrostatic discovery atomic force microscopy, a machine learning based method which provides immediate maps of the electrostatic potential directly from atomic force microscopy images with functionalized tips. We apply this to characterize the electrostatic properties of a variety of molecular systems and compare directly to reference simulations, demonstrating good agreement. This approach offers reliable atomic scale electrostatic maps on any system with minimal computational overhead.

| S-EPMC8793147 | biostudies-literature

Deep atomic force microscopy.

Project description:The Atomic Force Microscope (AFM) possesses several desirable imaging features including the ability to produce height profiles as well as two-dimensional images, in fluid or air, at high resolution. AFM has been used to study a vast selection of samples on the scale of angstroms to micrometers. However, current AFMs cannot access samples with vertical topography of the order of 100 μm or greater. Research efforts have produced AFM scanners capable of vertical motion greater than 100 μm, but commercially available probe tip lengths are still typically less than 10 μm high. Even the longest probe tips are below 100 μm and even at this range are problematic. In this paper, we present a method to hand-fabricate "Deep AFM" probes with tips of the order of 100 μm and longer so that AFM can be used to image samples with large scale vertical topography, such as fractured bone samples.

| S-EPMC3869821 | biostudies-other

Functional dependence of resonant harmonics on nanomechanical parameters in dynamic mode atomic force microscopy.

Project description:We present a combined theoretical and experimental study of the dependence of resonant higher harmonics of rectangular cantilevers of an atomic force microscope (AFM) as a function of relevant parameters such as the cantilever force constant, tip radius and free oscillation amplitude as well as the stiffness of the sample's surface. The simulations reveal a universal functional dependence of the amplitude of the 6th harmonic (in resonance with the 2nd flexural mode) on these parameters, which can be expressed in terms of a gun-shaped function. This analytical expression can be regarded as a practical tool for extracting qualitative information from AFM measurements and it can be extended to any resonant harmonics. The experiments confirm the predicted dependence in the explored 3-45 N/m force constant range and 2-345 GPa sample's stiffness range. For force constants around 25 N/m, the amplitude of the 6th harmonic exhibits the largest sensitivity for ultrasharp tips (tip radius below 10 nm) and polymers (Young's modulus below 20 GPa).

| S-EPMC5405692 | biostudies-literature

Quantification of Biomolecular Dynamics Inside Real and Synthetic Nuclear Pore Complexes Using Time-Resolved Atomic Force Microscopy.

Project description:Over the past decades, atomic force microscopy (AFM) has emerged as an increasingly powerful tool to study the dynamics of biomolecules at nanometer length scales. However, the more stochastic the nature of such biomolecular dynamics, the harder it becomes to distinguish them from AFM measurement noise. Rapid, stochastic dynamics are inherent to biological systems comprising intrinsically disordered proteins. One role of such proteins is in the formation of the transport barrier of the nuclear pore complex (NPC): the selective gateway for macromolecular traffic entering or exiting the nucleus. Here, we use AFM to observe the dynamics of intrinsically disordered proteins from two systems: the transport barrier of native NPCs and the transport barrier of a mimetic NPC made using a DNA origami scaffold. Analyzing data recorded with 50-200 ms temporal resolution, we highlight the importance of drift correction and appropriate baseline measurements in such experiments. In addition, we describe an autocorrelation analysis to quantify time scales of observed dynamics and to assess their veracity-an analysis protocol that lends itself to the quantification of stochastic fluctuations in other biomolecular systems. The results reveal the surprisingly slow rate of stochastic, collective transitions inside mimetic NPCs, highlighting the importance of FG-nup cohesive interactions.

| S-EPMC6660115 | biostudies-literature

Polynomial force approximations and multifrequency atomic force microscopy.

Project description:We present polynomial force reconstruction from experimental intermodulation atomic force microscopy (ImAFM) data. We study the tip-surface force during a slow surface approach and compare the results with amplitude-dependence force spectroscopy (ADFS). Based on polynomial force reconstruction we generate high-resolution surface-property maps of polymer blend samples. The polynomial method is described as a special example of a more general approximative force reconstruction, where the aim is to determine model parameters that best approximate the measured force spectrum. This approximative approach is not limited to spectral data, and we demonstrate how it can be adapted to a force quadrature picture.

| S-EPMC3701428 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data