Browse
Submit Data
Databases
API
Help

Dataset Information

14 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Multiscale Models for Fibril Formation: Rare Events Methods, Microkinetic Models, and Population Balances.

ABSTRACT: Amyloid fibrils are thought to grow by a two-step dock-lock mechanism. However, previous simulations of fibril formation (i) overlook the bi-molecular nature of the docking step and obtain rates with first-order units, or (ii) superimpose the docked and locked states when computing the potential of mean force for association and thereby muddle the docking and locking steps. Here, we developed a simple microkinetic model with separate locking and docking steps and with the appropriate concentration dependences for each step. We constructed a simple model comprised of chiral dumbbells that retains qualitative aspects of fibril formation. We used rare events methods to predict separate docking and locking rate constants for the model. The rate constants were embedded in the microkinetic model, with the microkinetic model embedded in a population balance model for "bottom-up" multiscale fibril growth rate predictions. These were compared to "top-down" results using simulation data with the same model and multiscale framework to obtain maximum likelihood estimates of the separate lock and dock rate constants. We used the same procedures to extract separate docking and locking rate constants from experimental fibril growth data. Our multiscale strategy, embedding rate theories, and kinetic models in conservation laws should help to extract docking and locking rate constants from experimental data or long molecular simulations with correct units and without compromising the molecular description.

SUBMITTER: Shayesteh Zadeh A

PROVIDER: S-EPMC8234428 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Estimating the probabilities of rare arrhythmic events in multiscale computational models of cardiac cells and tissue.

Project description:Ectopic heartbeats can trigger reentrant arrhythmias, leading to ventricular fibrillation and sudden cardiac death. Such events have been attributed to perturbed Ca2+ handling in cardiac myocytes leading to spontaneous Ca2+ release and delayed afterdepolarizations (DADs). However, the ways in which perturbation of specific molecular mechanisms alters the probability of ectopic beats is not understood. We present a multiscale model of cardiac tissue incorporating a biophysically detailed three-dimensional model of the ventricular myocyte. This model reproduces realistic Ca2+ waves and DADs driven by stochastic Ca2+ release channel (RyR) gating and is used to study mechanisms of DAD variability. In agreement with previous experimental and modeling studies, key factors influencing the distribution of DAD amplitude and timing include cytosolic and sarcoplasmic reticulum Ca2+ concentrations, inwardly rectifying potassium current (IK1) density, and gap junction conductance. The cardiac tissue model is used to investigate how random RyR gating gives rise to probabilistic triggered activity in a one-dimensional myocyte tissue model. A novel spatial-average filtering method for estimating the probability of extreme (i.e. rare, high-amplitude) stochastic events from a limited set of spontaneous Ca2+ release profiles is presented. These events occur when randomly organized clusters of cells exhibit synchronized, high amplitude Ca2+ release flux. It is shown how reduced IK1 density and gap junction coupling, as observed in heart failure, increase the probability of extreme DADs by multiple orders of magnitude. This method enables prediction of arrhythmia likelihood and its modulation by alterations of other cellular mechanisms.

| S-EPMC5689829 | biostudies-literature

Towards gaining sight of multiscale events: utilizing network models and normal modes in hybrid methods.

Project description:With the explosion of normal mode analyses (NMAs) based on elastic network models (ENMs) in the last decade, and the proven precision of MD simulations for visualizing interactions at atomic scale, many hybrid methods have been proposed in recent years. These aim at exploiting the best of both worlds: the atomic precision of MD that often fall short of exploring time and length scales of biological interest, and the capability of ENM-NMA to predict the cooperative and often functional rearrangements of large structures and assemblies, albeit at low resolution. We present an overview of recent progress in the field with examples of successful applications highlighting the utility of such hybrid methods and pointing to emerging future directions guided by advances in experimental characterization of biomolecular systems structure and dynamics.

| S-EPMC7666066 | biostudies-literature

Network Hamiltonian models reveal pathways to amyloid fibril formation.

Project description:Amyloid fibril formation is central to the etiology of a wide range of serious human diseases, such as Alzheimer's disease and prion diseases. Despite an ever growing collection of amyloid fibril structures found in the Protein Data Bank (PDB) and numerous clinical trials, therapeutic strategies remain elusive. One contributing factor to the lack of progress on this challenging problem is incomplete understanding of the mechanisms by which these locally ordered protein aggregates self-assemble in solution. Many current models of amyloid deposition diseases posit that the most toxic species are oligomers that form either along the pathway to forming fibrils or in competition with their formation, making it even more critical to understand the kinetics of fibrillization. A recently introduced topological model for aggregation based on network Hamiltonians is capable of recapitulating the entire process of amyloid fibril formation, beginning with thousands of free monomers and ending with kinetically accessible and thermodynamically stable amyloid fibril structures. The model can be parameterized to match the five topological classes encompassing all amyloid fibril structures so far discovered in the PDB. This paper introduces a set of network statistical and topological metrics for quantitative analysis and characterization of the fibrillization mechanisms predicted by the network Hamiltonian model. The results not only provide insight into different mechanisms leading to similar fibril structures, but also offer targets for future experimental exploration into the mechanisms by which fibrils form.

| S-EPMC7515878 | biostudies-literature

ATUM-FIB microscopy for targeting and multiscale imaging of rare events in mouse cortex.

Project description:Here, we describe a detailed workflow for ATUM-FIB microscopy, a hybrid method that combines serial-sectioning scanning electron microscopy (SEM) with focused ion beam SEM (FIB-SEM). This detailed protocol is optimized for mouse cortex samples. The main processing steps include the generation of semi-thick sections from sequentially cured resin blocks using a heated microtomy approach. We demonstrate the different imaging modalities, including serial light and electron microscopy for target recognition and FIB-SEM for isotropic imaging of regions of interest. For complete details on the use and execution of this protocol, please refer to Kislinger et al. (2020).

| S-EPMC7757728 | biostudies-literature

Population of nonnative states of lysozyme variants drives amyloid fibril formation.

Project description:The propensity of protein molecules to self-assemble into highly ordered, fibrillar aggregates lies at the heart of understanding many disorders ranging from Alzheimer's disease to systemic lysozyme amyloidosis. In this paper we use highly accurate kinetic measurements of amyloid fibril growth in combination with spectroscopic tools to quantify the effect of modifications in solution conditions and in the amino acid sequence of human lysozyme on its propensity to form amyloid fibrils under acidic conditions. We elucidate and quantify the correlation between the rate of amyloid growth and the population of nonnative states, and we show that changes in amyloidogenicity are almost entirely due to alterations in the stability of the native state, while other regions of the global free-energy surface remain largely unmodified. These results provide insight into the complex dynamics of a macromolecule on a multidimensional energy landscape and point the way for a better understanding of amyloid diseases.

| S-EPMC4982536 | biostudies-literature

Spatial scaling in multiscale models: methods for coupling agent-based and finite-element models of wound healing.

Project description:Multiscale models that couple agent-based modeling (ABM) and finite-element modeling (FEM) allow the dynamic simulation of tissue remodeling and wound healing, with mechanical environment influencing cellular behaviors even as tissue remodeling modifies mechanics. One of the challenges in coupling ABM to FEM is that these two domains typically employ grid or element sizes that differ by several orders of magnitude. Here, we develop and demonstrate an interpolation-based method for mapping between ABM and FEM domains of different resolutions that is suitable for linear and nonlinear FEM meshes and balances accuracy with computational demands. We then explore the effects of refining the FEM mesh and the ABM grid in the setting of a fully coupled model. ABM grid refinement studies showed unexpected effects of grid size whenever cells were present at a high enough density for crowding to affect proliferation and migration. In contrast to an FE-only model, refining the FE mesh in the coupled model increased strain differences between adjacent finite elements. Allowing strain-dependent feedback on collagen turnover magnified the effects of regional heterogeneity, producing highly nonlinear spatial and temporal responses. Our results suggest that the choice of both ABM grid and FEM mesh density in coupled models must be guided by experimental data and accompanied by careful grid and mesh refinement studies in the individual domains as well as the fully coupled model.

| S-EPMC8427702 | biostudies-literature

Collagen fibril formation.

Project description:Collagen is most abundant in animal tissues as very long fibrils with a characteristic axial periodic structure. The fibrils provide the major biomechanical scaffold for cell attachment and anchorage of macromolecules, allowing the shape and form of tissues to be defined and maintained. How the fibrils are formed from their monomeric precursors is the primary concern of this review. Collagen fibril formation is basically a self-assembly process (i.e. one which is to a large extent determined by the intrinsic properties of the collagen molecules themselves) but it is also sensitive to cell-mediated regulation, particularly in young or healing tissues. Recent attention has been focused on "early fibrils' or "fibril segments' of approximately 10 microns in length which appear to be intermediates in the formation of mature fibrils that can grow to be hundreds of micrometers in length. Data from several laboratories indicate that these early fibrils can be unipolar (with all molecules pointing in the same direction) or bipolar (in which the orientation of collagen molecules reverses at a single location along the fibril). The occurrence of such early fibrils has major implications for tissue morphogenesis and repair. In this article we review the current understanding of the origin of unipolar and bipolar fibrils, and how mature fibrils are assembled from early fibrils. We include preliminary evidence from invertebrates which suggests that the principles for bipolar fibril assembly were established at least 500 million years ago.

| S-EPMC1217307 | biostudies-other

Multiscale methods for macromolecular simulations.

Project description:In this article we review the key modeling tools available for simulating biomolecular systems. We consider recent developments and representative applications of mixed quantum mechanics/molecular mechanics (QM/MM), elastic network models (ENMs), coarse-grained molecular dynamics, and grid-based tools for calculating interactions between essentially rigid protein assemblies. We consider how the different length scales can be coupled, both in a sequential fashion (e.g. a coarse-grained or grid model using parameterization from MD simulations), and via concurrent approaches, where the calculations are performed together and together control the progression of the simulation. We suggest how the concurrent coupling approach familiar in the context of QM/MM calculations can be generalized, and describe how this has been done in the CHARMM macromolecular simulation package.

| S-EPMC6407689 | biostudies-literature

Sequence determinants of amyloid fibril formation.

Project description:The establishment of rules that link sequence and amyloid feature is critical for our understanding of misfolding diseases. To this end, we have performed a saturation mutagenesis analysis on the de novo-designed amyloid peptide STVIIE (1). The positional scanning mutagenesis has revealed that there is a position dependence on mutation of amyloid fibril formation and that both very tolerant and restrictive positions to mutation can be found within an amyloid sequence. In this system, mutations that accelerate beta-sheet polymerization do not always lead to an increase of amyloid products. On the contrary, abundant fibrils are typically found for mutants that polymerize slowly. From these experiments, we have extracted a sequence pattern to identify amyloidogenic stretches in proteins. The pattern has been validated experimentally. In silico sequence scanning of amyloid proteins also supports the pattern. Analysis of protein databases has shown that highly amyloidogenic sequences matching the pattern are less frequent in proteins than innocuous amino acid combinations and that, if present, they are surrounded by amino acids that disrupt their aggregating capability (amyloid breakers). This study provides the potential for a proteome-wide scanning to detect fibril-forming regions in proteins, from which molecules can be designed to prevent and/or disrupt this process.

| S-EPMC314143 | biostudies-literature

Sterol-activated amyloid beta fibril formation.

Project description:The metabolic processes that link Alzheimer's disease (AD) to elevated cholesterol levels in the brain are not fully defined. Amyloid beta (Aβ) plaque accumulation is believed to begin decades prior to symptoms and to contribute significantly to the disease. Cholesterol and its metabolites accelerate plaque formation through as-yet-undefined mechanisms. Here, the mechanism of cholesterol (CH) and cholesterol 3-sulfate (CS) induced acceleration of Aβ42 fibril formation is examined in quantitative ligand binding, Aβ42 fibril polymerization, and molecular dynamics studies. Equilibrium and pre-steady-state binding studies reveal that monomeric Aβ42•ligand complexes form and dissociate rapidly relative to oligomerization, that the ligand/peptide stoichiometry is 1-to-1, and that the peptide is likely saturated in vivo. Analysis of Aβ42 polymerization progress curves demonstrates that ligands accelerate polymer synthesis by catalyzing the conversion of peptide monomers into dimers that nucleate the polymerization reaction. Nucleation is accelerated ∼49-fold by CH, and ∼13,000-fold by CS - a minor CH metabolite. Polymerization kinetic models predict that at presumed disease-relevant CS and CH concentrations, approximately half of the polymerization nuclei will contain CS, small oligomers of neurotoxic dimensions (∼12-mers) will contain substantial CS, and fibril-formation lag times will decrease 13-fold relative to unliganded Aβ42. Molecular dynamics models, which quantitatively predict all experimental findings, indicate that the acceleration mechanism is rooted in ligand-induced stabilization of the peptide in non-helical conformations that readily form polymerization nuclei.

| S-EPMC10704437 | 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