Browse
Submit Data
Databases
API
Help

Dataset Information

28 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Kinetic Monte Carlo simulations of the assembly of filamentous biomacromolecules by dimer addition mechanism.

ABSTRACT: In cells, several important biomacromolecules form oligomers through a dimer addition mechanism. Rate equations based on mean field approximations are usually employed to describe the assembly process. However, such equations often require multiple assumptions that mask some detailed changes of the biomolecular configurations during aggregations. Here, we present a Kinetic Monte Carlo simulation scheme to account for the diffusion and rotation of dimers on two-dimensional hexagonal lattices while naturally including the stochastic features. We investigate the effects of the interaction energy between dimers, the diffusion coefficient and the concentration of dimers on the aggregation by dimer addition mechanism. Our simulations identified unusual double-S shape evolutions of aggregation kinetics, which are probably associated with the formation of metastable clusters.

SUBMITTER: Luo T

PROVIDER: S-EPMC4283931 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Kinetic Monte Carlo simulations of the assembly of filamentous biomacromolecules by dimer addition mechanism.

Luo Tianzhi T Robinson Douglas N DN

RSC advances 20150101 6

In cells, several important biomacromolecules form oligomers through a dimer addition mechanism. Rate equations based on mean field approximations are usually employed to describe the assembly process. However, such equations often require multiple assumptions that mask some detailed changes of the biomolecular configurations during aggregations. Here, we present a Kinetic Monte Carlo simulation scheme to account for the diffusion and rotation of dimers on two-dimensional hexagonal lattices whil ...[more]

PMID: 25574377

Similar Datasets

A Practical Guide to Surface Kinetic Monte Carlo Simulations.

Project description:This review article is intended as a practical guide for newcomers to the field of kinetic Monte Carlo (KMC) simulations, and specifically to lattice KMC simulations as prevalently used for surface and interface applications. We will provide worked out examples using the kmos code, where we highlight the central approximations made in implementing a KMC model as well as possible pitfalls. This includes the mapping of the problem onto a lattice and the derivation of rate constant expressions for various elementary processes. Example KMC models will be presented within the application areas surface diffusion, crystal growth and heterogeneous catalysis, covering both transient and steady-state kinetics as well as the preparation of various initial states of the system. We highlight the sensitivity of KMC models to the elementary processes included, as well as to possible errors in the rate constants. For catalysis models in particular, a recurrent challenge is the occurrence of processes at very different timescales, e.g., fast diffusion processes and slow chemical reactions. We demonstrate how to overcome this timescale disparity problem using recently developed acceleration algorithms. Finally, we will discuss how to account for lateral interactions between the species adsorbed to the lattice, which can play an important role in all application areas covered here.

| S-EPMC6465329 | biostudies-literature

Metal-Insulator Transition in Nanoparticle Solids: Insights from Kinetic Monte Carlo Simulations.

Project description:Progress has been rapid in increasing the efficiency of energy conversion in nanoparticles. However, extraction of the photo-generated charge carriers remains challenging. Encouragingly, the charge mobility has been improved recently by driving nanoparticle (NP) films across the metal-insulator transition (MIT). To simulate MIT in NP films, we developed a hierarchical Kinetic Monte Carlo transport model. Electrons transfer between neighboring NPs via activated hopping when the NP energies differ by more than an overlap energy, but transfer by a non-activated quantum delocalization, if the NP energies are closer than the overlap energy. As the overlap energy increases, emerging percolating clusters support a metallic transport across the entire film. We simulated the evolution of the temperature-dependent electron mobility. We analyzed our data in terms of two candidate models of the MIT: (a) as a Quantum Critical Transition, signaled by an effective gap going to zero; and (b) as a Quantum Percolation Transition, where a sample-spanning metallic percolation path is formed as the fraction of the hopping bonds in the transport paths is going to zero. We found that the Quantum Percolation Transition theory provides a better description of the MIT. We also observed an anomalously low gap region next to the MIT. We discuss the relevance of our results in the light of recent experimental measurements.

| S-EPMC5539282 | biostudies-literature

Theoretical prediction and atomic kinetic Monte Carlo simulations of void superlattice self-organization under irradiation.

Project description:Nano-structured superlattices may have novel physical properties and irradiation is a powerful mean to drive their self-organization. However, the formation mechanism of superlattice under irradiation is still open for debate. Here we use atomic kinetic Monte Carlo simulations in conjunction with a theoretical analysis to understand and predict the self-organization of nano-void superlattices under irradiation, which have been observed in various types of materials for more than 40 years but yet to be well understood. The superlattice is found to be a result of spontaneous precipitation of voids from the matrix, a process similar to phase separation in regular solid solution, with the symmetry dictated by anisotropic materials properties such as one-dimensional interstitial atom diffusion. This discovery challenges the widely accepted empirical rule of the coherency between the superlattice and host matrix crystal lattice. The atomic scale perspective has enabled a new theoretical analysis to successfully predict the superlattice parameters, which are in good agreement with independent experiments. The theory developed in this work can provide guidelines for designing target experiments to tailor desired microstructure under irradiation. It may also be generalized for situations beyond irradiation, such as spontaneous phase separation with reaction.

| S-EPMC5920090 | biostudies-literature

Data sets of migration barriers for atomistic Kinetic Monte Carlo simulations of Fe self-diffusion.

Project description:Atomistic rigid lattice Kinetic Monte Carlo (KMC) is an efficient method for simulating nano-objects and surfaces at timescales much longer than those accessible by molecular dynamics. A laborious and non-trivial part of constructing any KMC model is, however, to calculate all migration barriers that are needed to give the probabilities for any atom jump event to occur in the simulations. We calculated three data sets of migration barriers for Fe self-diffusion: barriers of first nearest neighbour jumps, second nearest neighbours hop-on jumps on the Fe {100} surface and a set of barriers of the diagonal exchange processes for various cases of the local atomic environments within the 2nn coordination shell.

| S-EPMC5997586 | biostudies-literature

Nativelike topology assembly of small proteins using predicted restraints in Monte Carlo folding simulations.

Project description:By incorporating predicted secondary and tertiary restraints derived from multiple sequence alignments into ab initio folding simulations, it has been possible to assemble native-like tertiary structures for a test set of 19 nonhomologous proteins ranging from 29 to 100 residues in length and representing all secondary structural classes. Secondary structural restraints are provided by the PHD secondary structure prediction algorithm that incorporates multiple sequence information. Multiple sequence alignments also provide predicted tertiary restraints via a two-step process: First, seed side chain contacts are selected from a correlated mutation analysis, and then an inverse folding algorithm expands these seed contacts. The predicted secondary and tertiary restraints are incorporated into a lattice-based, reduced protein model for structure assembly and refinement. The resulting native-like topologies exhibit a coordinate root-mean-square deviation from native for the whole chain between 3.1 and 6.7 A, with values ranging from 2.6 to 4.1 A over approximately 80% of the structure. Overall, this study suggests that the use of restraints derived from multiple sequence alignments combined with a fold assembly algorithm is a promising approach to the prediction of the global topology of small proteins.

| S-EPMC18658 | biostudies-literature

Investigating the mechanism of peptide aggregation: insights from mixed monte carlo-molecular dynamics simulations.

Project description:The early stages of peptide aggregation are currently not accessible by experimental techniques at atomic resolution. In this article, we address this problem through the application of a mixed simulation scheme in which a preliminary coarse-grained Monte Carlo analysis of the free-energy landscape is used to identify representative conformations of the aggregates and subsequent all-atom molecular dynamics simulations are used to analyze in detail possible pathways for the stabilization of oligomers. This protocol was applied to systems consisting of multiple copies of the model peptide GNNQQNY, whose detailed structures in the aggregated state have been recently solved in another study. The analysis of the various trajectories provides dynamical and structural insight into the details of aggregation. In particular, the simulations suggest a hierarchical mechanism characterized by the initial formation of stable parallel beta-sheet dimers and identify the formation of the polar zipper motif as a fundamental feature for the stabilization of initial oligomers. Simulation results are consistent with experimentally derived observations and provide an atomically detailed view of the putative initial stages of fibril formation.

| S-EPMC2480662 | biostudies-literature

Anomalous phase separation in a correlated electron system: Machine-learning-enabled large-scale kinetic Monte Carlo simulations.

Project description:SignificancePhase separation is crucial to the functionalities of many correlated electron materials with notable examples including colossal magnetoresistance in manganites and high-Tc superconductivity in cuprates. However, the nonequilibrium phase-separation dynamics in such systems are poorly understood theoretically, partly because the required multiscale modeling is computationally very demanding. With the aid of machine-learning methods, we have achieved large-scale dynamical simulations in a representative correlated electron system. We observe an unusual relaxation process that is beyond the framework of classical phase-ordering theories. We also uncover a correlation-induced freezing behavior, which could be a generic feature of phase separation in correlated electron systems.

| S-EPMC9170136 | biostudies-literature

Nanoparticle design optimization for enhanced targeting: Monte Carlo simulations.

Project description:Using computer simulations, we systematically studied the influence of different design parameters of a spherical nanoparticle tethered with monovalent ligands on its efficiency of targeting planar cell surfaces containing mobile receptors. We investigate how the nanoparticle affinity can be affected by changing the binding energy, the percent of functionalization by ligands, tether length, grafting density, and nanoparticle core size. In general, using a longer tether length or increasing the number of tethered chains without increasing the number of ligands increases the conformational penalty for tether stretching/compression near the cell surface and leads to a decrease in targeting efficiency. At the same time, using longer tethers or a larger core size allows ligands to interact with receptors over a larger cell surface area, which can enhance the nanoparticle affinity toward the cell surface. We also discuss the selectivity of nanoparticle targeting of cells with a high receptor density. Based on the obtained results, we provide recommendations for improving the nanoparticle binding affinity and selectivity, which can guide future nanoparticle development for diagnostic and therapeutic purposes.

| S-EPMC2999362 | biostudies-literature

Polypeptide-Nanoparticle Interactions and Corona Formation Investigated by Monte Carlo Simulations.

Project description:Biomacromolecule activity is usually related to its ability to keep a specific structure. However, in solution, many parameters (pH, ionic strength) and external compounds (polyelectrolytes, nanoparticles) can modify biomacromolecule structure as well as acid/base properties, thus resulting in a loss of activity and denaturation. In this paper, the impact of neutral and charged nanoparticles (NPs) is investigated by Monte Carlo simulations on polypeptide (PP) chains with primary structure based on bovine serum albumin. The influence of pH, salt valency, and NP surface charge density is systematically studied. It is found that the PP is extended at extreme pH, when no complex formation is observed, and folded at physiological pH. PP adsorption around oppositely-charged NPs strongly limits chain structural changes and modifies its acid/base properties. At physiological pH, the complex formation occurs only with positively-charged NPs. The presence of salts, in particular those with trivalent cations, introduces additional electrostatic interactions, resulting in a mitigation of the impact of negative NPs. Thus, the corona structure is less dense with locally-desorbed segments. On the contrary, very limited impact of salt cation valency is observed when NPs are positive, due to the absence of competitive effects between multivalent cations and NP.

| S-EPMC6432162 | biostudies-literature

Coarse-grained Monte Carlo simulations of mucus: structure, dynamics, and thermodynamics.

Project description:A simple coarse-grained model of mucus structure and dynamics is proposed and evaluated. The model is based on simple cubic, face-centered lattice representation. Mucins are simulated as lattice chains in which each bead of the model chains represents a mucin domain, equivalent to its Kuhn segment. The remaining lattice sites are considered to be occupied by the solvent. Model mucins consist of three types of domains: polar (glycosylated central segments), hydrophobic, and cysteine-rich, located at the terminal part of the mucin chains. The sequence of these domains mimics the sequence of real mucins. Static and dynamic properties of the system were studied by means of Monte Carlo dynamics. It was shown that the model system undergoes sol-gel transition and that the interactions between hydrophobic domains are responsible for the transition and characteristic properties of the dynamic network in the gel phase. Cysteine-rich domains are essential for frictional properties of the system. Structural and dynamic properties of the model mucus observed in simulations are in qualitative agreement with known experimental facts and provide mechanistic explanation of complex properties of real mucus.

| S-EPMC2998598 | 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