176 research outputs found
Recommended from our members
Changing the Receptor Specificity of Anthrax Toxin
The actions of many bacterial toxins depend on their ability to bind to one or more cell-surface receptors. Anthrax toxin acts by a sequence of events that begins when the protective-antigen (PA) moiety of the toxin binds to either one of two cell-surface proteins, ANTXR1 and ANTXR2, and is proteolytically activated. The activated PA self-associates to form oligomeric pore precursors, which, in turn, bind the enzymatic moieties of the toxin and transport them to the cytosol. We introduced a double mutation into domain 4 of PA to ablate its native receptor-binding function and fused epidermal growth factor (EGF) to the C terminus of the mutated protein. The resulting fusion protein transported enzymatic effector proteins into a cell line that expressed the EGF receptor (A431 cells), but not into a line lacking this receptor (CHO-K1 cells). Addition of excess free EGF blocked transport of effector proteins into A431 cells via the fusion protein, but not via native PA. We also showed that fusing the diphtheria toxin receptor-binding domain to the C terminus of the mutated PA channeled effector-protein transport through the diphtheria toxin receptor. PA fusion proteins with altered receptor specificity may be useful in biological research and could have practical applications, including ablation or perturbation of selected populations of cells in vivo
Accurate Estimation of Diffusion Coefficients and their Uncertainties from Computer Simulation
Self-diffusion coefficients, , are routinely estimated from molecular
dynamics simulations by fitting a linear model to the observed mean-squared
displacements (MSDs) of mobile species. MSDs derived from simulation suffer
from statistical noise, which introduces uncertainty in the resulting estimate
of . An optimal scheme for estimating will minimise this
uncertainty, i.e., will have high statistical efficiency, and will give an
accurate estimate of the uncertainty itself. We present a scheme for estimating
from a single simulation trajectory with high statistical efficiency and
accurately estimating the uncertainty in the predicted value. The statistical
distribution of MSDs observable from a given simulation is modelled as a
multivariate normal distribution using an analytical covariance matrix for an
equivalent system of freely diffusing particles, which we parameterise from the
available simulation data. We then perform Bayesian regression to sample the
distribution of linear models that are compatible with this model multivariate
normal distribution, to obtain a statistically efficient estimate of and
an accurate estimate of the associated statistical uncertainty
Self-assembly of collagen molecules into fibrils in solution
Type I collagen is a major constituent of many biological tissues, including skin, bone, tendon and cartilages. Its main functions are to shape extracellular matrices, promote cell attachment and provide tissues with strength, flexibility and elasticity. At the core these functions is its remarkable ability of collagen to form highly organized fibrils through the self-assembly of the molecules. The fibrilogenesis involves the lateral association of collagen triple helices into staggered parallel arrays that give rise to the characteristic D-band periodicity of 67 nm. Currently, the mechanisms of collagen self-assembly are poorly understood. Here, we combine the nanometer-scale resolution of cryo-transmission electron microscopy (cryoTEM) with molecular dynamics to investigate the self-assembly of collagen molecules into fibrils in solution
Kinisi:Bayesian analysis of mass transport from molecular dynamics simulations
kinisi is a Python package for estimating transport coefficients—e.g., self-diffusion coefficients, ∗—and their corresponding uncertainties from molecular dynamics simulation data. It includes an implementation of the approximate Bayesian regression scheme described in McCluskey etal. (2023), wherein the mean-squared displacement (MSD) of mobile atoms is modelled as a multivariate normal distribution that is parametrised from the input simulation data. kinisi uses Markov-chain Monte Carlo (Foreman-Mackey et al., 2019; Goodman & Weare, 2010) to sample this model multivariate normal distribution to give a posterior distribution of linear model ensemble MSDs that are compatible with the observed simulation data. For each linear ensemble MSD, x(), a corresponding estimate of the diffusion coefficient, ̂∗ is given via the Einstein relation, ̂∗ =1d x() / 6 d where is time. The posterior distribution of compatible model ensemble MSDs calculated by kinisi gives a point estimate for the most probable value of ∗ , given the observed simulation data, and an estimate of the corresponding uncertainty in ̂∗. kinisi also provides equivalent functionality for estimating collective transport coefficients, i.e., jump-diffusion coefficients and ionic conductivities<br/
Assessing molecular simulation for the analysis of lipid monolayer reflectometry
Using molecular simulation to aid in the analysis of neutron reflectometry
measurements is commonplace. However, reflectometry is a tool to probe
large-scale structures, and therefore the use of all-atom simulation may be
irrelevant. This work presents the first direct comparison between the
reflectometry profiles obtained from different all-atom and coarse-grained
molecular dynamics simulations. These are compared with a traditional model
layer structure analysis method to determine the minimum simulation resolution
required to accurately reproduce experimental data. We find that systematic
limits reduce the efficacy of the MARTINI potential model, while the Berger
united-atom and Slipids all-atom potential models agree similarly well with the
experimental data. The model layer structure gives the best agreement, however,
the higher resolution simulation-dependent methods produce an agreement that is
comparable. Finally, we use the atomistic simulation to advise on possible
improvements that may be offered to the model layer structures, creating a more
realistic monolayer model.Comment: Electronic Supplementary Information (ESI) available: All
analysis/plotting scripts and figure files, allowing for a fully
reproducible, and automated, analysis workflow for the work presented is
available at \url{https://github.com/arm61/sim_vs_trad} (DOI:
10.5281/zenodo.2600729) under a CC BY-SA 4.0 licens
Advice on describing Bayesian analysis of neutron and X-ray reflectometry
Driven by the availability of modern software and hardware, Bayesian analysis
is becoming more popular in neutron and X-ray reflectometry analysis. The
understandability and replicability of these analyses may be harmed by
inconsistencies in how the probability distributions central to Bayesian
methods are represented in the literature. Herein, we provide advice on how to
report the results of Bayesian analysis as applied to neutron and X-ray
reflectometry. This includes the clear reporting of initial starting
conditions, the prior probabilities, and results of any analysis, and the
posterior probabilities that are the Bayesian equivalent of the error bar, to
enable replicability and improve understanding. We believe that this advice,
grounded in our experience working in the field, will enable greater analytical
reproducibility among the reflectometry community, as well as improve the
quality and usability of results
Overscreening and Underscreening in Solid-Electrolyte Grain Boundary Space-Charge Layers
Polycrystalline solids can exhibit material properties that differ
significantly from those of equivalent single-crystal samples, in part, because
of a spontaneous redistribution of mobile point defects into so-called
space-charge regions adjacent to grain boundaries. The general analytical form
of these space-charge regions is known only in the dilute limit, where
defect-defect correlations can be neglected. Using kinetic Monte Carlo
simulations of a three-dimensional Coulomb lattice gas, we show that
grain-boundary space-charge regions in non-dilute solid electrolytes exhibit
overscreening -- damped oscillatory space-charge profiles -- and underscreening
-- decay lengths that are longer than the corresponding Debye length and that
increase with increasing defect-defect interaction strength. Overscreening and
underscreening are known phenomena in concentrated liquid electrolytes, and the
observation of functionally analogous behaviour in solid electrolyte
space-charge regions suggests that the same underlying physics drives behaviour
in both classes of systems. We therefore expect theoretical approaches
developed to study non-dilute liquid electrolytes to be equally applicable to
future studies of solid electrolytes
An introduction to classical molecular dynamics simulation for experimental scattering users
Classical molecular dynamics simulations are a common component of
multi-modal analyses from scattering measurements, such as small-angle
scattering and diffraction. Users of these experimental techniques often have
no formal training in the theory and practice of molecular dynamics simulation,
leading to the possibility of these simulations being treated as a "black box"
analysis technique. In this article, we describe an open educational resource
(OER) designed to introduce classical molecular dynamics to users of scattering
methods. This resource is available as a series of interactive web pages, which
can be easily accessed by students, and as an open source software repository,
which can be freely copied, modified, and redistributed by educators. The topic
covered in this OER includes classical atomistic modelling, parameterising
interatomic potentials, molecular dynamics simulations, typical sources of
error, and some of the approaches to using simulations in the analysis of
scattering data.Comment: Electronic Supplementary Information (ESI) available: All
analysis/plotting scripts and figure files, allowing for a fully
reproducible, and automated, analysis workflow for the work presented is
available at \url{https://github.com/arm61/sim_and_scat_paper} (DOI:
10.5281/zenodo.2556826) under a CC BY-SA 4.0 licens
- …