Publications by authors named "MC Payne"

Lineage transitions are a central feature of prostate development, tumourigenesis and treatment resistance. While epigenetic changes are well known to drive prostate lineage transitions, it remains unclear how upstream metabolic signalling contributes to the regulation of prostate epithelial identity. To fill this gap, we developed an approach to perform metabolomics on primary prostate epithelial cells.

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What makes an agonist and a competitive antagonist? In this work, we aim to answer this question by performing parallel tempering Monte Carlo simulations on the serotonin type 3A (5-HT) receptor. We use linear response theory to predict conformational changes in the 5-HT receptor active site after weak perturbations are applied to its allosteric binding sites. A covariance tensor is built from conformational sampling of its apo state, and a harmonic approximation allows us to substitute the calculation of ligand-induced forces with the binding site's displacement vector.

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Artificial intelligence (AI) image translation has been a valuable tool for processing image data in biological and medical research. To apply such a tool in mission-critical applications, including drug screening, toxicity study, and clinical diagnostics, it is essential to ensure that the AI prediction is trustworthy. Here, we demonstrate that an ensemble learning method can quantify the uncertainty of AI image translation.

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3D cancer spheroids represent a highly promising model for study of cancer progression and therapeutic development. Wide-scale adoption of cancer spheroids, however, remains a challenge due to the lack of control over hypoxic gradients that may cloud the assessment of cell morphology and drug response. Here, we present a Microwell Flow Device (MFD) that generates in-well laminar flow around 3D tissues via repetitive tissue sedimentation.

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Charge transport properties in single-walled carbon nanotubes (SWCNTs) can be significantly modified through doping, tuning their electrical and thermoelectric properties. In our study, we used more than 40 nitrogen-bearing compounds as dopants and determined their impact on the material's electrical conductivity. The application of nitrogen compounds of diverse structures and electronic configurations enabled us to determine how the dopant nature affects the SWCNTs.

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We have performed fully atomistic molecular dynamics simulations of the intracellular domain of a model of the GABA receptor with and without the GABA receptor associated protein (GABARAP) bound. We have also calculated the electrostatic potential due to the receptor, in the absence and presence of GABARAP. We find that GABARAP binding changes the electrostatic properties around the GABA receptor and could lead to increased conductivity of chloride ions through the receptor.

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Combined molecular dynamics (MD) and quantum mechanics (QM) simulation procedures have gained popularity in modeling the spectral properties of functional organic molecules. However, the potential energy surfaces used to propagate long-time scale dynamics in these simulations are typically described using general, transferable force fields designed for organic molecules in their electronic ground states. These force fields do not typically include spectroscopic data in their training, and importantly, there is no general protocol for including changes in geometry or intermolecular interactions with the environment that may occur upon electronic excitation.

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Carbon nanotubes (CNTs) are materials with exceptional electrical, thermal, mechanical, and optical properties. Ever since it was demonstrated that they also possess interesting thermoelectric properties, they have been considered a promising solution for thermal energy harvesting. In this study, we present a simple method to enhance their performance.

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Mesenchymal stromal cells (MSCs) are multipotent cells that have great potential for regenerative medicine, tissue repair, and immunotherapy. Unfortunately, the outcomes of MSC-based research and therapies can be highly inconsistent and difficult to reproduce, largely due to the inherently significant heterogeneity in MSCs, which has not been well investigated. To quantify cell heterogeneity, a standard approach is to measure marker expression on the protein level via immunochemistry assays.

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The fundamental colloidal properties of pristine graphene flakes remain incompletely understood, with conflicting reports about their chemical character, hindering potential applications that could exploit the extraordinary electronic, thermal, and mechanical properties of graphene. Here, the true amphipathic nature of pristine graphene flakes is demonstrated through wet-chemistry testing, optical microscopy, electron microscopy, and density functional theory, molecular dynamics, and Monte Carlo calculations, and it is shown how this fact paves the way for the formation of ultrastable water/oil emulsions. In contrast to commonly used graphene oxide flakes, pristine graphene flakes possess well-defined hydrophobic and hydrophilic regions: the basal plane and edges, respectively, the interplay of which allows small flakes to be utilized as stabilizers with an amphipathic strength that depends on the edge-to-surface ratio.

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First-principles electronic structure calculations are now accessible to a very large community of users across many disciplines, thanks to many successful software packages, some of which are described in this special issue. The traditional coding paradigm for such packages is monolithic, i.e.

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We have performed a parallel tempering crankshaft motion Monte Carlo simulation on a model of the GABA type A receptor with the aim of exploring a wide variety of local conformational space. We develop a novel method to analyse the protein movements in terms of a correlation tensor and use this to explore the gating process, that is, how agonist binding could cause ion channel opening. We find that simulated binding impulses to varying clusters of GABA binding site residues produce channel opening, and that equivalent impulses to single GABA sites produce partial opening.

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We introduce the unification of dynamical mean field theory (DMFT) and linear-scaling density functional theory (DFT), as recently implemented in ONETEP, a linear-scaling DFT package, and TOSCAM, a DMFT toolbox. This code can account for strongly correlated electronic behavior while simultaneously including the effects of the environment, making it ideally suited for studying complex and heterogeneous systems that contain transition metals and lanthanides, such as metalloproteins. We systematically introduce the necessary formalism, which must account for the nonorthogonal basis set used by ONETEP.

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We present an overview of the onetep program for linear-scaling density functional theory (DFT) calculations with large basis set (plane-wave) accuracy on parallel computers. The DFT energy is computed from the density matrix, which is constructed from spatially localized orbitals we call Non-orthogonal Generalized Wannier Functions (NGWFs), expressed in terms of periodic sinc (psinc) functions. During the calculation, both the density matrix and the NGWFs are optimized with localization constraints.

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Molecular mechanics force field parameters for macromolecules, such as proteins, are traditionally fit to reproduce experimental properties of small molecules, and thus, they neglect system-specific polarization. In this paper, we introduce a complete protein force field that is designed to be compatible with the quantum mechanical bespoke (QUBE) force field by deriving nonbonded parameters directly from the electron density of the specific protein under study. The main backbone and sidechain protein torsional parameters are rederived in this work by fitting to quantum mechanical dihedral scans for compatibility with QUBE nonbonded parameters.

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A new framework is presented for evaluating the performance of self-consistent field methods in Kohn-Sham density functional theory (DFT). The aims of this work are two-fold. First, we explore the properties of Kohn-Sham DFT as it pertains to the convergence of self-consistent field iterations.

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Hydration sites are locations of interest to water and they can be used to classify the behavior of water around chemical motifs commonly found on the surface of proteins. Inhomogeneous fluid solvation theory (IFST) is a method for calculating hydration free-energy changes from molecular dynamics (MD) trajectories. In this paper, hydration sites are identified from MD simulations of 380 diverse protein structures.

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We have performed docking simulations on GABARAP interacting with the GABA type A receptor using SwarmDock. We have also used a novel method to study hydration sites on the surface of these two proteins; this method identifies regions around proteins where desolvation is relatively easy, and these are possible locations where proteins can bind each other. There is a high degree of consistency between the predictions of these two methods.

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Observation of excitonic quantum beats in photosynthetic antennae has prompted wide debate regarding the function of excitonic coherence in pigment-protein complexes. Much of this work focuses on the interactions of excitons with the femto-to-picosecond dynamical fluctuations of their environment. However, in experiments these effects can be masked by static disorder of the excited-state energies across ensembles, whose microscopic origins are challenging to predict.

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The solvatochromic shift, as well as the change in colour of the simple organic dye nile red, is studied in two polar and two non-polar solvents in the context of large-scale time-dependent density-functional theory (TDDFT) calculations treating large parts of the solvent environment from first principles. We show that an explicit solvent representation is vital to resolve absorption peak shifts between nile red in n-hexane and toluene, as well as acetone and ethanol. The origin of the failure of implicit solvent models for these solvents is identified as being due to the strong solute-solvent interactions in form of π-stacking and hydrogen bonding in the case of toluene and ethanol.

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The use of principal component analysis (PCA) to statistically infer features of local structure from experimental pair distribution function (PDF) data is assessed on a case study of rare-earth phosphate glasses (REPGs). Such glasses, codoped with two rare-earth ions (R and R') of different sizes and optical properties, are of interest to the laser industry. The determination of structure-property relationships in these materials is an important aspect of their technological development.

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