The effects of resistant starch and whole grains on appetite, food intake and metabolic response.

With the rise in obesity, there has been an increased interest in foods which may beneficially affect appetite. Resistant starch (RS) and whole grains (of which RS is a main dietary fibre component) have been proposed to affect satiety and therefore may be beneficial in weight management. There is little direct evidence confirming this in humans. Whilst animal data suggest a positive effect of RS on appetite, the few existing human intervention studies provide inconsistent findings. For whole grains the majority of evidence is from epidemiological work as opposed to intervention studies. Therefore a series of studies was conducted to investigate effects of RS and whole grains on appetite and food intake. Two studies were conducted using RS. The first investigated the acute (24 hours) effects of 48 g RS in healthy adult males compared with an energy and available carbohydrate matched placebo. Following RS there was a significantly lower energy intake compared with placebo. There was also a significantly lower postprandial insulin response with RS, possibly explained by increased hepatic insulin clearance determined by a higher C-peptide to insulin ratio. In the second study 40 g RS consumed daily for 4 weeks was compared with the placebo, in overweight and obese participants. Effects on food intake were assessed and a frequently sampled intravenous glucose tolerance test (FSIVGTT) was conducted. This study found no effect on either appetite or energy intake, but did find significantly higher glucose, insulin and C-peptide concentrations, measured during the FSIVGTT, with the RS compared with the placebo, possibly explained by an improved first-phase insulin response. This finding did not translate into differences in parameters obtained from modelling the FSIVGTT data, but this and the lack of appetite and food intake differences could be explained by the small participant numbers. Two intervention studies were conducted with whole grains incorporated into bread rolls. The first, a crossover study, involved 3 weeks' daily consumption of 48 g milled whole grain or control, in young healthy adults. Whilst no significant difference was found between interventions in energy intake or subjective appetite ratings, a significantly lower systolic blood pressure was observed with the milled whole grains. The second was an 8 week parallel study (48 g intact or 48 g milled whole grains or control) in overweight and obese adults. No significant difference was found between groups on energy intake, subjective appetite ratings, cholesterol or postprandial metabolite concentrations. RS appears to be a possible satiating ingredient when consumed acutely and, whilst this was not confirmed in our chronic study, effects may have been masked by small participant numbers. A novel finding from our RS studies was an effect on the insulin response. These studies suggest that RS could have a beneficial role in weight management and favourable metabolic effects. Our whole grain interventions appear not to agree with epidemiological work that suggests a beneficial role on appetite, but there maybe effects on blood pressure regulation. In all instances further investigations are required in other population groups, with more participants and for longer time periods

Dynamic Density Functional Theory of Multicomponent Cellular Membranes

We present a continuum model trained on molecular dynamics (MD) simulations for cellular membranes composed of an arbitrary number of lipid types. The model is constructed within the formalism of dynamic density functional theory and can be extended to include features such as the presence of proteins and membrane deformations. This framework represents a paradigm shift by enabling simulations that can access cellular length-scales ($\mu$m) and time-scales on the order of seconds, all while maintaining near-fidelity to the underlying MD models. Membrane interactions with RAS, a potentially oncogenic protein, are considered as an application. Simulation results are presented and verified with MD simulations, and implications of this new capability are discussed

Identifying Orientation-specific Lipid-protein Fingerprints using Deep Learning

Improved understanding of the relation between the behavior of RAS and RAF proteins and the local lipid environment in the cell membrane is critical for getting insights into the mechanisms underlying cancer formation. In this work, we employ deep learning (DL) to learn this relationship by predicting protein orientational states of RAS and RAS-RAF protein complexes with respect to the lipid membrane based on the lipid densities around the protein domains from coarse-grained (CG) molecular dynamics (MD) simulations. Our DL model can predict six protein states with an overall accuracy of over 80%. The findings of this work offer new insights into how the proteins modulate the lipid environment, which in turn may assist designing novel therapies to regulate such interactions in the mechanisms associated with cancer development

Computational Lipidomics of the Neuronal Plasma Membrane

Membrane lipid composition varies greatly within submembrane compartments, different organelle membranes, and also between cells of different cell stage, cell and tissue types, and organisms. Environmental factors (such as diet) also influence membrane composition. The membrane lipid composition is tightly regulated by the cell, maintaining a homeostasis that, if disrupted, can impair cell function and lead to disease. This is especially pronounced in the brain, where defects in lipid regulation are linked to various neurological diseases. The tightly regulated diversity raises questions on how complex changes in composition affect overall bilayer properties, dynamics, and lipid organization of cellular membranes. Here, we utilize recent advances in computational power and molecular dynamics force fields to develop and test a realistically complex human brain plasma membrane (PM) lipid model and extend previous work on an idealized, "average" mammalian PM. The PMs showed both striking similarities, despite significantly different lipid composition, and interesting differences. The main differences in composition (higher cholesterol concentration and increased tail unsaturation in brain PM) appear to have opposite, yet complementary, influences on many bilayer properties. Both mixtures exhibit a range of dynamic lipid lateral inhomogeneities ("domains"). The domains can be small and transient or larger and more persistent and can correlate between the leaflets depending on lipid mixture, Brain or Average, as well as on the extent of bilayer undulations

OFraMP: a fragment-based tool to facilitate the parametrization of large molecules

An Online tool for Fragment-based Molecule Parametrization (OFraMP) is described. OFraMP is a web application for assigning atomic interaction parameters to large molecules by matching sub-fragments within the target molecule to equivalent sub-fragments within the Automated Topology Builder (ATB, atb.uq.edu.au) database. OFraMP identifies and compares alternative molecular fragments from the ATB database, which contains over 890,000 pre-parameterized molecules, using a novel hierarchical matching procedure. Atoms are considered within the context of an extended local environment (buffer region) with the degree of similarity between an atom in the target molecule and that in the proposed match controlled by varying the size of the buffer region. Adjacent matching atoms are combined into progressively larger matched sub-structures. The user then selects the most appropriate match. OFraMP also allows users to manually alter interaction parameters and automates the submission of missing substructures to the ATB in order to generate parameters for atoms in environments not represented in the existing database. The utility of OFraMP is illustrated using the anti-cancer agent paclitaxel and a dendrimer used in organic semiconductor devices. Graphical abstract: OFraMP applied to paclitaxel (ATB ID 35922).[Figure not available: see fulltext.