PyThinFilm: Automated Molecular Dynamics Simulation Protocols for the Generation of Thin Film Morphologies.

The performance of organic optoelectronic devices, such as organic light-emitting diodes (OLEDs) and organic solar cells (OSCs), is intrinsically related to the molecular-scale morphology of the thin films from which they are composed. However, the experimental characterization of morphology at the molecular level is challenging due to the often amorphous or at best semicrystalline nature of these films. Classical molecular modeling techniques, such as molecular dynamics (MD) simulation, are increasingly used to understand the relationship between morphology and the properties of thin-film devices. PyThinFilm (github.com/ATB-UQ/PyThinFilm) is an open-source Python package which allows fully automated MD simulations of thin film growth to be performed using vacuum and/or solution deposition processes. PyThinFilm utilizes the GROMACS simulation package in combination with interaction parameters from the Automated Topology Builder (atb.uq.edu.au). Here, PyThinFilm is described along with an overview of applications in which PyThinFilm has been used to study the thin films of organic semiconductor materials typically used in OLEDs and OSCs.

Parameterizing amylose chain-length distributions for biosynthesis-structure-property relations.

Amylose, one of the components of starch, is a glucose polymer consisting largely of long, linear chains with a few long-chain branch points. The chain-length (molecular weight) distribution (CLD) of the component chains of amylose can provide information on amylose biosynthesis-structure-property relations, as has been done previously by fitting amylopectin CLDs to a model with physically meaningful parameters. Due to the presence of long chains, the CLD of amylose can currently best be obtained by size-exclusion chromatography, a technique that suffers from band-broadening effects which alter the observed distribution. The features of the multiple regions present in amylose chain-length distributions are also difficult to resolve, an issue that combines with band broadening to compound the difficulty of analysis and subsequent parameterization of the structural characteristics of amylose. A new method is presented to fit these distributions with biologically meaningful parameters in a way that accounts for band broadening. This is achieved by assuming that band broadening takes the form of a simple Gaussian over a relatively small region and that chain stoppage is a random process independent of the length of the substrate chain over the same region; these assumptions are relatively weak and expected to be frequently applicable. The method provides inbuilt consistency tests for its applicability to a given data set and, in cases where it is applicable, allows for the first nonempirical parameterization of amylose biosynthesis-structure-property relations from CLDs by using parameters directly linked to the activities of the enzymes responsible for chain growth and chain stoppage. Graphical abstract Model calculation illustrating the method described and showing the division between the three characteristic regions of a typical amylose chain-length distribution.

Proteomic Investigation of the Binding Agent between Liver Glycogen ß Particles.

Glycogen is a highly branched glucose polymer which plays an important role in glucose storage and the maintenance of blood sugar homeostasis. The dimeric protein glycogenin can self-glucosylate to act as a primer for glycogen synthesis, eventually resulting in small (∼20 nm diameter) glycogen ß particles with a dimer of glycogenin at their core. In the liver, glycogen is also found in the form of α particles: large bound composites of many ß particles. Here, we provide evidence using qualitative and quantitative proteomics and size-exclusion chromatography from healthy rat, mouse, and human liver glycogen that glycogenin is the binding agent linking ß particles together into α particles.

Exploring glycogen biosynthesis through Monte Carlo simulation.

Glycogen, a complex branched polymer of glucose (average chain length ~10 monomer units), is the blood-sugar reservoir in humans and other animals. Certain aspects of its molecular structure relevant to its biological functions are currently unamenable to experimental exploration. Knowledge of these is needed to develop future models for quantitative data-fitting to obtain mechanistic understanding of the biosynthetic processes that give rise to glycogen structure. Monte Carlo simulations of the biosynthesis of this structure with realistic macromolecular parameters reveal how chain growth and stoppage (the latter assumed to be through both the action of glycogen branching enzyme and other degradative enzymes, and by hindrance) control structural features. The simulated chain-length, pair-distance and radial density distributions agree semi-quantitatively with the limited available data. The simulations indicate that a steady state in molecular structure and size is rapidly obtained, that molecular density reaches a maximum near the center of the particle (not at the periphery, as is the case with dendrimers), and that particle size is controlled by both enzyme activity and hindrance. This knowledge will aid in the understanding of diabetes (loss of blood-sugar control), which has been found to involve subtle differences in glycogen molecular structure.

Diurnal changes of glycogen molecular structure in healthy and diabetic mice.

Glycogen is a complex branched glucose polymer functioning as a blood-sugar reservoir in animals. Liver glycogen ß particles can bind together to form α particles, which have a slower enzymatic degradation to glucose. The linkage between ß particles in α particles in diabetic liver breaks (is fragile) in dimethyl sulfoxide (DMSO), a H-bond disruptor, consistent with blood-sugar homeostasis loss in diabetes. We examined diurnal changes in the molecular structure of healthy and diabetic mouse-liver glycogen. Healthy mouse glycogen was fragile to DMSO during glycogen synthesis but not degradation; diabetic glycogen was always fragile. Two alternative mechanisms for this are suggested: healthy glycogen is fragile when formed and becomes stable during subsequent degradation, a process damaged in diabetes; alternatively, there are two types of glycogen: one compact but fragile and the other loose but non-fragile. This suggests potential types of diabetes drug targets through modifying the activities of glycogen synthesis enzymes.

On the Role of Catabolic Enzymes in Biosynthetic Models of Glycogen Molecular Weight Distributions.

Glycogen and starch are complex branched polymers of glucose that serve as units of glucose storage in animals and plants, respectively. Changes in the structure of these molecules have been linked to changes in their respective functional properties. Enzymatic models of starch synthesis have provided valuable insights into the biosynthetic origins of starch structure and functional properties but have not successfully been applied to glycogen despite the structural similarities between the two polymers. Modifications to biosynthetic models of starch structure were tested for applicability to glycogen. Mathematical evidence is provided showing the necessity (which has hitherto been in doubt) of considering the effects of catabolic (degradative) enzymes in biosynthesis-based approaches that seek to accurately describe the molecular weight distributions of individual chains of glycogen formed in vivo through glycogenesis. This finding also provides future direction for inferring the dependence of enzyme activities on substrate chain length from in vivo data.