Size-dependent melting point depression of nickel nanoparticles

We investigate the phase-transition behaviour of nickel nanoparticles (3–6 nm) via dynamic TEM. The nanoparticles were synthesized within a reverse microemulsion and then monitored via dynamic TEM simultaneously while undergoing controlled heating. The size-dependent melting point depression experimentally observed is compared with, and is in good agreement with existing thermodynamic and molecular dynamic predictions

Beyond tripeptides - two-step active machine learning for very large datasets

Self-assembling peptide nanostructures have been shown to be of great importance in nature and have presented many promising applications, for example, in medicine as drug-delivery vehicles, biosensors, and antivirals. Being very promising candidates for the growing field of bottom-up manufacture of functional nanomaterials, previous work (Frederix, et al. 2011 and 2015) has screened all possible amino acid combinations for di- and tripeptides in search of such materials. However, the enormous complexity and variety of linear combinations of the 20 amino acids make exhaustive simulation of all combinations of tetrapeptides and above infeasible. Therefore, we have developed an active machine-learning method (also known as "iterative learning" and "evolutionary search method") which leverages a lower-resolution data set encompassing the whole search space and a just-in-time high-resolution data set which further analyzes those target peptides selected by the lower-resolution model. This model uses newly generated data upon each iteration to improve both lower- and higher-resolution models in the search for ideal candidates. Curation of the lower-resolution data set is explored as a method to control the selected candidates, based on criteria such as log P. A major aim of this method is to produce the best results in the least computationally demanding way. This model has been developed to be broadly applicable to other search spaces with minor changes to the algorithm, allowing its use in other areas of research

Commercialisation and commodification of breastfeeding: video diaries by first-time mothers.

BACKGROUND: Many of aspects of our lives became increasingly commercialised in post-modern society. Although breastfeeding is perhaps a late comer to this process in recent years, it too has seen significant commercialisation facilitated by social media and our obsession with celebrity culture. This paper explores how the commercialisation and commodification of breastfeeding impacts mothers' experiences of breastfeeding. METHODS: In a qualitative study, five mothers in the United Kingdom recorded their real-time breastfeeding experiences in video diaries. Using a multi-modal method of analysis, incorporating both visual and audio data, a thematic approach was applied. FINDINGS: Women preparing for breastfeeding are exposed to increasing commercialisation. When things do not go to plan, women are even more exposed to commercial solutions. The impact of online marketing strategies fuelled their need for paraphernalia so that their dependence on such items became important aspects of their parenting and breastfeeding experiences. CONCLUSIONS: The audio-visual data demonstrated the extent to which "essential" paraphernalia was used, offering new insights into how advertising influenced mothers' need for specialist equipment and services. Observing mothers in their video diaries, provided valuable insights into their parenting styles and how this affected their breastfeeding experience

Short peptide self-assembly in the Martini coarse grain forcefield family

Pivotal to the success of any computational experiment is the ability to make reliable predictions about the system under study and the time required to yield these results. Biomolecular interactions is one area of research that sits in every camp of resolution vs time required, from the quantum mechanical level through to in vivo studies. At an approximate mid-point there is coarse-grained molecular dynamics, for which the Martini forcefields have become the most widely used, fast enough to simulate the entire membrane of a mitochondrion though lacking atom-specific precision. While many forcefields have been parameterized to account for a specific system under study, the Martini forcefield has aimed at casting a wider net with more generalized bead types that have demonstrated suitability for broad use and reuse in applications from protein-graphene oxide co-assembly to polysaccharides interactions. In this account, the progressive (Martini versions 1 through 3) and peripheral (Sour Martini, constant pH, Martini Straight, Dry Martini, etc) developmental trajectory of the Martini forcefield will be analyzed in terms of self-assembling systems with a focus on short (2-3 amino acids) peptide self-assembly in aqueous environments. Particularly, this will focus on the effects of the Martini solvent model and compare how changes in bead definitions and mapping have effects on different systems. Considerable effort in the development of Martini has been taken to reduce the ”stickiness” of amino acids to better simulate proteins in bilayers. We have included in this account a short study of dipeptide self-assembly in water, using all mainstream Martini forcefields, to examine their ability to reproduce this behavior. The three most recently released versions of Martini and variations in their solvents are used to simulate in triplicate all 400 dipeptides of the 20 gene-encoded amino acids. The ability of the forcefields to model the self-assembly of the dipeptides in aqueoues environments is determined by measurement of the aggregation propensity and additional descriptors are used to gain further insight into the dipeptide aggregates

An active machine learning discovery platform for membrane-disrupting and pore-forming peptides

Membrane-disrupting and pore-forming peptides (PFPs) play a substantial role in bionanotechnology and can determine the life and death of cells. The control of chemical and ion transport through cell membranes is essential to maintaining concentration gradients. Likewise, the delivery of drugs and intracellular proteins aided by pore-forming agents are of interest in treating malfunctioning cells. Known PFPs tend to be up to 50 residues in length, which is commensurate with the thickness of a lipid bilayer. Accordingly, few short PFPs are known. Here we show that the discovery of PFPs can be accelerated via an active machine learning approach. The approach identified 71 potential PFPs from the 25.6 billion octapeptide sequence space; 13 sequences were tested experimentally, and all were found to have the predicted membrane-disrupting ability, with 1 forming highly stable pores. Experimental verification of the predicted pore-forming ability demonstrated that a range of short peptides can form pores in membranes, while the positioning and characteristics of residues that favour pore-forming behaviour were identified. This approach identified more ultrashort (8-residues, unmodified, non-cyclic) PFPs than previously known. We anticipate our findings and methodology will be useful in discovering new pore-forming and membrane-disrupting peptides for range of applications from nanoreactors to therapeutics

Integrating computation, experiment, and machine learning in the design of peptide-based supramolecular materials and systems

Interest in peptide-based supramolecular materials has grown extensively since the 1980s and the application of computational methods has paralleled this. These methods contribute to the understanding of experimental observations based on interactions and inform the design of new supramolecular systems. They are also used to virtually screenand navigate these very large design spaces. Increasingly, the use of artificial intelligence is employed to screen far more candidates than traditional methods. Based on a brief history of computational and experimentally integrated investigations of peptide structures, we explore recent impactful examples of computationally driven investigation into peptide self-assembly, focusing on recent advances in methodology development. It is clear that the integration between experiment and computation to understand and design new systems is becoming near seamless in this growing field

The therapeutic role of video diaries: A qualitative study involving breastfeeding mothers.

BACKGROUND: Despite breastfeeding providing maximum health benefits to mother and baby, many women in the United Kingdom do not breastfeed, or do so briefly. PURPOSE: Using tenets of ethnography, this study aimed to explore the everyday experiences of first time breastfeeding mothers in the early weeks following birth. METHODS: Using a camcorder, five mothers in the United Kingdom captured their real-time experiences in a video diary, until they perceived their infant feeding was established. Using a multidimensional approach to analysis, we examined how five mothers interacted with the camcorder as they shared their emotions, feelings, thoughts and actions in real-time. FINDINGS: Mothers recorded 294 video clips, total recording time exceeded 43h. This paper focuses on one theme, the therapeutic role of the camcorder in qualitative research. Four subthemes are discussed highlighting the therapeutic impact of talking to the camcorder: personifying the camcorder; using the camcorder as a confidante; a sounding board; and a mirror and motivator. CONCLUSION: Frequent opportunities to relieve tension by talking to "someone" without interruption, judgement or advice can be therapeutic. Further research needs to explore how the video diary method can be integrated into standard postnatal care to provide benefits for a wider population

Constant pH coarse-grained molecular dynamics with stochastic charge neutralization

pH dependence abounds in biochemical systems; however, many simulation methods used to investigate these systems do not consider this property. Using a modified version of the hybrid non-equilibrium molecular dynamics (MD)/Monte Carlo algorithm, we include a stochastic charge neutralization method, which is particularly suited to the MARTINI force field and enables artifact-free Ewald summation methods in electrostatic calculations. We demonstrate the efficacy of this method by reproducing pH-dependent self-assembly and self-organization behavior previously reported in experimental literature. In addition, we have carried out experimental oleic acid titrations where we report the results in a more relevant way for the comparison with computational methods than has previously been done

Martinoid : the peptoid martini force field

Many exciting innovations have been made in the development of assembling peptoid materials. Typically, these have utilised large oligomeric sequences, though elsewhere the study of peptide self-assembly has yielded numerous examples of assemblers below 6–8 residues in length, evidencing that minimal peptoid assemblers are not only feasible but expected. A productive means of discovering such materials is through the application of in silico screening methods, which often benefit from the use of coarse-grained molecular dynamics (CG-MD) simulations. At the current level of development, CG models for peptoids are insufficient and we have been motivated to develop a Martini forcefield compatible peptoid model. A dual bottom-up and top-down parameterisation approach has been adopted, in keeping with the Martini parameterisation methodology, targeting the reproduction of atomistic MD dynamics and trends in experimentally obtained log D7.4 partition coefficients, respectively. This work has yielded valuable insights into the practicalities of parameterising peptoid monomers. Additionally, we demonstrate that our model can reproduce the experimental observations of two very different peptoid assembly systems, namely peptoid nanosheets and minimal tripeptoid assembly. Further we can simulate the peptoid helix secondary structure relevant for antimicrobial sequences. To be of maximum usefulness to the peptoid research community, we have developed freely available code to generate all requisite simulation files for the application of this model with Gromacs MD software

Synthesis of (+)-(R)-Tiruchanduramine

The absolute stereochemistry of the marine alkaloid (+)-(R)-tiruchanduramine was established via a convergent total synthesis in six steps and 15.5% overall yield from Fmoc-D-Dab(Boc)-OH