Heat-activated growth of metastable and length-defined DNA fibers expands traditional polymer assembly.

Biopolymers such as nucleic acids and proteins exhibit dynamic backbone folding, wherein site-specific intramolecular interactions determine overall structure. Proteins then hierarchically assemble into supramolecular polymers such as microtubules, that are robust yet dynamic, constantly growing or shortening to adjust to cellular needs. The combination of dynamic, energy-driven folding and growth with structural stiffness and length control is difficult to achieve in synthetic polymer self-assembly. Here we show that highly charged, monodisperse DNA-oligomers assemble via seeded growth into length-controlled supramolecular fibers during heating; when the temperature is lowered, these metastable fibers slowly disassemble. Furthermore, the specific molecular structures of oligomers that promote fiber formation contradict the typical theory of block copolymer self-assembly. Efficient curling and packing of the oligomers - or 'curlamers' - determine morphology, rather than hydrophobic to hydrophilic ratio. Addition of a small molecule stabilises the DNA fibers, enabling temporal control of polymer lifetime and underscoring their potential use in nucleic-acid delivery, stimuli-responsive biomaterials, and soft robotics.

Thermodynamic Analysis of Silk Fibroin-Graphite Hybrid Materials and Their Morphology.

Silk fibroin (SF) is a ß-sheet-rich protein that is responsible for the remarkable tensile strength of silk. In addition to its mechanical properties, SF is biocompatible and biodegradable, making it an attractive candidate for use in biotic/abiotic hybrid materials. A pairing of particular interest is the use of SF with graphene-based nanomaterials (GBNs). The properties of this interface drive the formation of well-ordered nanostructures and can improve the electronic properties of the resulting hybrid. It was previously demonstrated that SF can form lamellar nanostructures in the presence of graphite; however, the equilibrium morphology and associated driving interactions are not fully understood. In this study, we characterize these interactions between SF and SF lamellar with graphite using molecular dynamics (MD) simulations and umbrella sampling (US). We find that SF lamellar nanostructures have strong orientational and spatial preferences on graphite that are driven by the hydrophobic effect, destabilizing solvent-protein interactions and stabilizing protein-protein and protein-graphite interactions. Finally, we show how careful consideration of these underlying interactions can be applied to rationally modify the nanostructure morphology.

Machine Learning in a Molecular Modeling Course for Chemistry, Biochemistry, and Biophysics Students.

Recent advances in computer hardware and software, particularly the availability of machine learning libraries, allow the introduction of data-based topics such as machine learning into the Biophysical curriculum for undergraduate and/or graduate levels. However, there are many practical challenges of teaching machine learning to advanced-level students in the biophysics majors, who often do not have a rich computational background. Aiming to overcome such challenges, we present an educational study, including the design of course topics, pedagogical tools, and assessments of student learning, to develop the new methodology to incorporate the basis of machine learning in an existing Biophysical elective course, and engage students in exercises to solve problems in an interdisciplinary field. In general, we observed that students had ample curiosity to learn and apply machine learning algorithms to predict molecular properties. Notably, feedback from the students suggests that care must be taken to ensure student preparations for understanding the data-driven concepts and fundamental coding aspects required for using machine learning algorithms. This work establishes a framework for future teaching approaches that unite machine learning and any existing course in the biophysical curriculum, while also pinpointing the critical challenges that educators and students will likely face.

Aggression and discrimination among closely versus distantly related species of Drosophila.

Fighting between different species is widespread in the animal kingdom, yet this phenomenon has been relatively understudied in the field of aggression research. Particularly lacking are studies that test the effect of genetic distance, or relatedness, on aggressive behaviour between species. Here we characterized male-male aggression within and between species of fruit flies across the Drosophila phylogeny. We show that male Drosophila discriminate between conspecifics and heterospecifics and show a bias for the target of aggression that depends on the genetic relatedness of opponent males. Specifically, males of closely related species treated conspecifics and heterospecifics equally, whereas males of distantly related species were overwhelmingly aggressive towards conspecifics. To our knowledge, this is the first study to quantify aggression between Drosophila species and to establish a behavioural bias for aggression against conspecifics versus heterospecifics. Our results suggest that future study of heterospecific aggression behaviour in Drosophila is warranted to investigate the degree to which these trends in aggression among species extend to broader behavioural, ecological and evolutionary contexts.