Structural Dynamics of Lateral and Diagonal Loops of Human Telomeric G-Quadruplexes in Extended MD Simulations

The NMR solution structures of human telomeric (Htel) G-quadruplexes (GQs) are characterized by the presence of two lateral loops complemented by either diagonal or propeller loops. Bases of a given loop can establish interactions within the loop as well as with other loops and the flanking bases. This can lead to a formation of base alignments above and below the GQ stems. These base alignments are known to affect the loop structures and relative stabilities of different Htel GQ folds. We have carried out a total of 217 μs of classical (unbiased) molecular dynamics (MD) simulations starting from the available solution structures of Htel GQs to characterize structural dynamics of the lateral and diagonal loops, using several recent AMBER DNA force-field variants. As the loops are involved in diverse stacking and H-bonding interactions, their dynamics is slow, and extended sampling is required to capture different conformations. Nevertheless, although the simulations are far from being quantitatively converged, the data suggest that multiple 10 μs-scale simulations can provide a quite good assessment of the loop conformational space as described by the force field. The simulations indicate that the lateral loops may sample multiple coexisting conformations, which should be considered when comparing simulations with the NMR models as the latter include ensemble averaging. The adenine–thymine Watson–Crick arrangement was the most stable base pairing in the simulations. Adenine–adenine and thymine–thymine base pairs were also sampled but were less stable. The data suggest that the description of lateral and diagonal GQ loops in contemporary MD simulations is considerably more realistic than the description of propeller loops, though definitely not flawless

Can We Execute Stable Microsecond-Scale Atomistic Simulations of Protein–RNA Complexes?

We report over 30 μs of unrestrained molecular dynamics simulations of six protein–RNA complexes in explicit solvent. We utilize the AMBER ff99bsc0χ_OL3 RNA force field combined with the ff99SB protein force field and its more recent ff12SB version with reparametrized side-chain dihedrals. The simulations show variable behavior, ranging from systems that are essentially stable to systems with progressive deviations from the experimental structure, which we could not stabilize anywhere close to the starting experimental structure. For some systems, microsecond-scale simulations are necessary to achieve stabilization after initial sizable structural perturbations. The results show that simulations of protein–RNA complexes are challenging and every system should be treated individually. The simulations are affected by numerous factors, including properties of the starting structures (the initially high force field potential energy, resolution limits, conformational averaging, crystal packing, etc.), force field imbalances, and real flexibility of the studied systems. These factors, and thus the simulation behavior, differ from system to system. The structural stability of simulated systems does not correlate with the size of buried interaction surface or experimentally determined binding affinities but reflects the type of protein–RNA recognition. Protein–RNA interfaces involving shape-specific recognition of RNA are more stable than those relying on sequence-specific RNA recognition. The differences between the protein force fields are considerably smaller than the uncertainties caused by sampling and starting structures. The ff12SB improves description of the tyrosine side-chain group, which eliminates some problems associated with tyrosine dynamics

Comparison of onshore and offshore student learning experience in an economic unit of study

University students often view economics as one of the most challenging subjects. In this paper, we explore whether the same attitude is shared by students at an offshore campus. The analysis is based on a survey of onshore and offshore students in an Australian university. The focus of the paper is on identifying similarities/differences in student attitudes towards economics, their degree of motivation, satisfaction with the content and the delivery of lectures and tutorials, major challenges, and student profile characteristics. A non-parametric test indicates significant differences between the two cohorts, in terms of their age, enrolment status, paid work commitments, prior study of economics, their motivation and approach to studying the subject, and satisfaction with both the content and the delivery of the lectures/tutorials. The findings in this study are helpful in developing strategies for enhancing student learning in culturally diverse cohorts

The normalized revealed comparative advantage index

F14, R12, C43,