Enhanced Conformational Sampling using Replica Exchange with Collective-Variable Tempering

The computational study of conformational transitions in RNA and proteins with atomistic molecular dynamics often requires suitable enhanced sampling techniques. We here introduce a novel method where concurrent metadynamics are integrated in a Hamiltonian replica-exchange scheme. The ladder of replicas is built with different strength of the bias potential exploiting the tunability of well-tempered metadynamics. Using this method, free-energy barriers of individual collective variables are significantly reduced compared with simple force-field scaling. The introduced methodology is flexible and allows adaptive bias potentials to be self-consistently constructed for a large number of simple collective variables, such as distances and dihedral angles. The method is tested on alanine dipeptide and applied to the difficult problem of conformational sampling in a tetranucleotide

Structural determinants of the SINE B2 element embedded in the long non-coding RNA activator of translation AS Uchl1

Pervasive transcription of mammalian genomes leads to a previously underestimated level of complexity in gene regulatory networks. Recently, we have identified a new functional class of natural and synthetic antisense long non-coding RNAs (lncRNA) that increases translation of partially overlapping sense mRNAs. These molecules were named SINEUPs, as they require an embedded inverted SINE B2 element for their UP-regulation of translation. Mouse AS Uchl1 is the representative member of natural SINEUPs. It was originally discovered for its role in increasing translation of Uchl1 mRNA, a gene associated with neurodegenerative diseases. Here we present the secondary structure of the SINE B2 Transposable Element (TE) embedded in AS Uchl1. We find that specific structural regions, containing a short hairpin, are required for the ability of AS Uchl1 RNA to increase translation of its target mRNA. We also provide a high-resolution structure of the relevant hairpin, based on NMR observables. Our results highlight the importance of structural determinants in embedded TEs for their activity as functional domains in lncRNAs

Accurate multiple time step in biased molecular simulations

Many recently introduced enhanced sampling techniques are based on biasing coarse descriptors (collective variables) of a molecular system on the fly. Sometimes the calculation of such collective variables is expensive and becomes a bottleneck in molecular dynamics simulations. An algorithm to treat smooth biasing forces within a multiple time step framework is here discussed. The implementation is simple and allows a speed up when expensive collective variables are employed. The gain can be substantial when using massively parallel or GPU-based molecular dynamics software. Moreover, a theoretical framework to assess the sampling accuracy is introduced, which can be used to assess the choice of the integration time step in both single and multiple time step biased simulations