Molecular dynamics (MD) simulations allow the investigation of the structural dynamics of biomolecular systems with unrivaled time and space resolution. However, in order to compensate for the inaccuracies of the utilized empirical force fields, it is becoming common to integrate MD simulations with experimental data obtained from ensemble measurements. We review here the approaches that can be used to combine MD and experiment under the guidance of the maximum entropy principle. We mostly focus on methods based on Lagrangian multipliers, either implemented as reweighting of existing simulations or through an on-the-fly optimization. We discuss how errors in the experimental data can be modeled and accounted for. Finally, we use simple model systems to illustrate the typical difficulties arising when applying these methods.

RNA molecules are highly dynamic systems characterized by a complex interplay between sequence, structure, dynamics, and function. Molecular simulations can potentially provide powerful insights into the nature of these relationships. The analysis of structures and molecular trajectories of nucleic acids can be nontrivial because it requires processing very high-dimensional data that are not easy to visualize and interpret. Here we introduce Barnaba, a Python library aimed at facilitating the analysis of nucleic acid structures and molecular simulations. The software consists of a variety of analysis tools that allow the user to (i) calculate distances between three-dimensional structures using different metrics, (ii) back-calculate experimental data from three-dimensional structures, (iii) perform cluster analysis and dimensionality reductions, (iv) search three-dimensional motifs in PDB structures and trajectories, and (v) construct elastic network models for nucleic acids and nucleic acids–protein complexes. In addition, Barnaba makes it possible to calculate torsion angles, pucker conformations, and to detect base-pairing/base-stacking interactions. Barnaba produces graphics that conveniently visualize both extended secondary structure and dynamics for a set of molecular conformations. The software is available as a command-line tool as well as a library, and supports a variety of file formats such as PDB, dcd, and xtc files. Source code, documentation, and examples are freely available at https://github.com/srnas/barnaba under GNU GPLv3 license.

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.