The rules governing sequence-specific DNA-protein recognition are under a longstanding debate regarding the prevalence of base versus shape readout mechanisms to explain sequence specificity and of the conformational selection versus induced fit binding paradigms to explain binding-related conformational changes in DNA. Using a combination of atomistic simulations on a subset of representative sequences and mesoscopic simulations at the protein-DNA interactome level, we demonstrate the prevalence of the shape readout model in determining sequence-specificity and of the

Although chemically close to DNA, RNA can adopt a wide range of structures from regular helices to complex globular conformations, showing a complexity similar to that of proteins. The determination of the structure of RNA molecules, crucial for functional understanding, is severely handicapped by their size and flexibility, which makes the systematic use of experimental approaches difficult. Simulation techniques are also suffering from very severe problems related to the accuracy of the methods and their ability to sample a large and complex conformational landscape. Here, we systematically review recent approaches created to reduce the shortcoming of the current generation of simulation methods-from highly accurate models able to deal with small systems to coarse-grained approaches that are less accurate but applicable to dealing with large models. WHAT HAVE WE LEARNED ABOUT RNA STRUCTURE FROM QM AND QM/MM METHODS?Physics teaches us that ab initio quantum mechanics (QM) can represent with high accuracy any biomolecular system, among them RNA. Unfortunately, because of their computational cost, the practical application of ab initio QM formalisms to large systems such as RNA is often impossible. In fact, even simpler QM methods such as those based on density functional theory (DFT) fail to treat systems larger than 10 2 atoms, several orders of magnitude less than the size required to study RNAs in solution. 1 Further simplifications of the basic QM formalism, such as those implicit in semiempirical (SE) methods, can extend the range of applicability of QM theory but at the expense of an expected loss of accuracy. 2 High-level QM and DFT calculations have had a central role in the development and validation of recent RNA force fields (FFs; see below). A recent example is the B97D3/AUG-CC-PVTZ study of the backbone and glycosidic torsions by Aytenfisu et al., 3 who highlighted systematic errors in current RNA FFs that might lead to incorrect molecular dynamics (MD) trajectories. Different conclusions were reached by the group led by Sponer, who again used DFT calculations as a reference, namely that the errors in current RNA FFs are related to imbalanced hydration and not to intrinsic errors in the classical gas phase Hamiltonian. 4 The same group recently studied 46 different backbone conformations of the UpU dinucleotide step (see Figure 1) by using a variety of QM methods from the state-of-the-art CCSD(T) to the latest-generation SE algorithms, 5 providing the community with an invaluable dataset for refinement of RNA FFs. Very recently our group used DFT/MM (density functional theory and molecular mechanics) calculations to fit some dihedrals directly for QM calculations in solution, opening a new approach to using DFT calculations in the refinement of RNA FF (see the next section). 6 The Bigger PictureRNAs are the ultimate frontier of structural biology. They are large and complex molecules that can adopt complex structures displaying a wide variety of functions from carriers of genetic information to regu...

Summary veriNA3d is an R package for the analysis of nucleic acids structural data, with an emphasis in complex RNA structures. In addition to single-structure analyses, veriNA3d also implements functions to handle whole datasets of mmCIF/PDB structures that could be retrieved from public/local repositories. Our package aims to fill a gap in the data mining of nucleic acids structures to produce flexible and high throughput analysis of structural databases. Availability and implementation http://mmb.irbbarcelona.org/gitlab/dgallego/veriNA3d. Supplementary information Supplementary data are available at Bioinformatics online.

The characterization of the conformational landscape of the RNA backbone is rather complex due to the ability of RNA to assume a big variety of conformations. These backbone conformations can be depicted by pseudo-torsional angles linking RNA backbone atoms, from which Ramachandran-like plots can be built. We explored here different definitions of these pseudo-torsional angles, finding that the most accurate ones are the traditional η (eta) and θ (theta) angles, which represent the relative position of RNA backbone atoms P and C4'. We explore the distribution of η-θ in known experimental structures, comparing the pseudo-torsional space generated with structures determined exclusively by one experimental technique. We found that the complete picture only appears when combining data from different sources. The maps provide a quite comprehensive representation of the RNA accessible space, which can be used in RNA-structural prediction. Finally, our results highlight that protein interactions leads to significant changes in the population of the η-θ space, pointing towards the role of induced-fit mechanisms in protein-RNA recognition.