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Facing the current challenges posed by human health diseases requires the understanding of cell machinery at a molecular level. The interplay between proteins and RNA is key for any physiological phenomenon, as well protein–RNA interactions. To understand these interactions, many experimental techniques have been developed, spanning a very wide range of spatial and temporal resolutions. In particular, the knowledge of tridimensional structures of protein–RNA complexes provides structural, mechanical, and dynamical pieces of information essential to understand their functions. To get insights into the dynamics of protein–RNA complexes, we carried out all-atom molecular dynamics simulations in explicit solvent on nine different protein–RNA complexes with different functions and interface size by taking into account the bound and unbound forms. First, we characterized structural changes upon binding and, for the RNA part, the change in the puckering. Second, we extensively analyzed the interfaces, their dynamics and structural properties, and the structural waters involved in the binding, as well as the contacts mediated by them. Based on our analysis, the interfaces rearranged during the simulation time showing alternative and stable residue–residue contacts with respect to the experimental structure.

We investigated the capability of internal normal modes to reproduce RNA flexibility and predict observed RNA conformational changes and, notably, those induced by the formation of RNA-protein and RNA-ligand complexes. Here, we extended our iNMA approach developed for proteins to study RNA molecules using a simplified representation of the RNA structure and its potential energy. Three data sets were also created to investigate different aspects. Despite all the approximations, our study shows that iNMA is a suitable method to take into account RNA flexibility and describe its conformational changes opening the route to its applicability in any integrative approach where these properties are crucial.

RNA molecules can easily adopt alternative structures in response to different environmental conditions. As a result, a molecule’s energy landscape is rough and can exhibit a multitude of deep basins. In the absence of a high-resolution structure, small-angle X-ray scattering data (SAXS) can narrow down the conformational space available to the molecule and be used in conjunction with physical modeling to obtain high-resolution putative structures to be further tested by experiments. Because of the low resolution of these data, it is natural to implement the integration of SAXS data into simulations using a coarse-grained representation of the molecule, allowing for much wider searches and faster evaluation of SAXS theoretical intensity curves than with atomistic models. We present here the theoretical framework and the implementation of a simulation approach based on our coarse-grained model HiRE-RNA combined with SAXS evaluations “on-the-fly” leading the simulation toward conformations agreeing with the scattering data, starting from partially folded structures as the ones that can easily be obtained from secondary structure prediction-based tools. We show on three benchmark systems how our approach can successfully achieve high-resolution structures with remarkable similarity with the native structure recovering not only the overall shape, as imposed by SAXS data, but also the details of initially missing base pairs.

We analyze the capacity of normal mode analysis in internal coordinates’ space to generate large-amplitude structural deformations that can describe the conformational changes occurring upon the binding of proteins to other species. We also analyze how many modes need to be studied to capture a given transition and whether a combination of two modes is better than using a single mode. The technique is tested on known unbound-to-bound structural transitions for a set of single- or multidomain proteins. The results suggest that this approach is a promising way to generate structures for protein docking or for more refined molecular dynamics simulations.

We analyze the capacity of normal modes to predict observed protein conformational changes, and, notably, those induced by the formation of protein–protein complexes. We show that normal modes calculated in internal coordinate space (ICS) provide better predictions. For a large test set, using the ICS approach describes the conformational changes more completely, and with fewer low-frequency modes than the equivalent Cartesian coordinate modes, despite the fact that the internal coordinate calculations were restricted to torsional angles. This can be attributed to the fact that the use of ICS extends the range over which movements along the corresponding eigenvectors remain close to the true conformational energy hypersurface. We also show that the PaLaCe coarse-grain protein model performs better than a simple elastic network model. We apply ICS normal-mode analysis to protein complexes and, by extending the approach of Sunada and Go̅, [Sunada, S.; Go̅, N. J. Comput. Chem.1995, 16, 328–336], we show that we can couple an accurate view of the Cartesian coordinate movements induced by ICS modes with the detection of the key residues responsible for the movements.