100 research outputs found
Effects and limitations of a nucleobase-driven backmapping procedure for nucleic acids using steered molecular dynamics
No full textCoarse-grained models can be of great help to address the problem of structure prediction in nucleic acids. On one hand they can make the prediction more efficient, while on the other hand they can also help to identify the essential degrees of freedom and interactions for the description of a number of structures. With the aim to provide an all-atom representation in an explicit solvent to the predictions of our SPlit and conQueR (SPQR) coarse-grained model of RNA, we recently introduced a backmapping procedure which enforces the predicted structure into an atomistic one by means of steered molecular dynamics. These simulations minimize the ERMSD, a particular metric which deals exclusively with the relative arrangement of nucleobases, between the atomistic representation and the target structure. In this paper, we explore the effects of this approach on the resulting interaction networks and backbone conformations by applying it on a set of fragments using as a target their native structure. We find that the geometry of the target structures can be reliably recovered, with limitations in the regions with unpaired bases such as bulges. In addition, we observe that the folding pathway can also change depending on the parameters used in the definition of the ERMSD and the use of other metrics such as the RMSD
Automated Force-Field Parametrization Guided by Multisystem Ensemble Averages
No full textRNA structure and dynamics play a fundamental role in many cellular processes such as gene expression inhibition, splicing and catalysis. Molecular dynamics is a computational tool that can be used to investigate RNA structure and dynamics at atomistic resolution. However, its capability to predict and explain experimental data is limited by the accuracy of the employed potential energy functions, also known as force fields. Recent works have shown that state-of-the-art force fields could predict unphysical RNA conformations that are not in agreement with experiments. The emerging strategy to overcome these limitations is to complement molecular dynamics with experimental data included as restraints. In a recent work, we suggested a maximum-entropy based method to enforce solution experiments in molecular dynamics simulations. Importantly, this approach reduces the risk of overfitting by simultaneously adapting the force-field corrections to multiple systems. We here push this idea further and develop a general scheme to fit arbitrary force-field parameters given a set of ensemble averages. Such quantities can range from NMR data, such as 3J couplings or NOE, to native state populations. The key feature of our method is the possibility to concurrently combine ensemble averages from multiple systems into a unique error function to be minimized, drastically enhancing corrections’ transferability. The method is applied to the difficult case of GAGA and UUCG tetraloops for which we are able to maximize their native state population by refining torsional potentials alone
Conformational Ensembles of Noncoding Elements in the SARS-CoV-2 Genome from Molecular Dynamics Simulations
No full textThe 5′ untranslated region (UTR) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome is a conserved, functional and structured genomic region consisting of several RNA stem-loop elements. While the secondary structure of such elements has been determined experimentally, their three-dimensional structures are not known yet. Here, we predict structure and dynamics of five RNA stem loops in the 5′-UTR of SARS-CoV-2 by extensive atomistic molecular dynamics simulations, more than 0.5 ms of aggregate simulation time, in combination with enhanced sampling techniques. We compare simulations with available experimental data, describe the resulting conformational ensembles, and identify the presence of specific structural rearrangements in apical and internal loops that may be functionally relevant. Our atomic-detailed structural predictions reveal a rich dynamics in these RNA molecules, could help the experimental characterization of these systems, and provide putative three-dimensional models for structure-based drug design studies. </p
A nucleobase-centered coarse-grained representation for structure prediction of RNA motifs
No full textWe introduce the SPlit-and-conQueR (SPQR) model, a coarse-grained (CG) representation of RNA designed for structure prediction and refinement. In our approach, the representation of a nucleotide consists of a point particle for the phosphate group and an anisotropic particle for the nucleoside. The interactions are, in principle, knowledge-based potentials inspired by the SCORE function, a base-centered scoring function. However, a special treatment is given to base-pairing interactions and certain geometrical conformations which are lost in a raw knowledge-based model. This results in a representation able to describe planar canonical and non-canonical base pairs and base-phosphate interactions and to distinguish sugar puckers and glycosidic torsion conformations. The model is applied to the folding of several structures, including duplexes with internal loops of non-canonical base pairs, tetraloops, junctions and a pseudoknot. For the majority of these systems, experimental structures are correctly predicted at the level of individual contacts. We also propose a method for efficiently reintroducing atomistic detail from the CG representation
RNA Folding Pathways in Stop Motion
No full textWe introduce a method for predicting RNA folding pathways, with an application to the most important RNA tetraloops. The method is based on the idea that ensembles of three-dimensional fragments extracted from high-resolution crystal structures are heterogeneous enough to describe metastable as well as intermediate states. These ensembles are first validated by performing a quantitative comparison against available solution NMR data of a set of RNA tetranucleotides. Notably, the agreement is better with respect to the one obtained by comparing NMR with extensive all-atom molecular dynamics simulations. We then propose a procedure based on diffusion maps and Markov models that makes it possible to obtain reaction pathways and their relative probabilities from fragment ensembles. This approach is applied to study the helix-to-loop folding pathway of all the tetraloops from the GNRA and UNCG families. The results give detailed insights into the folding mechanism that are compatible with available experimental data and clarify the role of intermediate states observed in previous simulation studies. The method is computationally inexpensive and can be used to study arbitrary conformational transitions
RNA Conformational Ensembles: Narrowing the GAP between Experiments and Simulations with Metadynamics
No full textThe computational study of conformational transitions in nucleic acids still faces many challenges. For example, in the case of single stranded RNA tetranucleotides, agreement between simulations and experiments is not satisfactory due to inaccuracies in the force fields commonly used in molecular dynamics. Improvement of force fields is however hindered by the difficulties of decoupling those errors from the statistical errors caused by insufficient sampling. We here tackle both problems simultaneously by introducing a novel enhancing sampling method and using experimental data to improve RNA force fields.
In this novel method, concurrent well-tempered 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 so as to scale barriers on individual collective variables [1]. At the same time, the metadynamics algorithm is modified so as to allow enforcing a target distribution of backbone and sugar-base torsion angles taken from experimental structures, using a procedure related to two recently introduced techniques. Replica-exchange simulations of several RNA tetranucleotides with experimental corrections show significantly better agreement with NMR experimental data and suggest a systematic procedure for force field refinement.
[1] Gil-Ley, A.; Bussi, G. J. Chem. Theory Comput. 2015, 11, 1077-1085
Empirical corrections to the Amber RNA force field with Target Metadynamics
No full textThe computational study of conformational transitions in nucleic acids still faces many challenges. For example, in the case of single stranded RNA tetranucleotides, agreement between simulations and experiments is not satisfactory due to inaccuracies in the force fields commonly used in molecular dynamics simulations. We here use experimental data collected from high-resolution X-ray structures to attempt an improvement of the latest version of the AMBER force field. A modified metadynamics algorithm is used to calculate correcting potentials designed to enforce experimental distributions of backbone torsion angles. Replica-exchange simulations of tetranucleotides including these correcting potentials show significantly better agreement with independent solution experiments for the oligonucleotides containing pyrimidine bases. Although the proposed corrections do not seem to be portable to generic RNA systems, the simulations revealed the importance of the α and ζ backbone angles for the modulation of the RNA conformational ensemble. The correction protocol presented here suggests a systematic procedure for force-field refinement
A Nucleobase-Centric Coarse-Grained Model for Structure Prediction of RNA Fragments
No full textThe structural characterization of RNA usually poses additional challenges when compared to other biomolecular systems. For example, there is a relatively scarce amount of structural data available, which demands the development of three-dimensional structure prediction tools. In addition, full-resolution simulations can be a hard task not only because of the complexity of the interactions involved, but also due to the limitations of the current force fields. At this point, coarse-grained simulations are a good candidate to fill the gaps in this growing research field. The use of these techniques has rapidly increased in the past decades in the study of biological systems on which the experiments require an additional interpretation or where an atomistic computational approach results difficult or unfeasible. Nevertheless, the development of coarse-grained models involves the understanding of the main structural features of the original system, which can represent a challenge by itself.
In this work, we present a knowledge-base coarse-grained model for RNA structure prediction, representing each nucleotide by a single anisotropic particle. The mapping and the main interactions are designed to reproduce the geometrical distribution of the closest pairs of nucleotides, extracted from a set of ribosomal structures. The model is inspired in the ESCORE function [1], a knowledge-based scoring function that has been shown to perform better compared to fully atomistic techniques in identifying native-like structures from a set of decoys. Its minimalistic nature and successful application on a broad range of structures straightforwardly suggest a representation for a coarse-grained approach. We show the preliminary results of our simulations and discuss the role of pair interactions in the prediction of RNA structures.
[1] S. Bottaro, F. Di Palma and G. Bussi, “The Role of Nucleobase Interactions in RNA Structure and Dynamics”, Nucleic Acids Res., accepted
RNA Conformational Fluctuations from Elastic Network Models: A Comparison with Molecular Dynamics and Shape Experiments
No full textThe role of ribonucleic acid (RNA) in molecular biology is shifting from a mere messenger between DNA and proteins to an important player in many cellular activities. The central role of RNA molecules calls for a precise characterization of their structural and dynamical properties. Nowadays, experiments can be efficiently complemented by computational approaches. Within this framework, elastic network models (ENMs) are valuable and efficient tools for characterizing the collective internal dynamics of biomolecules starting from the sole knowledge of their structures. The increasing evidence that the biological functionality of RNAs is often linked to their innate internal motions, poses the question of whether ENM approaches can be successfully extended to these biomolecules. This issue, which is still largely unexplored, is tackled here by considering various families of elastic networks for a representative set of RNAs. The large-scale motions predicted by the alternative ENMs are stringently validated by comparison against extensive molecular dynamics (MD) simulations and SHAPE experimental data. We propose a specific combination of three ENM centroids (sugar-base-phosphate) as an optimal compromise capable of reproducing simulations and experiments. Copyright © 2016 Biophysical Society. Published by Elsevier Inc. All rights reserved
Free Energy Landscape of GAGA and UUCG RNA Tetraloops
No full textWe report the folding thermodynamics of ccUUCGgg and ccGAGAgg RNA tetraloops using atomistic molecular dynamics simulations. We obtain a previously unreported estimation of the folding free energy using parallel tempering in combination with well-tempered metadynamics. A key ingredient is the use of a recently developed metric distance, eRMSD, as a biased collective variable. We find that the native fold of both tetraloops is not the global free energy minimum using the AmberχOL3 force field. The estimated folding free energies are 30.2 ± 0.5 kJ/mol for UUCG and 7.5 ± 0.6 kJ/mol for GAGA, in striking disagreement with experimental data. We evaluate the viability of all possible one-dimensional backbone force field corrections. We find that disfavoring the gauche+ region of α and ζ angles consistently improves the existing force field. The level of accuracy achieved with these corrections, however, cannot be considered sufficient by judging on the basis of available thermodynamic data and solution experiments. © 2016 American Chemical Society
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