Exploring RNA structure and dynamics through enhanced sampling simulations

https://doi.org/10.1016/j.sbi.2018.01.004Get rights and content

Highlights

  • Enhanced sampling methods can explore RNA structural dynamics and catalytic reactions.

  • Generic as well as RNA-specific methods are increasingly used.

  • Simulations should be carefully analyzed and display multiple transition events.

  • For small systems (up to short hairpin loops) ergodic sampling can be achieved.

  • Current force fields might predict incorrect structures as global free-energy minima.

RNA function is intimately related to its structural dynamics. Molecular dynamics simulations are useful for exploring biomolecular flexibility but are severely limited by the accessible timescale. Enhanced sampling methods allow this timescale to be effectively extended in order to probe biologically relevant conformational changes and chemical reactions. Here, we review the role of enhanced sampling techniques in the study of RNA systems. We discuss the challenges and promises associated with the application of these methods to force-field validation, exploration of conformational landscapes and ion/ligand-RNA interactions, as well as catalytic pathways. Important technical aspects of these methods, such as the choice of the biased collective variables and the analysis of multi-replica simulations, are examined in detail. Finally, a perspective on the role of these methods in the characterization of RNA dynamics is provided.

Introduction

Ribonucleic acids (RNA) play fundamental roles in the cell, ranging from catalysis [1] to control of gene expression [2]. RNA function is often linked to its three-dimensional structure, typically obtained using X-ray crystallography [3] or nuclear magnetic resonance (NMR) [4]. However, RNA molecules are not static and might exhibit a multitude of accessible functional structures in a narrow energetic range. Many examples have been reported, ranging from flexible RNA motifs [5] to excited states [6] and, in the extreme case, riboswitches [7]. In addition, RNA catalysis is initiated by a pre-organization of the active site, and transition states (TSs) need to be stabilized by neighboring groups [8]. The mentioned events might require timescales ranging from microseconds to seconds or hours to be observed in an experimental setup.

Molecular dynamics (MD) simulations, both using empirical force fields and quantum mechanics/molecular mechanics approaches (QM/MM), are in principle a powerful tool to access RNA flexibility. However, they are limited to timescales of a few microseconds (for empirical force fields) or a few hundreds of picoseconds (for QM/MM-MD approaches). In order to address the conformational transitions and chemical reactions mentioned above, they should be complemented with enhanced sampling methods. Even dedicated machines capable to perform millisecond-long classical MD need enhanced sampling methods in order to access biologically relevant timescales [9].

We here present a survey on the recent applications of enhanced sampling techniques to atomistic MD simulations of RNA systems. Many recent reviews discuss in detail enhanced sampling methods [10, 11] and MD simulations of RNA [12, 13, 14, 15, 16]. We opted for proceeding in an orthogonal direction, highlighting which enhanced sampling methods have been recently applied to RNA systems and, at the same time, underlying which aspects of RNA dynamics can benefit of enhanced sampling methods. In order to take a picture of the current state of the art for the application of these techniques to RNA systems, we deliberately limited the survey to the past two years. In addition, we only considered atomistic explicit solvent simulations where hydrogen atoms and water molecules are explicitly included.

A fundamental issue in MD simulations is the choice of an appropriate model to compute the interatomic forces. This is done using empirical force fields (see [14, 15, 16] and references therein) and/or QM methods. In the latter case, a compromise between accuracy and computational cost should be found, choosing between fast but approximate semi-empirical (SE) methods and more accurate but computationally demanding density functional theory (DFT) methods (see [12, 13] and references therein).

A central idea of all enhanced sampling methods is to alter the system's dynamics to characterize specific events that would otherwise require significantly longer simulation timescales. Generally speaking, this can be done in two ways (Figure 1): by changing the probability distribution of a limited number of selected degrees of freedom, so called collective variables (CVs), deemed to be important for the investigated conformational transition; and by acting on the total energy or, equivalently, on the temperature of the system. Prototypical methods for these two approaches are umbrella sampling (US) and temperature replica-exchange molecular dynamics (T-REMD), respectively (see [10, 11] and references therein). Methods based on CVs are extremely efficient when the chosen CVs identify correctly the kinetically relevant states of the system, including metastable and TSs. Methods based on tempering are more computationally demanding, but usually require less a priori information. CV-based and tempering methods can be combined and methods at the boundary between these two classes have been proposed as well. We note that the usage of replicas is not necessarily a distinctive trait of tempering methods. US can indeed be performed in a replica-exchange scheme, as it is discussed below. Conversely, temperature methods using a single simulation are used as well. Alchemical approaches such as the free-energy perturbation method, where transitions are enforced through a non-physical path involving changes in particle number and/or identity [10], can be considered as a special case of CV-based methods. Other approaches using unbiased simulations to analyze and reconstruct long-time kinetics, as well as non-dynamical methods where energies of individual structures are calculated and compared, are not considered here.

Section snippets

Applications of enhanced sampling methods to RNA systems

Table 1 reports an extensive list of publications in the last two years where enhanced sampling methods were applied to RNA systems. We arbitrarily classified them in groups according to the presented application, although some of them could be assigned to more than one group.

Discussion and perspectives

In this review we surveyed the enhanced sampling methods recently applied to study RNA structural dynamics. In the following, we summarize our recommendations and perspectives.

Conflict of interest

None declared.

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

  • •• of outstanding interest

Acknowledgements

Alejandro Gil-Ley, Sandro Bottaro, Carlo Camilloni, Angel E García, Alex MacKerell, and Alessandra Magistrato are acknowledged for reading the manuscript and providing useful suggestions. This work has been supported by the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement n. 306662, S-RNA-S.

References (69)

  • D.M. Lilley

    The structure and folding of kink turns in RNA

    Wiley Interdiscip Rev RNA

    (2012)
  • E.A. Dethoff et al.

    Visualizing transient low-populated structures of RNA

    Nature

    (2012)
  • G.M. Emilsson et al.

    Ribozyme speed limits

    RNA

    (2003)
  • A.C. Pan et al.

    Demonstrating an order-of-magnitude sampling enhancement in molecular dynamics simulations of complex protein systems

    J Chem Theory Comput

    (2016)
  • O. Valsson et al.

    Enhancing important fluctuations: rare events and metadynamics from a conceptual viewpoint

    Annu Rev Phys Chem

    (2016)
  • M. Huang et al.

    Nucleic acid reactivity: challenges for next-generation semiempirical quantum models

    J Comput Chem

    (2015)
  • J. Šponer et al.

    How to understand atomistic molecular dynamics simulations of RNA and protein–RNA complexes?

    Wiley Interdiscip Rev RNA

    (2017)
  • S. Vangaveti et al.

    Advances in RNA molecular dynamics: a simulator's guide to RNA force fields

    Wiley Interdiscip Rev RNA

    (2017)
  • L.G. Smith et al.

    Physics-based all-atom modeling of RNA energetics and structure

    Wiley Interdiscip Rev RNA

    (2017)
  • C. Bergonzo et al.

    Highly sampled tetranucleotide and tetraloop motifs enable evaluation of common RNA force fields

    RNA

    (2015)
  • C. Bergonzo et al.

    Improved force field parameters lead to a better description of RNA structure

    J Chem Theory Comput

    (2015)
  • S. Sakuraba et al.

    Predicting RNA duplex dimerization free-energy changes upon mutations using molecular dynamics simulations

    J Phys Chem Lett

    (2015)
  • S. Bottaro et al.

    RNA folding pathways in stop motion

    Nucleic Acids Res

    (2016)
  • A. Gil-Ley et al.

    Empirical corrections to the AMBER RNA force field with target metadynamics

    J Chem Theory Comput

    (2016)
  • P. Kührová et al.

    Computer folding of RNA tetraloops: identification of key force field deficiencies

    J Chem Theory Comput

    (2016)
  • S. Bottaro et al.

    Free energy landscape of GAGA and UUCG RNA tetraloops

    J Phys Chem Lett

    (2016)
  • A. Cesari et al.

    Combining simulations and solution experiments as a paradigm for RNA force field refinement

    J Chem Theory Comput

    (2016)
  • C. Yang et al.

    Predicting RNA structures via a simple van der Waals correction to an all-atom force field

    J Chem Theory Comput

    (2017)
  • A.H. Aytenfisu et al.

    Revised RNA dihedral parameters for the AMBER force field improve RNA molecular dynamics

    J Chem Theory Comput

    (2017)
  • A. Gil-Ley et al.

    Enhanced conformational sampling using replica exchange with collective-variable tempering

    J Chem Theory Comput

    (2015)
  • S. Haldar et al.

    Insights into stability and folding of GNRA and UNCG tetraloops revealed by microsecond molecular dynamics and well-tempered metadynamics

    J Chem Theory Comput

    (2015)
  • X. Xue et al.

    Folding of SAM-II riboswitch explored by replica-exchange molecular dynamics simulation

    J Theor Biol

    (2015)
  • H. Park et al.

    Crystallographic and computational analyses of AUUCU repeating RNA that causes Spinocerebellar Ataxia type 10 (SCA10)

    Biochemistry

    (2015)
  • C. Bergonzo et al.

    Stem-loop V of varkud satellite RNA exhibits characteristics of the Mg2+ bound structure in the presence of monovalent ions

    J Phys Chem B

    (2015)
  • Cited by (0)

    View full text