Exploring RNA structure and dynamics through enhanced sampling simulations
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)
- et al.
A decade of riboswitches
Cell
(2013) - et al.
Enhanced sampling techniques in molecular dynamics simulations of biological systems
Biochim Biophys Acta Gen Subj
(2015) - et al.
How to understand quantum chemical computations on DNA and RNA systems? A practical guide for non-specialists
Methods
(2013) - et al.
Free energy landscape and multiple folding pathways of an H-type RNA pseudoknot
PLOS ONE
(2015) - et al.
Unraveling Mg2+-RNA binding with atomistic molecular dynamics
RNA
(2017) - et al.
Characterization of Mg2+ distributions around RNA in solution
ACS Omega
(2016) How RNA acts as a nuclease: some mechanistic comparisons in the nucleolytic ribozymes
Biochem Soc Trans
(2017)- et al.
The rise of regulatory RNA
Nat Rev Genet
(2014) Twenty years of RNA crystallography
RNA
(2015)- et al.
Mapping the landscape of RNA dynamics with NMR spectroscopy
Acc Chem Res
(2011)