1,720,984 research outputs found
Integrating experimental data with molecular simulations to investigate RNA structural dynamics
Full text linkConformational dynamics is crucial for ribonucleic acid (RNA) function. Techniques such as nuclear magnetic resonance, cryo-electron microscopy, small- and wide-angle X-ray scattering, chemical probing, single-molecule Förster resonance energy transfer, or even thermal or mechanical denaturation experiments probe RNA dynamics at different time and space resolutions. Their combination with accurate atomistic molecular dynamics (MD) simulations paves the way for quantitative and detailed studies of RNA dynamics. First, experiments provide a quantitative validation tool for MD simulations. Second, available data can be used to refine simulated structural ensembles to match experiments. Finally, comparison with experiments allows for improving MD force fields that are transferable to new systems for which data is not available. Here we review the recent literature and provide our perspective on this field
Pressure control using stochastic cell rescaling
Full text linkMolecular dynamics simulations require barostats to be performed at a constant pressure. The usual recipe is to employ the Berendsen barostat first, which displays a first-order volume relaxation efficient in equilibration but results in incorrect volume fluctuations, followed by a second-order or a Monte Carlo barostat for production runs. In this paper, we introduce stochastic cell rescaling, a first-order barostat that samples the correct volume fluctuations by including a suitable noise term. The algorithm is shown to report volume fluctuations compatible with the isobaric ensemble and its anisotropic variant is tested on a membrane simulation. Stochastic cell rescaling can be straightforwardly implemented in the existing codes and can be used effectively in both equilibration and production phases
Reweighting of molecular simulations with explicit-solvent SAXS restraints elucidates ion-dependent RNA ensembles
Full text linkSmall-angle X-ray scattering (SAXS) experiments are increasingly used to
probe RNA structure. A number of \emph{forward models} that relate measured
SAXS intensities and structural features, and that are suitable to model either
explicit-solvent effects or solute dynamics, have been proposed in the past
years. Here we introduce an approach that integrates atomistic molecular
dynamics simulations and SAXS experiments to reconstruct RNA structural
ensembles while simultaneously accounting for both RNA conformational dynamics
and explicit-solvent effects. Our protocol exploits SAXS pure-solute forward
models and enhanced sampling methods to sample an heterogenous ensemble of
structures, with no information towards the experiments provided on-the-fly.
The generated structural ensemble is then reweighted through the maximum
entropy principle so as to match reference SAXS experimental data at multiple
ionic conditions. Importantly, accurate explicit-solvent forward models are
used at this reweighting stage. We apply this framework to the
GTPase-associated center, a relevant RNA molecule involved in protein
translation, in order to elucidate its ion-dependent conformational ensembles.
We show that (a) both solvent and dynamics are crucial to reproduce
experimental SAXS data and (b) the resulting dynamical ensembles contain an
ion-dependent fraction of extended structures.Comment: Supporting information included in ancillary files. This version
includes corrections implemented after receiving feedbacks from the communit
Similarities and Differences in Ligand Binding to Protein and RNA Targets: The Case of Riboflavin
No full textIt is nowadays clear that RNA molecules can play active roles in several biological processes. As a result, an increasing number of RNAs are gradually being identified as potentially druggable targets. In particular, noncoding RNAs can adopt highly organized conformations that are suitable for drug binding. However, RNAs are still considered challenging targets due to their complex structural dynamics and high charge density. Thus, elucidating relevant features of drug-RNA binding is fundamental for advancing drug discovery. Here, by using Molecular Dynamics simulations, we compare key features of ligand binding to proteins with those observed in RNA. Specifically, we explore similarities and differences in terms of (i) conformational flexibility of the target, (ii) electrostatic contribution to binding free energy, and (iii) water and ligand dynamics. As a test case, we examine binding of the same ligand, namely riboflavin, to protein and RNA targets, specifically the riboflavin (RF) kinase and flavin mononucleotide (FMN) riboswitch. The FMN riboswitch exhibited enhanced fluctuations and explored a wider conformational space, compared to the protein target, underscoring the importance of RNA flexibility in ligand binding. Conversely, a similar electrostatic contribution to the binding free energy of riboflavin was found. Finally, greater stability of water molecules was observed in the FMN riboswitch compared to the RF kinase, possibly due to the different shape and polarity of the pockets
Probing allosteric communication with combined molecular dynamics simulations and network analysis
Full text linkUnderstanding the allosteric mechanisms within biomolecules involved in diseases is of paramount importance for drug discovery. Indeed, characterizing communication pathways and critical hotspots in signal transduction can guide a rational approach to leverage allosteric modulation for therapeutic purposes. While the atomistic signatures of allosteric processes are difficult to determine experimentally, computational methods can be a remarkable resource. Network analysis built on Molecular Dynamics simulation data is particularly suited in this respect and is gradually becoming of routine use. Herein, we collect the recent literature in the field, discussing different aspects and available options for network construction and analysis. We further highlight interesting refinements and extensions, eventually providing our perspective on this topic
Enhanced Molecular Dynamics Simulations of Intrinsically Disordered Proteins
No full textMolecular dynamics simulations represent a powerful tool to gain insights into structural and dynamical features of biomolecular systems. Nevertheless, their recognized limitation in terms of achievable timescales becomes particularly severe when dealing with slow processes. In such cases, the employment of enhanced sampling methods, which allow accelerating the characterization of rare events in a timeframe consistent with conventional computational resources, results as crucial. In particular, such advanced techniques have proven highly valuable in the context of protein folding and, specifically, to explore the conformational ensemble spanned by intrinsically disordered proteins (IDPs). Here, we describe how to set up molecular dynamics simulations with one of these enhanced sampling approaches (namely, Parallel Tempering Metadynamics in the Well-Tempered Ensemble) using the NTAIL peptide as a test case
Data-Driven Molecular Dynamics: A Multifaceted Challenge
Full text linkThe big data concept is currently revolutionizing several fields of science including drug discovery and development. While opening up new perspectives for better drug design and related strategies, big data analysis strongly challenges our current ability to manage and exploit an extraordinarily large and possibly diverse amount of information. The recent renewal of machine learning (ML)-based algorithms is key in providing the proper framework for addressing this issue. In this respect, the impact on the exploitation of molecular dynamics (MD) simulations, which have recently reached mainstream status in computational drug discovery, can be remarkable. Here, we review the recent progress in the use of ML methods coupled to biomolecular simulations with potentially relevant implications for drug design. Specifically, we show how different ML-based strategies can be applied to the outcome of MD simulations for gaining knowledge and enhancing sampling. Finally, we discuss how intrinsic limitations of MD in accurately modeling biomolecular systems can be alleviated by including information coming from experimental dat
Molecular dynamics simulations of chemically modified ribonucleotides
Full text linkPost-transcriptional modifications are crucial for RNA function, with roles
ranging from the stabilization of functional RNA structures to modulation of
RNA--protein interactions. Additionally, artificially modified RNAs have been
suggested as optimal oligonucleotides for therapeutic purposes. The impact of
chemical modifications on secondary structure has been rationalized for some of
the most common modifications. However, the characterization of how the
modifications affect the three-dimensional RNA structure and dynamics and its
capability to bind proteins is still highly challenging. Molecular dynamics
simulations, coupled with enhanced sampling methods and integration of
experimental data, provide a direct access to RNA structural dynamics. In the
context of RNA chemical modifications, alchemical simulations where a wild type
nucleotide is converted to a modified one are particularly common. In this
Chapter, we review recent molecular dynamics studies of modified
ribonucleotides. We discuss the technical aspects of the reviewed works,
including the employed force fields, enhanced sampling methods, and alchemical
methods, in a way that is accessible to experimentalists. Finally, we provide
our perspective on this quickly growing field of research. The goal of this
Chapter is to provide a guide for experimentalists to understand molecular
dynamics works and, at the same time, give to molecular dynamics experts a
solid review of published articles that will be a useful starting point for new
research.Comment: Submitted as a chapter for the book "RNA Structure and Function",
series "RNA Technologies", published by Springe
Refinement of molecular dynamics ensembles using experimental data and flexible forward models
Full text linkA novel method combining maximum entropy principle, the Bayesian-inference of
ensembles approach, and the optimization of empirical forward models is
presented. Here we focus on the Karplus parameters for RNA systems, which
relate the dihedral angles of , , and the dihedrals in the sugar
ring to the corresponding -coupling signal between coupling protons.
Extensive molecular simulations are performed on a set of RNA tetramers and
hexamers and combined with available nucleic-magnetic-resonance data. Within
the new framework, the sampled structural dynamics can be reweighted to match
experimental data while the error arising from inaccuracies in the forward
models can be corrected simultaneously and consequently does not leak into the
reweighted ensemble. Carefully crafted cross-validation procedure and
regularization terms enable obtaining transferable Karplus parameters. Our
approach identifies the optimal regularization strength and new sets of Karplus
parameters balancing good agreement between simulations and experiments with
minimal changes to the original ensemble.Comment: Submitted to journal; added zenodo link; replaced fig. 3 with correct
on
Toward empirical force fields that match experimental observables
Full text linkBiomolecular force fields have been traditionally derived based on a mixture of reference quantum chemistry data and experimental information obtained on small fragments. However, the possibility to run extensive molecular dynamics simulations on larger systems achieving ergodic sampling is paving the way to directly using such simulations along with solution experiments obtained on macromolecular systems. Recently, a number of methods have been introduced to automatize this approach. Here, we review these methods, highlight their relationship with machine learning methods, and discuss the open challenges in the field
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