FRET-guided modeling of nucleic acids.

The functional diversity of RNAs is encoded in their innate conformational heterogeneity. The combination of single-molecule spectroscopy and computational modeling offers new attractive opportunities to map structural transitions within nucleic acid ensembles. Here, we describe a framework to harmonize single-molecule Förster resonance energy transfer (FRET) measurements with molecular dynamics simulations and de novo structure prediction. Using either all-atom or implicit fluorophore modeling, we recreate FRET experiments in silico, visualize the underlying structural dynamics and quantify the reaction coordinates. Using multiple accessible-contact volumes as a post hoc scoring method for fragment assembly in Rosetta, we demonstrate that FRET can be used to filter a de novo RNA structure prediction ensemble by refuting models that are not compatible with in vitro FRET measurement. We benchmark our FRET-assisted modeling approach on double-labeled DNA strands and validate it against an intrinsically dynamic manganese(II)-binding riboswitch. We show that a FRET coordinate describing the assembly of a four-way junction allows our pipeline to recapitulate the global fold of the riboswitch displayed by the crystal structure. We conclude that computational fluorescence spectroscopy facilitates the interpretability of dynamic structural ensembles and improves the mechanistic understanding of nucleic acid interactions.

Zooming in on the O-O Bond Formation-An Ab Initio Molecular Dynamics Study Applying Enhanced Sampling Techniques.

Mastering artificial water oxidation is a key step on moving away from fossil fuels toward a carbon emission-free society. Unfortunately, the crucial chemical transformation of this reaction, the O-O bond formation, is still not well understood, even though there are various known active water oxidation catalysts, such as Ru-based catalysts bearing a Py5 ligand. Those were recently investigated both experimentally and using a static density functional theory (DFT) approach based on geometry optimizations. In this work, we shed light on the O-O formation catalyzed by those Ru-based complexes, utilizing enhanced sampling techniques such as the Bluemoon ensemble and metadynamics together with high-performance DFT-based molecular dynamics simulations. This allowed unprecedented detailed insights into the process of the oxygen-oxygen bond formation and also extended the view on the reaction network and the flexibility of the product state because of the consideration of the dynamics at ambient conditions. Our model system contained both the catalyst and a large number of explicit water molecules which can participate in the reaction and stabilize intermediates. Moreover, it is demonstrated how crucial the choice of the collective variable is in order to capture relevant features of the studied reaction.

Metal ions and sugar puckering balance single-molecule kinetic heterogeneity in RNA and DNA tertiary contacts.

The fidelity of group II intron self-splicing and retrohoming relies on long-range tertiary interactions between the intron and its flanking exons. By single-molecule FRET, we explore the binding kinetics of the most important, structurally conserved contact, the exon and intron binding site 1 (EBS1/IBS1). A comparison of RNA-RNA and RNA-DNA hybrid contacts identifies transient metal ion binding as a major source of kinetic heterogeneity which typically appears in the form of degenerate FRET states. Molecular dynamics simulations suggest a structural link between heterogeneity and the sugar conformation at the exon-intron binding interface. While Mg2+ ions lock the exon in place and give rise to long dwell times in the exon bound FRET state, sugar puckering alleviates this structural rigidity and likely promotes exon release. The interplay of sugar puckering and metal ion coordination may be an important mechanism to balance binding affinities of RNA and DNA interactions in general.

RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview.

With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the available RNA simulation literature, ranging from studies of the smallest RNA oligonucleotides to investigations of the entire ribosome. Our review encompasses tetranucleotides, tetraloops, a number of small RNA motifs, A-helix RNA, kissing-loop complexes, the TAR RNA element, the decoding center and other important regions of the ribosome, as well as assorted others systems. Extended sections are devoted to RNA-ion interactions, ribozymes, riboswitches, and protein/RNA complexes. Our overview is written for as broad of an audience as possible, aiming to provide a much-needed interdisciplinary bridge between computation and experiment, together with a perspective on the future of the field.

Unraveling Mg2+-RNA binding with atomistic molecular dynamics.

Interaction with divalent cations is of paramount importance for RNA structural stability and function. We report here a detailed molecular dynamics study of all the possible binding sites for Mg2+ on an RNA duplex, including both direct (inner sphere) and indirect (outer sphere) binding. In order to tackle sampling issues, we develop a modified version of bias-exchange metadynamics, which allows us to simultaneously compute affinities with previously unreported statistical accuracy. Results correctly reproduce trends observed in crystallographic databases. Based on this, we simulate a carefully chosen set of models that allows us to quantify the effects of competition with monovalent cations, RNA flexibility, and RNA hybridization. Our simulations reproduce the decrease and increase of Mg2+ affinity due to ion competition and hybridization, respectively, and predict that RNA flexibility has a site-dependent effect. This suggests a nontrivial interplay between RNA conformational entropy and divalent cation binding.

Modeling the coverage of an AFM tip by enzymes and its application in nanobiosensors.

A stochastic simulation of adsorption processes was developed to simulate the coverage of an atomic force microscope (AFM) tip with enzymes represented as rigid polyhedrons. From geometric considerations of the enzyme structure and AFM tip, we could estimate the average number of active sites available to interact with substrate molecules in the bulk. The procedure was exploited to determine the interaction force between acetyl-CoA carboxylase enzyme (ACC enzyme) and its substrate diclofop, for which steered molecular dynamics (SMD) was used. The theoretical force of (1.6±0.5) nN per enzyme led to a total force in remarkable agreement with the experimentally measured force with AFM, thus demonstrating the usefulness of the procedure proposed here to assist in the interpretation of nanobiosensors experiments.

Molecular modeling of enzyme attachment on AFM probes.

The immobilization of enzymes on atomic force microscope tip (AFM tip) surface is a crucial step in the development of nanobiosensors to be used in detection process. In this work, an atomistic modeling of the attachment of the acetyl coenzyme A carboxylase (ACC enzyme) on a functionalized AFM tip surface is proposed. Using electrostatic considerations, suitable enzyme-surface orientations with the active sites of the ACC enzyme available for interactions with bulk molecules were found. A 50 ns molecular dynamics trajectory in aqueous solution was obtained and surface contact area, hydrogen bonding and protein stability were analyzed. The enzyme-surface model proposed here with minor adjustment can be applied to study antigen-antibody interactions as well as enzyme immobilization on silica for chromatography applications.

Designing an enzyme-based nanobiosensor using molecular modeling techniques.

Nanobiosensors can be built via functionalization of atomic force microscopy (AFM) tips with biomolecules capable of interacting with the analyte on a substrate, and the detection being performed by measuring the force between the immobilized biomolecule and the analyte. The optimization of such sensors may require multiple experiments to determine suitable experimental conditions for the immobilization and detection. In this study we employ molecular modeling techniques to assist in the design of nanobiosensors to detect herbicides. As a proof of principle, the properties of acetyl co-enzyme A carboxylase (ACC) were obtained with molecular dynamics simulations, from which the dimeric form in an aqueous solution was found to be more suitable for immobilization owing to a smaller structural fluctuation than the monomeric form. Upon solving the nonlinear Poisson-Boltzmann equation using a finite-difference procedure, we found that the active sites of ACC exhibited a positive surface potential while the remainder of the ACC surface was negatively charged. Therefore, optimized biosensors should be prepared with electrostatic adsorption of ACC onto an AFM tip functionalized with positively charged groups, leaving the active sites exposed to the analyte. The preferential orientation for the herbicides diclofop and atrazine with the ACC active site was determined by molecular docking calculations which displayed an inhibition coefficient of 0.168 µM for diclofop, and 44.11 µM for atrazine. This binding selectivity for the herbicide family of diclofop was confirmed by semiempirical PM6 quantum chemical calculations which revealed that ACC interacts more strongly with the herbicide diclofop than with atrazine, showing binding energies of -119.04 and +8.40 kcal mol(-1), respectively. The initial measurements of the proposed nanobiosensor validated the theoretical calculations and displayed high selectivity for the family of the diclofop herbicides.