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.

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.

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.

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.

Unraveling Mg-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 Mg 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.

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.

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.

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.