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Bussi, G.

Publications and source records attributed to Bussi, G..

3 recordsLinked to original sources

IMPROVING THE PERFORMANCE OF THE RNA AMBER FORCE FIELD BY TUNING THE HYDROGEN-BONDING INTERACTIONS

Molecular dynamics (MD) simulations became a leading tool for investigation of structural dynamics of nucleic acids. Despite recent efforts to improve the empirical potentials (force fields, ffs), RNA ffs have persisting deficiencies, which hamper their utilization in quantitatively accurate simulations. Previous studies have shown that at least two salient problems contribute to difficulties in description of free-energy landscapes of small RNA motifs: (i) excessive stabilization of the unfolded single-stranded RNA ensemble by intramolecular base-phosphate and sugar-phosphate interactions, and (ii) destabilization of the native folded state by underestimation of stability of base pairing. Here, we introduce a general ff term (gHBfix) that can selectively fine-tune non-bonding interaction terms in RNA ffs, in particular the H-bonds. gHBfix potential affects the pair-wise interactions between all possible pairs of the specific atom types, while all other interactions remain intact, i.e., it is not a structure-based model. In order to probe the ability of the gHBfix potential to refine the ff non-bonded terms, we performed an extensive set of folding simulations of RNA tetranucleotides and tetraloops. Based on these data we propose particular gHBfix parameters to modify the AMBER RNA ff. The suggested parametrization significantly improves the agreement between experimental data and the simulation conformational ensembles, although our current ff version still remains far from being flawless. While attempts to tune the RNA ffs by conventional reparametrizations of dihedral potentials or non-bonded terms can lead to major undesired side effects as we demonstrate for some recently published ffs, gHBfix has a clear promising potential to improve the ff performance while avoiding introduction of major new imbalances.

biochemistry

Analyze Nucleic Acids Structures and Trajectories with Barnaba.

RNA molecules are highly dynamic systems characterized by a complex interplay between sequence, structure, dynamics, and function. Molecular simulations can potentially provide powerful insights into the nature of these relationships. The analysis of structures and molecular trajectories of nucleic acids can be non-trivial because it requires processing very high-dimensional data that are not easy to visualize and interpret.\n\nHere we introduce Barnaba, a Python library aimed at facilitating the analysis of nucleic acids structures and molecular simulations. The software consists of a variety of analysis tools that allow the user to i) calculate distances between three-dimensional structures using different metrics, ii) back-calculate experimental data from three-dimensional structures, iii) perform cluster analysis and dimensionality reductions, iv) search three-dimensional motifs in PDB structures and trajectories and v) construct elastic network models (ENM) for nucleic acids and nucleic acids-protein complexes.\n\nIn addition, Barnaba makes it possible to calculate torsion angles, pucker conformations and to detect base-pairing/base-stacking interactions. Barnaba produces graphics that conveniently visualize both extended secondary structure and dynamics for a set of molecular conformations. The software is available as a command-line tool as well as a library, and supports a variety of 1le formats such as PDB, dcd and xtc 1les. Source code, documentation and examples are freely available at https://github.com/srnas/barnaba under GNU GPLv3 license.

bioinformatics

Conformational Ensemble of RNA Oligonucleotides from Reweighted Molecular Simulations

We determine the conformational ensemble of four RNA tetranucleotides by using available nuclear magnetic spectroscopy data in conjunction with extensive atomistic molecular dynamics simulations. This combination is achieved by applying a reweighting scheme based on the maximum entropy principle. We provide a quantitative estimate for the population of different conformational states by considering different NMR parameters, including distances derived from nuclear Overhauser effect intensities and scalar coupling constants. We show the usefulness of the method as a general tool for studying the conformational dynamics of flexible biomolecules as well as for detecting inaccuracies in molecular dynamics force fields.

biophysics