Dataset Information

Elucidating molecular motion through structural and dynamic filters of energy-minimized conformer ensembles.

ABSTRACT: Complex RNA structures are constructed from helical segments connected by flexible loops that move spontaneously and in response to binding of small molecule ligands and proteins. Understanding the conformational variability of RNA requires the characterization of the coupled time evolution of interconnected flexible domains. To elucidate the collective molecular motions and explore the conformational landscape of the HIV-1 TAR RNA, we describe a new methodology that utilizes energy-minimized structures generated by the program "Fragment Assembly of RNA with Full-Atom Refinement (FARFAR)". We apply structural filters in the form of experimental residual dipolar couplings (RDCs) to select a subset of discrete energy-minimized conformers and carry out principal component analyses (PCA) to corroborate the choice of the filtered subset. We use this subset of structures to calculate solution T1 and T(1?) relaxation times for (13)C spins in multiple residues in different domains of the molecule using two simulation protocols that we previously published. We match the experimental T1 times to within 2% and the T(1?) times to within less than 10% for helical residues. These results introduce a protocol to construct viable dynamic trajectories for RNA molecules that accord well with experimental NMR data and support the notion that the motions of the helical portions of this small RNA can be described by a relatively small number of discrete conformations exchanging over time scales longer than 1 ?s.

SUBMITTER: Emani PS

PROVIDER: S-EPMC3983377 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Elucidating molecular motion through structural and dynamic filters of energy-minimized conformer ensembles.

Emani Prashant S PS Bardaro Michael F MF Huang Wei W Aragon Sergio S Varani Gabriele G Drobny Gary P GP

The journal of physical chemistry. B 20140211 7

Complex RNA structures are constructed from helical segments connected by flexible loops that move spontaneously and in response to binding of small molecule ligands and proteins. Understanding the conformational variability of RNA requires the characterization of the coupled time evolution of interconnected flexible domains. To elucidate the collective molecular motions and explore the conformational landscape of the HIV-1 TAR RNA, we describe a new methodology that utilizes energy-minimized st ...[more]

PMID: 24479561

Similar Datasets

Project description:BackgroundThe microtubule-associated protein tau forms aggregates in different neurodegenerative diseases called tauopathies. Prior work has shown that a single P301L mutation in tau gene, MAPT, can promote alternative tau folding pathways that correlate with divergent clinical diagnoses. Using progressive chemical denaturation, some tau preparations from the brain featured complex transitions starting at low concentrations of guanidine hydrochloride (GdnHCl) denaturant, indicating an ensemble of differently folded tau species called conformers. On the other hand, brain samples with abundant, tangle-like pathology had simple GdnHCl unfolding profile resembling the profile of fibrillized recombinant tau and suggesting a unitary conformer composition. In studies here we sought to understand tau conformer progression and potential relationships with condensed liquid states, as well as associated perturbations in cell biological processes.ResultsAs starting material, we used brain samples from P301L transgenic mice containing tau conformer ensembles that unfolded at low GdnHCl concentrations and with signatures resembling brain material from P301L subjects presenting with language or memory problems. We seeded reporter cells expressing a soluble form of 4 microtubule-binding repeat tau fused to GFP or YFP reporter moieties, resulting in redistribution of dispersed fluorescence signals into focal assemblies that could fuse together and move within processes between adjacent cells. Nuclear envelope fluorescent tau signals and small fluorescent inclusions behaved as a demixed liquid phase, indicative of liquid-liquid phase separation (LLPS); these droplets exhibited spherical morphology, fusion events and could recover from photobleaching. Moreover, juxtanuclear tau assemblies were associated with disrupted nuclear transport and reduced cell viability in a stable cell line. Staining for thioflavin S (ThS) became more prevalent as tau-derived inclusions attained cross-sectional area greater than 3 μm2, indicating (i) a bipartite composition, (ii) in vivo progression of tau conformers, and (iii) that a mass threshold applying to demixed condensates may drive liquid-solid transitions.ConclusionsTau conformer ensembles characterized by denaturation at low GdnHCl concentration templated the production of condensed droplets in living cells. These species exhibit dynamic changes and develop in vivo, and the larger ThS-positive assemblies may represent a waystation to arrive at intracellular fibrillar tau inclusions seen in end-stage genetic tauopathies.

Project description:BackgroundThe ability to generate, visualize, and analyze motions of biomolecules has made a significant impact upon modern biology. Molecular Dynamics has gained substantial use, but remains computationally demanding and difficult to setup for many biologists. Elastic network models (ENMs) are an alternative and have been shown to generate the dominant equilibrium motions of biomolecules quickly and efficiently. These dominant motions have been shown to be functionally relevant and also to indicate the likely direction of conformational changes. Most structures have a small number of dominant motions. Comparing computed motions to the structure's conformational ensemble derived from a collection of static structures or frames from an MD trajectory is an important way to understand functional motions as well as evaluate the models. Modes of motion computed from ENMs can be visualized to gain functional and mechanistic understanding and to compute useful quantities such as average positional fluctuations, internal distance changes, collectiveness of motions, and directional correlations within the structure.ResultsOur new software, MAVEN, aims to bring ENMs and their analysis to a broader audience by integrating methods for their generation and analysis into a user friendly environment that automates many of the steps. Models can be constructed from raw PDB files or density maps, using all available atomic coordinates or by employing various coarse-graining procedures. Visualization can be performed either with our software or exported to molecular viewers. Mixed resolution models allow one to study atomic effects on the system while retaining much of the computational speed of the coarse-grained ENMs. Analysis options are available to further aid the user in understanding the computed motions and their importance for its function.ConclusionMAVEN has been developed to simplify ENM generation, allow for diverse models to be used, and facilitate useful analyses, all on the same platform. This represents an integrated approach that incorporates all four levels of the modeling process - generation, evaluation, analysis, visualization - and also brings to bear multiple ENM types. The intension is to provide a versatile modular suite of programs to a broader audience. MAVEN is available for download at http://maven.sourceforge.net.

Project description:Identifying bioactive conformations of small molecules is an essential process for virtual screening applications relying on three-dimensional structure such as molecular docking. For most small molecules, conformer generators retrieve at least one bioactive-like conformation, with an atomic root-mean-square deviation (ARMSD) lower than 1 Å, among the set of low-energy conformers generated. However, there is currently no general method to prioritise these likely target-bound conformations in the ensemble. In this work, we trained atomistic neural networks (AtNNs) on 3D information of generated conformers of a curated subset of PDBbind ligands to predict the ARMSD to their closest bioactive conformation, and evaluated the early enrichment of bioactive-like conformations when ranking conformers by AtNN prediction. AtNN ranking was compared with bioactivity-unaware baselines such as ascending Sage force field energy ranking, and a slower bioactivity-based baseline ranking by ascending Torsion Fingerprint Deviation to the Maximum Common Substructure to the most similar molecule in the training set (TFD2SimRefMCS). On test sets from random ligand splits of PDBbind, ranking conformers using ComENet, the AtNN encoding the most 3D information, leads to early enrichment of bioactive-like conformations with a median BEDROC of 0.29 ± 0.02, outperforming the best bioactivity-unaware Sage energy ranking baseline (median BEDROC of 0.18 ± 0.02), and performing on a par with the bioactivity-based TFD2SimRefMCS baseline (median BEDROC of 0.31 ± 0.02). The improved performance of the AtNN and TFD2SimRefMCS baseline is mostly observed on test set ligands that bind proteins similar to proteins observed in the training set. On a more challenging subset of flexible molecules, the bioactivity-unaware baselines showed median BEDROCs up to 0.02, while AtNNs and TFD2SimRefMCS showed median BEDROCs between 0.09 and 0.13. When performing rigid ligand re-docking of PDBbind ligands with GOLD using the 1% top-ranked conformers, ComENet ranked conformers showed a higher successful docking rate than bioactivity-unaware baselines, with a rate of 0.48 ± 0.02 compared to CSD probability baseline with a rate of 0.39 ± 0.02. Similarly, on a pharmacophore searching experiment, selecting the 20% top-ranked conformers ranked by ComENet showed higher hit rate compared to baselines. Hence, the approach presented here uses AtNNs successfully to focus conformer ensembles towards bioactive-like conformations, representing an opportunity to reduce computational expense in virtual screening applications on known targets that require input conformations.

Project description:BACKGROUND:The accuracy of any 3D-QSAR, Pharmacophore and 3D-similarity based chemometric target fishing models are highly dependent on a reasonable sample of active conformations. Since a number of diverse conformational sampling algorithm exist, which exhaustively generate enough conformers, however model building methods relies on explicit number of common conformers. RESULTS:In this work, we have attempted to make clustering algorithms, which could find reasonable number of representative conformer ensembles automatically with asymmetric dissimilarity matrix generated from openeye tool kit. RMSD was the important descriptor (variable) of each column of the N × N matrix considered as N variables describing the relationship (network) between the conformer (in a row) and the other N conformers. This approach used to evaluate the performance of the well-known clustering algorithms by comparison in terms of generating representative conformer ensembles and test them over different matrix transformation functions considering the stability. In the network, the representative conformer group could be resampled for four kinds of algorithms with implicit parameters. The directed dissimilarity matrix becomes the only input to the clustering algorithms. CONCLUSIONS:Dunn index, Davies-Bouldin index, Eta-squared values and omega-squared values were used to evaluate the clustering algorithms with respect to the compactness and the explanatory power. The evaluation includes the reduction (abstraction) rate of the data, correlation between the sizes of the population and the samples, the computational complexity and the memory usage as well. Every algorithm could find representative conformers automatically without any user intervention, and they reduced the data to 14-19% of the original values within 1.13 s per sample at the most. The clustering methods are simple and practical as they are fast and do not ask for any explicit parameters. RCDTC presented the maximum Dunn and omega-squared values of the four algorithms in addition to consistent reduction rate between the population size and the sample size. The performance of the clustering algorithms was consistent over different transformation functions. Moreover, the clustering method can also be applied to molecular dynamics sampling simulation results.

Dataset Information

Elucidating molecular motion through structural and dynamic filters of energy-minimized conformer ensembles.

Publications

Elucidating molecular motion through structural and dynamic filters of energy-minimized conformer ensembles.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets