Discrete molecular dynamics

Proctor, Elizabeth A.; Ding, Feng; Dokholyan, Nikolay V.

doi:10.1002/wcms.4

Cited by 112 publications

(117 citation statements)

References 55 publications

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“…Structure modeling was performed using discrete molecular dynamics (DMD) simulations (Dokholyan et al 1998;Proctor et al 2011). The Dokholyan group's structure prediction pipeline comprises sequential steps.…”

Section: Dokholyan and Weeks Groupsmentioning

confidence: 99%

RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme

Miao

Adamiak

Antczak

et al. 2017

RNA

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185

209

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RNA-Puzzles is a collective experiment in blind 3D RNA structure prediction. We report here a third round of RNA-Puzzles. Five puzzles, 4, 8, 12, 13, 14, all structures of riboswitch aptamers and puzzle 7, a ribozyme structure, are included in this round of the experiment. The riboswitch structures include biological binding sites for small molecules (S-adenosyl methionine, cyclic diadenosine monophosphate, 5-amino 4-imidazole carboxamide riboside 5 ′ -triphosphate, glutamine) and proteins (YbxF), and one set describes large conformational changes between ligand-free and ligand-bound states. The Varkud satellite ribozyme is the most recently solved structure of a known large ribozyme. All puzzles have established biological functions and require structural understanding to appreciate their molecular mechanisms. Through the use of fast-track experimental data, including multidimensional chemical mapping, and accurate prediction of RNA secondary structure, a large portion of the contacts in 3D have been predicted correctly leading to similar topologies for the top ranking predictions. Template-based and homologyderived predictions could predict structures to particularly high accuracies. However, achieving biological insights from de novo prediction of RNA 3D structures still depends on the size and complexity of the RNA. Blind computational predictions of RNA structures already appear to provide useful structural information in many cases. Similar to the previous RNA-Puzzles Round II experiment, the prediction of non-Watson-Crick interactions and the observed high atomic clash scores reveal a notable need for an algorithm of improvement. All prediction models and assessment results are available at http://ahsoka.ustrasbg.fr/rnapuzzles/.

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Section: Dokholyan and Weeks Groupsmentioning

confidence: 99%

RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme

Miao

Adamiak

Antczak

et al. 2017

RNA

Self Cite

185

209

View full text Add to dashboard Cite

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“…The Dokholyan group at the University of North Carolina at Chapel Hill in collaboration with the Ding group from Clemson provided predictions for Problems 5, 6, and 10 using multiscale discrete molecular dynamics (DMD) method (Proctor et al 2011;Shirvanyants et al 2012). The structure modeling was performed with coarse-grained folding simulations followed by an all-atom reconstruction.…”

Section: Dokholyan Groupmentioning

confidence: 99%

RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures

Miao

Adamiak

Blanchet

et al. 2015

RNA

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180

201

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This paper is a report of a second round of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited or no homology with previously solved RNA molecules. A lariat-capping ribozyme, as well as riboswitches complexed to adenosylcobalamin and tRNA, were predicted by seven groups using RNAComposer, ModeRNA/SimRNA, Vfold, Rosetta, DMD, MC-Fold, 3dRNA, and AMBER refinement. Some groups derived models using data from state-of-the-art chemicalmapping methods (SHAPE, DMS, CMCT, and mutate-and-map). The comparisons between the predictions and the three subsequently released crystallographic structures, solved at diffraction resolutions of 2.5-3.2 Å, were carried out automatically using various sets of quality indicators. The comparisons clearly demonstrate the state of present-day de novo prediction abilities as well as the limitations of these state-of-the-art methods. All of the best prediction models have similar topologies to the native structures, which suggests that computational methods for RNA structure prediction can already provide useful structural information for biological problems. However, the prediction accuracy for non-Watson-Crick interactions, key to proper folding of RNAs, is low and some predicted models had high Clash Scores. These two difficulties point to some of the continuing bottlenecks in RNA structure prediction. All submitted models are available for download at http://ahsoka.u-strasbg .fr/rnapuzzles/.

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“…The Dokholyan model more closely resembles our model; three beads represent each nucleotide (rather than five as in our model), and a sampling technique referred to as discrete molecular dynamics (DMD) is used to determine structure [19]. DMD breaks the interaction potential into discrete intervals, greatly simplifying molecule collision events [28]. DMD is a powerful tool with high efficiency; in this comparison, the Dokholyan group achieved the favorable structure for Puzzle 2.…”

Section: Resultsmentioning

confidence: 98%

Multiscale modeling of RNA 3D structures

Bell

Xia

Ren

2013

2013 Biomedical Sciences and Engineering Conference (BSEC)

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Balancing accuracy and computational efficiency while studying biomolecular structures and dynamics necessitates scalable modeling techniques. We have been developing a coarse-grained model for RNA that uses pseudoatoms in place of all-atom representation. By reducing the number of interactions and mean-field representation of environmental effects, significant improvement in computational efficiency is achieved in a comparison to all-atom based physical modeling approaches. A five bead coarse-grained model utilized for RNA 3D structure prediction is presented. Unique features of this framework include the direct mapping between all atom and coarse-grained models, incorporation of electrostatic interactions, continuous and analytical energy function that can be used in molecular dynamics simulations, and statistical derived parameters. Here we present the basic framework of our model and recent applications to RNA folding.

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Discrete molecular dynamics

Cited by 112 publications

References 55 publications

RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme

RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme

RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures

Multiscale modeling of RNA 3D structures

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