Modulating RNA Alignment Using Directional Dynamic Kinks: Application in Determining an Atomic-Resolution Ensemble for a Hairpin using NMR Residual Dipolar Couplings Article Swipe
Loïc Salmon
,
George M. Giambaşu
,
Evgenia N. Nikolova
,
Katja Petzold
,
Akash Bhattacharya
,
David A. Case
,
Hashim M. Al‐Hashimi
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1021/jacs.5b07229
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1021/jacs.5b07229
Approaches that combine experimental data and computational molecular dynamics (MD) to determine atomic resolution ensembles of biomolecules require the measurement of abundant experimental data. NMR residual dipolar couplings (RDCs) carry rich dynamics information, however, difficulties in modulating overall alignment of nucleic acids have limited the ability to fully extract this information. We present a strategy for modulating RNA alignment that is based on introducing variable dynamic kinks in terminal helices. With this strategy, we measured seven sets of RDCs in a cUUCGg apical loop and used this rich data set to test the accuracy of an 0.8 μs MD simulation computed using the Amber ff10 force field as well as to determine an atomic resolution ensemble. The MD-generated ensemble quantitatively reproduces the measured RDCs, but selection of a sub-ensemble was required to satisfy the RDCs within error. The largest discrepancies between the RDC-selected and MD-generated ensembles are observed for the most flexible loop residues and backbone angles connecting the loop to the helix, with the RDC-selected ensemble resulting in more uniform dynamics. Comparison of the RDC-selected ensemble with NMR spin relaxation data suggests that the dynamics occurs on the ps-ns time scales as verified by measurements of R(1ρ) relaxation-dispersion data. The RDC-satisfying ensemble samples many conformations adopted by the hairpin in crystal structures indicating that intrinsic plasticity may play important roles in conformational adaptation. The approach presented here can be applied to test nucleic acid force fields and to characterize dynamics in diverse RNA motifs at atomic resolution.
Related Topics
Concepts
Chemistry
Residual dipolar coupling
Molecular dynamics
Relaxation (psychology)
Dipole
Conformational ensembles
Protein dynamics
Biological system
Nucleic acid
Residual
Resolution (logic)
Nuclear magnetic resonance spectroscopy
Chemical physics
Statistical physics
Computational chemistry
Algorithm
Physics
Stereochemistry
Artificial intelligence
Computer science
Psychology
Organic chemistry
Biology
Biochemistry
Social psychology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1021/jacs.5b07229
- OA Status
- green
- Cited By
- 33
- References
- 92
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2402984257
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2402984257Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1021/jacs.5b07229Digital Object Identifier
- Title
-
Modulating RNA Alignment Using Directional Dynamic Kinks: Application in Determining an Atomic-Resolution Ensemble for a Hairpin using NMR Residual Dipolar CouplingsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-08-26Full publication date if available
- Authors
-
Loïc Salmon, George M. Giambaşu, Evgenia N. Nikolova, Katja Petzold, Akash Bhattacharya, David A. Case, Hashim M. Al‐HashimiList of authors in order
- Landing page
-
https://doi.org/10.1021/jacs.5b07229Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://figshare.com/articles/journal_contribution/Modulating_RNA_Alignment_Using_Directional_Dynamic_Kinks_Application_in_Determining_an_Atomic_Resolution_Ensemble_for_a_Hairpin_using_NMR_Residual_Dipolar_Couplings/2121895Direct OA link when available
- Concepts
-
Chemistry, Residual dipolar coupling, Molecular dynamics, Relaxation (psychology), Dipole, Conformational ensembles, Protein dynamics, Biological system, Nucleic acid, Residual, Resolution (logic), Nuclear magnetic resonance spectroscopy, Chemical physics, Statistical physics, Computational chemistry, Algorithm, Physics, Stereochemistry, Artificial intelligence, Computer science, Psychology, Organic chemistry, Biology, Biochemistry, Social psychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
33Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2, 2023: 5, 2022: 1, 2021: 2Per-year citation counts (last 5 years)
- References (count)
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92Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.play | 218 |
| abstract_inverted_index.rich | 30, 87 |
| abstract_inverted_index.sets | 76 |
| abstract_inverted_index.spin | 179 |
| abstract_inverted_index.test | 91, 232 |
| abstract_inverted_index.that | 1, 59, 183, 214 |
| abstract_inverted_index.this | 49, 71, 86 |
| abstract_inverted_index.time | 190 |
| abstract_inverted_index.used | 85 |
| abstract_inverted_index.well | 108 |
| abstract_inverted_index.with | 163, 177 |
| abstract_inverted_index.Amber | 103 |
| abstract_inverted_index.RDCs, | 123 |
| abstract_inverted_index.acids | 41 |
| abstract_inverted_index.based | 61 |
| abstract_inverted_index.carry | 29 |
| abstract_inverted_index.data. | 23, 199 |
| abstract_inverted_index.field | 106 |
| abstract_inverted_index.force | 105, 235 |
| abstract_inverted_index.fully | 47 |
| abstract_inverted_index.kinks | 66 |
| abstract_inverted_index.ps-ns | 189 |
| abstract_inverted_index.roles | 220 |
| abstract_inverted_index.seven | 75 |
| abstract_inverted_index.using | 101 |
| abstract_inverted_index.(RDCs) | 28 |
| abstract_inverted_index.R(1ρ) | 197 |
| abstract_inverted_index.angles | 156 |
| abstract_inverted_index.apical | 82 |
| abstract_inverted_index.atomic | 12, 113, 246 |
| abstract_inverted_index.cUUCGg | 81 |
| abstract_inverted_index.error. | 136 |
| abstract_inverted_index.fields | 236 |
| abstract_inverted_index.helix, | 162 |
| abstract_inverted_index.motifs | 244 |
| abstract_inverted_index.occurs | 186 |
| abstract_inverted_index.scales | 191 |
| abstract_inverted_index.within | 135 |
| abstract_inverted_index.ability | 45 |
| abstract_inverted_index.adopted | 206 |
| abstract_inverted_index.applied | 230 |
| abstract_inverted_index.between | 140 |
| abstract_inverted_index.combine | 2 |
| abstract_inverted_index.crystal | 211 |
| abstract_inverted_index.dipolar | 26 |
| abstract_inverted_index.diverse | 242 |
| abstract_inverted_index.dynamic | 65 |
| abstract_inverted_index.extract | 48 |
| abstract_inverted_index.hairpin | 209 |
| abstract_inverted_index.largest | 138 |
| abstract_inverted_index.limited | 43 |
| abstract_inverted_index.nucleic | 40, 233 |
| abstract_inverted_index.overall | 37 |
| abstract_inverted_index.present | 52 |
| abstract_inverted_index.require | 17 |
| abstract_inverted_index.samples | 203 |
| abstract_inverted_index.satisfy | 132 |
| abstract_inverted_index.uniform | 170 |
| abstract_inverted_index.abundant | 21 |
| abstract_inverted_index.accuracy | 93 |
| abstract_inverted_index.approach | 225 |
| abstract_inverted_index.backbone | 155 |
| abstract_inverted_index.computed | 100 |
| abstract_inverted_index.dynamics | 8, 31, 185, 240 |
| abstract_inverted_index.ensemble | 118, 166, 176, 202 |
| abstract_inverted_index.flexible | 151 |
| abstract_inverted_index.helices. | 69 |
| abstract_inverted_index.however, | 33 |
| abstract_inverted_index.measured | 74, 122 |
| abstract_inverted_index.observed | 147 |
| abstract_inverted_index.required | 130 |
| abstract_inverted_index.residual | 25 |
| abstract_inverted_index.residues | 153 |
| abstract_inverted_index.strategy | 54 |
| abstract_inverted_index.suggests | 182 |
| abstract_inverted_index.terminal | 68 |
| abstract_inverted_index.variable | 64 |
| abstract_inverted_index.verified | 193 |
| abstract_inverted_index.alignment | 38, 58 |
| abstract_inverted_index.couplings | 27 |
| abstract_inverted_index.determine | 11, 111 |
| abstract_inverted_index.dynamics. | 171 |
| abstract_inverted_index.ensemble. | 115 |
| abstract_inverted_index.ensembles | 14, 145 |
| abstract_inverted_index.important | 219 |
| abstract_inverted_index.intrinsic | 215 |
| abstract_inverted_index.molecular | 7 |
| abstract_inverted_index.presented | 226 |
| abstract_inverted_index.resulting | 167 |
| abstract_inverted_index.selection | 125 |
| abstract_inverted_index.strategy, | 72 |
| abstract_inverted_index.Approaches | 0 |
| abstract_inverted_index.Comparison | 172 |
| abstract_inverted_index.connecting | 157 |
| abstract_inverted_index.indicating | 213 |
| abstract_inverted_index.modulating | 36, 56 |
| abstract_inverted_index.plasticity | 216 |
| abstract_inverted_index.relaxation | 180 |
| abstract_inverted_index.reproduces | 120 |
| abstract_inverted_index.resolution | 13, 114 |
| abstract_inverted_index.simulation | 99 |
| abstract_inverted_index.structures | 212 |
| abstract_inverted_index.adaptation. | 223 |
| abstract_inverted_index.introducing | 63 |
| abstract_inverted_index.measurement | 19 |
| abstract_inverted_index.resolution. | 247 |
| abstract_inverted_index.MD-generated | 117, 144 |
| abstract_inverted_index.RDC-selected | 142, 165, 175 |
| abstract_inverted_index.biomolecules | 16 |
| abstract_inverted_index.characterize | 239 |
| abstract_inverted_index.difficulties | 34 |
| abstract_inverted_index.experimental | 3, 22 |
| abstract_inverted_index.information, | 32 |
| abstract_inverted_index.information. | 50 |
| abstract_inverted_index.measurements | 195 |
| abstract_inverted_index.sub-ensemble | 128 |
| abstract_inverted_index.computational | 6 |
| abstract_inverted_index.conformations | 205 |
| abstract_inverted_index.discrepancies | 139 |
| abstract_inverted_index.RDC-satisfying | 201 |
| abstract_inverted_index.conformational | 222 |
| abstract_inverted_index.quantitatively | 119 |
| abstract_inverted_index.relaxation-dispersion | 198 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.85956219 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |