Classification of RNA backbone conformations into rotamers using 13C' chemical shifts: Exploring how far we can go
The conformational space of the ribose-phosphate backbone is very complex as it is defined in terms of six torsional angles. To help delimit the RNA backbone conformational preferences, 46 rotamers have been defined in terms of these torsional angles. In the present work, we use the ribose experimen...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/136394 |
| Acceso en línea: | http://hdl.handle.net/11336/136394 |
| Access Level: | acceso abierto |
| Palabra clave: | CHEMICAL SHIFTS DFT MACHINE LEARNING RNA ROTAMERS https://purl.org/becyt/ford/1.6 https://purl.org/becyt/ford/1 |
| Sumario: | The conformational space of the ribose-phosphate backbone is very complex as it is defined in terms of six torsional angles. To help delimit the RNA backbone conformational preferences, 46 rotamers have been defined in terms of these torsional angles. In the present work, we use the ribose experimental and theoretical 13C' chemical shifts data and machine learning methods to classify RNA backbone conformations into rotamers and families of rotamers. We show to what extent the experimental 13C' chemical shifts can be used to identify rotamers and discuss some problem with the theoretical computations of 13C' chemical shifts. |
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