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...
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PeerJ Inc.
2019-10-01
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Online Access: | https://peerj.com/articles/7904.pdf |
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author | Alejandro A. Icazatti Juan M. Loyola Igal Szleifer Jorge A. Vila Osvaldo A. Martin |
author_facet | Alejandro A. Icazatti Juan M. Loyola Igal Szleifer Jorge A. Vila Osvaldo A. Martin |
author_sort | Alejandro A. Icazatti |
collection | DOAJ |
description | 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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institution | Directory Open Access Journal |
issn | 2167-8359 |
language | English |
last_indexed | 2024-03-09T06:34:26Z |
publishDate | 2019-10-01 |
publisher | PeerJ Inc. |
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series | PeerJ |
spelling | doaj.art-ae26a3bb898746c5b0978b6448e8b4652023-12-03T11:00:53ZengPeerJ Inc.PeerJ2167-83592019-10-017e790410.7717/peerj.7904Classification of RNA backbone conformations into rotamers using 13C′ chemical shifts: exploring how far we can goAlejandro A. Icazatti0Juan M. Loyola1Igal Szleifer2Jorge A. Vila3Osvaldo A. Martin4IMASL - CONICET, Universidad Nacional de San Luis, San Luis, ArgentinaIMASL - CONICET, Universidad Nacional de San Luis, San Luis, ArgentinaDepartment of Biomedical Engineering, Northwestern University, Evanston, IL, United States of AmericaIMASL - CONICET, Universidad Nacional de San Luis, San Luis, ArgentinaIMASL - CONICET, Universidad Nacional de San Luis, San Luis, ArgentinaThe 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.https://peerj.com/articles/7904.pdfRNARotamersMachine learningChemical shiftsDFT |
spellingShingle | Alejandro A. Icazatti Juan M. Loyola Igal Szleifer Jorge A. Vila Osvaldo A. Martin Classification of RNA backbone conformations into rotamers using 13C′ chemical shifts: exploring how far we can go PeerJ RNA Rotamers Machine learning Chemical shifts DFT |
title | Classification of RNA backbone conformations into rotamers using 13C′ chemical shifts: exploring how far we can go |
title_full | Classification of RNA backbone conformations into rotamers using 13C′ chemical shifts: exploring how far we can go |
title_fullStr | Classification of RNA backbone conformations into rotamers using 13C′ chemical shifts: exploring how far we can go |
title_full_unstemmed | Classification of RNA backbone conformations into rotamers using 13C′ chemical shifts: exploring how far we can go |
title_short | Classification of RNA backbone conformations into rotamers using 13C′ chemical shifts: exploring how far we can go |
title_sort | classification of rna backbone conformations into rotamers using 13c chemical shifts exploring how far we can go |
topic | RNA Rotamers Machine learning Chemical shifts DFT |
url | https://peerj.com/articles/7904.pdf |
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