Cargando…
Prediction of Protein Structure Using Surface Accessibility Data
An approach to the de novo structure prediction of proteins is described that relies on surface accessibility data from NMR paramagnetic relaxation enhancements by a soluble paramagnetic compound (sPRE). This method exploits the distance‐to‐surface information encoded in the sPRE data in the chemica...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5026166/ https://www.ncbi.nlm.nih.gov/pubmed/27560616 http://dx.doi.org/10.1002/anie.201604788 |
_version_ | 1782454087363592192 |
---|---|
author | Hartlmüller, Christoph Göbl, Christoph Madl, Tobias |
author_facet | Hartlmüller, Christoph Göbl, Christoph Madl, Tobias |
author_sort | Hartlmüller, Christoph |
collection | PubMed |
description | An approach to the de novo structure prediction of proteins is described that relies on surface accessibility data from NMR paramagnetic relaxation enhancements by a soluble paramagnetic compound (sPRE). This method exploits the distance‐to‐surface information encoded in the sPRE data in the chemical shift‐based CS‐Rosetta de novo structure prediction framework to generate reliable structural models. For several proteins, it is demonstrated that surface accessibility data is an excellent measure of the correct protein fold in the early stages of the computational folding algorithm and significantly improves accuracy and convergence of the standard Rosetta structure prediction approach. |
format | Online Article Text |
id | pubmed-5026166 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-50261662016-10-03 Prediction of Protein Structure Using Surface Accessibility Data Hartlmüller, Christoph Göbl, Christoph Madl, Tobias Angew Chem Int Ed Engl Communications An approach to the de novo structure prediction of proteins is described that relies on surface accessibility data from NMR paramagnetic relaxation enhancements by a soluble paramagnetic compound (sPRE). This method exploits the distance‐to‐surface information encoded in the sPRE data in the chemical shift‐based CS‐Rosetta de novo structure prediction framework to generate reliable structural models. For several proteins, it is demonstrated that surface accessibility data is an excellent measure of the correct protein fold in the early stages of the computational folding algorithm and significantly improves accuracy and convergence of the standard Rosetta structure prediction approach. John Wiley and Sons Inc. 2016-08-25 2016-09-19 /pmc/articles/PMC5026166/ /pubmed/27560616 http://dx.doi.org/10.1002/anie.201604788 Text en © 2016 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Communications Hartlmüller, Christoph Göbl, Christoph Madl, Tobias Prediction of Protein Structure Using Surface Accessibility Data |
title | Prediction of Protein Structure Using Surface Accessibility Data |
title_full | Prediction of Protein Structure Using Surface Accessibility Data |
title_fullStr | Prediction of Protein Structure Using Surface Accessibility Data |
title_full_unstemmed | Prediction of Protein Structure Using Surface Accessibility Data |
title_short | Prediction of Protein Structure Using Surface Accessibility Data |
title_sort | prediction of protein structure using surface accessibility data |
topic | Communications |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5026166/ https://www.ncbi.nlm.nih.gov/pubmed/27560616 http://dx.doi.org/10.1002/anie.201604788 |
work_keys_str_mv | AT hartlmullerchristoph predictionofproteinstructureusingsurfaceaccessibilitydata AT goblchristoph predictionofproteinstructureusingsurfaceaccessibilitydata AT madltobias predictionofproteinstructureusingsurfaceaccessibilitydata |