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The AUDANA algorithm for automated protein 3D structure determination from NMR NOE data
We introduce AUDANA (Automated Database-Assisted NOE Assignment), an algorithm for determining three-dimensional structures of proteins from NMR data that automates the assignment of 3D-NOE spectra, generates distance constraints, and conducts iterative high temperature molecular dynamics and simula...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4921114/ https://www.ncbi.nlm.nih.gov/pubmed/27169728 http://dx.doi.org/10.1007/s10858-016-0036-y |
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author | Lee, Woonghee Petit, Chad M. Cornilescu, Gabriel Stark, Jaime L. Markley, John L. |
author_facet | Lee, Woonghee Petit, Chad M. Cornilescu, Gabriel Stark, Jaime L. Markley, John L. |
author_sort | Lee, Woonghee |
collection | PubMed |
description | We introduce AUDANA (Automated Database-Assisted NOE Assignment), an algorithm for determining three-dimensional structures of proteins from NMR data that automates the assignment of 3D-NOE spectra, generates distance constraints, and conducts iterative high temperature molecular dynamics and simulated annealing. The protein sequence, chemical shift assignments, and NOE spectra are the only required inputs. Distance constraints generated automatically from ambiguously assigned NOE peaks are validated during the structure calculation against information from an enlarged version of the freely available PACSY database that incorporates information on protein structures deposited in the Protein Data Bank (PDB). This approach yields robust sets of distance constraints and 3D structures. We evaluated the performance of AUDANA with input data for 14 proteins ranging in size from 6 to 25 kDa that had 27–98 % sequence identity to proteins in the database. In all cases, the automatically calculated 3D structures passed stringent validation tests. Structures were determined with and without database support. In 9/14 cases, database support improved the agreement with manually determined structures in the PDB and in 11/14 cases, database support lowered the r.m.s.d. of the family of 20 structural models. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10858-016-0036-y) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4921114 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-49211142016-07-12 The AUDANA algorithm for automated protein 3D structure determination from NMR NOE data Lee, Woonghee Petit, Chad M. Cornilescu, Gabriel Stark, Jaime L. Markley, John L. J Biomol NMR Communication We introduce AUDANA (Automated Database-Assisted NOE Assignment), an algorithm for determining three-dimensional structures of proteins from NMR data that automates the assignment of 3D-NOE spectra, generates distance constraints, and conducts iterative high temperature molecular dynamics and simulated annealing. The protein sequence, chemical shift assignments, and NOE spectra are the only required inputs. Distance constraints generated automatically from ambiguously assigned NOE peaks are validated during the structure calculation against information from an enlarged version of the freely available PACSY database that incorporates information on protein structures deposited in the Protein Data Bank (PDB). This approach yields robust sets of distance constraints and 3D structures. We evaluated the performance of AUDANA with input data for 14 proteins ranging in size from 6 to 25 kDa that had 27–98 % sequence identity to proteins in the database. In all cases, the automatically calculated 3D structures passed stringent validation tests. Structures were determined with and without database support. In 9/14 cases, database support improved the agreement with manually determined structures in the PDB and in 11/14 cases, database support lowered the r.m.s.d. of the family of 20 structural models. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10858-016-0036-y) contains supplementary material, which is available to authorized users. Springer Netherlands 2016-05-12 2016 /pmc/articles/PMC4921114/ /pubmed/27169728 http://dx.doi.org/10.1007/s10858-016-0036-y Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Communication Lee, Woonghee Petit, Chad M. Cornilescu, Gabriel Stark, Jaime L. Markley, John L. The AUDANA algorithm for automated protein 3D structure determination from NMR NOE data |
title | The AUDANA algorithm for automated protein 3D structure determination from NMR NOE data |
title_full | The AUDANA algorithm for automated protein 3D structure determination from NMR NOE data |
title_fullStr | The AUDANA algorithm for automated protein 3D structure determination from NMR NOE data |
title_full_unstemmed | The AUDANA algorithm for automated protein 3D structure determination from NMR NOE data |
title_short | The AUDANA algorithm for automated protein 3D structure determination from NMR NOE data |
title_sort | audana algorithm for automated protein 3d structure determination from nmr noe data |
topic | Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4921114/ https://www.ncbi.nlm.nih.gov/pubmed/27169728 http://dx.doi.org/10.1007/s10858-016-0036-y |
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