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Recent Advances in Computational Methods for Nuclear Magnetic Resonance Data Processing

Although three-dimensional protein structure determination using nuclear magnetic resonance (NMR) spectroscopy is a computationally costly and tedious process that would benefit from advanced computational techniques, it has not garnered much research attention from specialists in bioinformatics and...

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Detalles Bibliográficos
Autor principal: Gao, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4357661/
https://www.ncbi.nlm.nih.gov/pubmed/23453016
http://dx.doi.org/10.1016/j.gpb.2012.12.003
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author Gao, Xin
author_facet Gao, Xin
author_sort Gao, Xin
collection PubMed
description Although three-dimensional protein structure determination using nuclear magnetic resonance (NMR) spectroscopy is a computationally costly and tedious process that would benefit from advanced computational techniques, it has not garnered much research attention from specialists in bioinformatics and computational biology. In this paper, we review recent advances in computational methods for NMR protein structure determination. We summarize the advantages of and bottlenecks in the existing methods and outline some open problems in the field. We also discuss current trends in NMR technology development and suggest directions for research on future computational methods for NMR.
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spelling pubmed-43576612015-05-06 Recent Advances in Computational Methods for Nuclear Magnetic Resonance Data Processing Gao, Xin Genomics Proteomics Bioinformatics Review Although three-dimensional protein structure determination using nuclear magnetic resonance (NMR) spectroscopy is a computationally costly and tedious process that would benefit from advanced computational techniques, it has not garnered much research attention from specialists in bioinformatics and computational biology. In this paper, we review recent advances in computational methods for NMR protein structure determination. We summarize the advantages of and bottlenecks in the existing methods and outline some open problems in the field. We also discuss current trends in NMR technology development and suggest directions for research on future computational methods for NMR. Elsevier 2013-02 2013-01-11 /pmc/articles/PMC4357661/ /pubmed/23453016 http://dx.doi.org/10.1016/j.gpb.2012.12.003 Text en © 2013 Beijing Institute of Genomics, Chinese Academy of Sciences and Genetics Society of China. Published by Elsevier Ltd and Science Press. All rights reserved. http://creativecommons.org/licenses/by-nc-sa/3.0/ This is an open access article under the CC BY-NC-SA license (http://creativecommons.org/licenses/by-nc-sa/3.0/).
spellingShingle Review
Gao, Xin
Recent Advances in Computational Methods for Nuclear Magnetic Resonance Data Processing
title Recent Advances in Computational Methods for Nuclear Magnetic Resonance Data Processing
title_full Recent Advances in Computational Methods for Nuclear Magnetic Resonance Data Processing
title_fullStr Recent Advances in Computational Methods for Nuclear Magnetic Resonance Data Processing
title_full_unstemmed Recent Advances in Computational Methods for Nuclear Magnetic Resonance Data Processing
title_short Recent Advances in Computational Methods for Nuclear Magnetic Resonance Data Processing
title_sort recent advances in computational methods for nuclear magnetic resonance data processing
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4357661/
https://www.ncbi.nlm.nih.gov/pubmed/23453016
http://dx.doi.org/10.1016/j.gpb.2012.12.003
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