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Capturing, sharing and analysing biophysical data from protein engineering and protein characterization studies

Large amounts of data are being generated annually on the connection between the sequence, structure and function of proteins using site-directed mutagenesis, protein design and directed evolution techniques. These data provide the fundamental building blocks for our understanding of protein functio...

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Detalles Bibliográficos
Autores principales: Farrell, Damien, O’Meara, Fergal, Johnston, Michael, Bradley, John, Søndergaard, Chresten R., Georgi, Nikolaj, Webb, Helen, Tynan-Connolly, Barbara Mary, Bjarnadottir, Una, Carstensen, Tommy, Nielsen, Jens Erik
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2978379/
https://www.ncbi.nlm.nih.gov/pubmed/20724439
http://dx.doi.org/10.1093/nar/gkq726
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author Farrell, Damien
O’Meara, Fergal
Johnston, Michael
Bradley, John
Søndergaard, Chresten R.
Georgi, Nikolaj
Webb, Helen
Tynan-Connolly, Barbara Mary
Bjarnadottir, Una
Carstensen, Tommy
Nielsen, Jens Erik
author_facet Farrell, Damien
O’Meara, Fergal
Johnston, Michael
Bradley, John
Søndergaard, Chresten R.
Georgi, Nikolaj
Webb, Helen
Tynan-Connolly, Barbara Mary
Bjarnadottir, Una
Carstensen, Tommy
Nielsen, Jens Erik
author_sort Farrell, Damien
collection PubMed
description Large amounts of data are being generated annually on the connection between the sequence, structure and function of proteins using site-directed mutagenesis, protein design and directed evolution techniques. These data provide the fundamental building blocks for our understanding of protein function, molecular biology and living organisms in general. However, much experimental data are never deposited in databases and is thus ‘lost’ in journal publications or in PhD theses. At the same time theoretical scientists are in need of large amounts of experimental data for benchmarking and calibrating novel predictive algorithms, and theoretical progress is therefore often hampered by the lack of suitable data to validate or disprove a theoretical assumption. We present PEAT (Protein Engineering Analysis Tool), an application that integrates data deposition, storage and analysis for researchers carrying out protein engineering projects or biophysical characterization of proteins. PEAT contains modules for DNA sequence manipulation, primer design, fitting of biophysical characterization data (enzyme kinetics, circular dichroism spectroscopy, NMR titration data, etc.), and facilitates sharing of experimental data and analyses for a typical university-based research group. PEAT is freely available to academic researchers at http://enzyme.ucd.ie/PEAT.
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spelling pubmed-29783792010-11-12 Capturing, sharing and analysing biophysical data from protein engineering and protein characterization studies Farrell, Damien O’Meara, Fergal Johnston, Michael Bradley, John Søndergaard, Chresten R. Georgi, Nikolaj Webb, Helen Tynan-Connolly, Barbara Mary Bjarnadottir, Una Carstensen, Tommy Nielsen, Jens Erik Nucleic Acids Res Methods Online Large amounts of data are being generated annually on the connection between the sequence, structure and function of proteins using site-directed mutagenesis, protein design and directed evolution techniques. These data provide the fundamental building blocks for our understanding of protein function, molecular biology and living organisms in general. However, much experimental data are never deposited in databases and is thus ‘lost’ in journal publications or in PhD theses. At the same time theoretical scientists are in need of large amounts of experimental data for benchmarking and calibrating novel predictive algorithms, and theoretical progress is therefore often hampered by the lack of suitable data to validate or disprove a theoretical assumption. We present PEAT (Protein Engineering Analysis Tool), an application that integrates data deposition, storage and analysis for researchers carrying out protein engineering projects or biophysical characterization of proteins. PEAT contains modules for DNA sequence manipulation, primer design, fitting of biophysical characterization data (enzyme kinetics, circular dichroism spectroscopy, NMR titration data, etc.), and facilitates sharing of experimental data and analyses for a typical university-based research group. PEAT is freely available to academic researchers at http://enzyme.ucd.ie/PEAT. Oxford University Press 2010-11 2010-08-19 /pmc/articles/PMC2978379/ /pubmed/20724439 http://dx.doi.org/10.1093/nar/gkq726 Text en © The Author(s) 2010. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.5 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methods Online
Farrell, Damien
O’Meara, Fergal
Johnston, Michael
Bradley, John
Søndergaard, Chresten R.
Georgi, Nikolaj
Webb, Helen
Tynan-Connolly, Barbara Mary
Bjarnadottir, Una
Carstensen, Tommy
Nielsen, Jens Erik
Capturing, sharing and analysing biophysical data from protein engineering and protein characterization studies
title Capturing, sharing and analysing biophysical data from protein engineering and protein characterization studies
title_full Capturing, sharing and analysing biophysical data from protein engineering and protein characterization studies
title_fullStr Capturing, sharing and analysing biophysical data from protein engineering and protein characterization studies
title_full_unstemmed Capturing, sharing and analysing biophysical data from protein engineering and protein characterization studies
title_short Capturing, sharing and analysing biophysical data from protein engineering and protein characterization studies
title_sort capturing, sharing and analysing biophysical data from protein engineering and protein characterization studies
topic Methods Online
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2978379/
https://www.ncbi.nlm.nih.gov/pubmed/20724439
http://dx.doi.org/10.1093/nar/gkq726
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