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Blind testing of cross‐linking/mass spectrometry hybrid methods in CASP11

Hybrid approaches combine computational methods with experimental data. The information contained in the experimental data can be leveraged to probe the structure of proteins otherwise elusive to computational methods. Compared with computational methods, the structures produced by hybrid methods ex...

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Detalles Bibliográficos
Autores principales: Schneider, Michael, Belsom, Adam, Rappsilber, Juri, Brock, Oliver
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/PMC5042049/
https://www.ncbi.nlm.nih.gov/pubmed/26945814
http://dx.doi.org/10.1002/prot.25028
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author Schneider, Michael
Belsom, Adam
Rappsilber, Juri
Brock, Oliver
author_facet Schneider, Michael
Belsom, Adam
Rappsilber, Juri
Brock, Oliver
author_sort Schneider, Michael
collection PubMed
description Hybrid approaches combine computational methods with experimental data. The information contained in the experimental data can be leveraged to probe the structure of proteins otherwise elusive to computational methods. Compared with computational methods, the structures produced by hybrid methods exhibit some degree of experimental validation. In spite of these advantages, most hybrid methods have not yet been validated in blind tests, hampering their development. Here, we describe the first blind test of a specific cross‐link based hybrid method in CASP. This blind test was coordinated by the CASP organizers and utilized a novel, high‐density cross‐linking/mass‐spectrometry (CLMS) approach that is able to collect high‐density CLMS data in a matter of days. This experimental protocol was developed in the Rappsilber laboratory. This approach exploits the chemistry of a highly reactive, photoactivatable cross‐linker to produce an order of magnitude more cross‐links than homobifunctional cross‐linkers. The Rappsilber laboratory generated experimental CLMS data based on this protocol, submitted the data to the CASP organizers which then released this data to the CASP11 prediction groups in a separate, CLMS assisted modeling experiment. We did not observe a clear improvement of assisted models, presumably because the properties of the CLMS data—uncertainty in cross‐link identification and residue‐residue assignment, and uneven distribution over the protein—were largely unknown to the prediction groups and their approaches were not yet tailored to this kind of data. We also suggest modifications to the CLMS‐CASP experiment and discuss the importance of rigorous blind testing in the development of hybrid methods. Proteins 2016; 84(Suppl 1):152–163. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.
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spelling pubmed-50420492016-10-03 Blind testing of cross‐linking/mass spectrometry hybrid methods in CASP11 Schneider, Michael Belsom, Adam Rappsilber, Juri Brock, Oliver Proteins Articles Hybrid approaches combine computational methods with experimental data. The information contained in the experimental data can be leveraged to probe the structure of proteins otherwise elusive to computational methods. Compared with computational methods, the structures produced by hybrid methods exhibit some degree of experimental validation. In spite of these advantages, most hybrid methods have not yet been validated in blind tests, hampering their development. Here, we describe the first blind test of a specific cross‐link based hybrid method in CASP. This blind test was coordinated by the CASP organizers and utilized a novel, high‐density cross‐linking/mass‐spectrometry (CLMS) approach that is able to collect high‐density CLMS data in a matter of days. This experimental protocol was developed in the Rappsilber laboratory. This approach exploits the chemistry of a highly reactive, photoactivatable cross‐linker to produce an order of magnitude more cross‐links than homobifunctional cross‐linkers. The Rappsilber laboratory generated experimental CLMS data based on this protocol, submitted the data to the CASP organizers which then released this data to the CASP11 prediction groups in a separate, CLMS assisted modeling experiment. We did not observe a clear improvement of assisted models, presumably because the properties of the CLMS data—uncertainty in cross‐link identification and residue‐residue assignment, and uneven distribution over the protein—were largely unknown to the prediction groups and their approaches were not yet tailored to this kind of data. We also suggest modifications to the CLMS‐CASP experiment and discuss the importance of rigorous blind testing in the development of hybrid methods. Proteins 2016; 84(Suppl 1):152–163. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc. John Wiley and Sons Inc. 2016-03-28 2016-09 /pmc/articles/PMC5042049/ /pubmed/26945814 http://dx.doi.org/10.1002/prot.25028 Text en © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc. 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 Articles
Schneider, Michael
Belsom, Adam
Rappsilber, Juri
Brock, Oliver
Blind testing of cross‐linking/mass spectrometry hybrid methods in CASP11
title Blind testing of cross‐linking/mass spectrometry hybrid methods in CASP11
title_full Blind testing of cross‐linking/mass spectrometry hybrid methods in CASP11
title_fullStr Blind testing of cross‐linking/mass spectrometry hybrid methods in CASP11
title_full_unstemmed Blind testing of cross‐linking/mass spectrometry hybrid methods in CASP11
title_short Blind testing of cross‐linking/mass spectrometry hybrid methods in CASP11
title_sort blind testing of cross‐linking/mass spectrometry hybrid methods in casp11
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5042049/
https://www.ncbi.nlm.nih.gov/pubmed/26945814
http://dx.doi.org/10.1002/prot.25028
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