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A fragment based method for modeling of protein segments into cryo-EM density maps

BACKGROUND: Single-particle analysis of electron cryo-microscopy (cryo-EM) is a key technology for elucidation of macromolecular structures. Recent technical advances in hardware and software developments significantly enhanced the resolution of cryo-EM density maps and broadened the applicability a...

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Autores principales: Ismer, Jochen, Rose, Alexander S., Tiemann, Johanna K. S., Hildebrand, Peter W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5683378/
https://www.ncbi.nlm.nih.gov/pubmed/29132296
http://dx.doi.org/10.1186/s12859-017-1904-5
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author Ismer, Jochen
Rose, Alexander S.
Tiemann, Johanna K. S.
Hildebrand, Peter W.
author_facet Ismer, Jochen
Rose, Alexander S.
Tiemann, Johanna K. S.
Hildebrand, Peter W.
author_sort Ismer, Jochen
collection PubMed
description BACKGROUND: Single-particle analysis of electron cryo-microscopy (cryo-EM) is a key technology for elucidation of macromolecular structures. Recent technical advances in hardware and software developments significantly enhanced the resolution of cryo-EM density maps and broadened the applicability and the circle of users. To facilitate modeling of macromolecules into cryo-EM density maps, fast and easy to use methods for modeling are now demanded. RESULTS: Here we investigated and benchmarked the suitability of a classical and well established fragment-based approach for modeling of segments into cryo-EM density maps (termed FragFit). FragFit uses a hierarchical strategy to select fragments from a pre-calculated set of billions of fragments derived from structures deposited in the Protein Data Bank, based on sequence similarly, fit of stem atoms and fit to a cryo-EM density map. The user only has to specify the sequence of the segment and the number of the N- and C-terminal stem-residues in the protein. Using a representative data set of protein structures, we show that protein segments can be accurately modeled into cryo-EM density maps of different resolution by FragFit. Prediction quality depends on segment length, the type of secondary structure of the segment and local quality of the map. CONCLUSION: Fast and automated calculation of FragFit renders it applicable for implementation of interactive web-applications e.g. to model missing segments, flexible protein parts or hinge-regions into cryo-EM density maps. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-017-1904-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-56833782017-11-20 A fragment based method for modeling of protein segments into cryo-EM density maps Ismer, Jochen Rose, Alexander S. Tiemann, Johanna K. S. Hildebrand, Peter W. BMC Bioinformatics Methodology Article BACKGROUND: Single-particle analysis of electron cryo-microscopy (cryo-EM) is a key technology for elucidation of macromolecular structures. Recent technical advances in hardware and software developments significantly enhanced the resolution of cryo-EM density maps and broadened the applicability and the circle of users. To facilitate modeling of macromolecules into cryo-EM density maps, fast and easy to use methods for modeling are now demanded. RESULTS: Here we investigated and benchmarked the suitability of a classical and well established fragment-based approach for modeling of segments into cryo-EM density maps (termed FragFit). FragFit uses a hierarchical strategy to select fragments from a pre-calculated set of billions of fragments derived from structures deposited in the Protein Data Bank, based on sequence similarly, fit of stem atoms and fit to a cryo-EM density map. The user only has to specify the sequence of the segment and the number of the N- and C-terminal stem-residues in the protein. Using a representative data set of protein structures, we show that protein segments can be accurately modeled into cryo-EM density maps of different resolution by FragFit. Prediction quality depends on segment length, the type of secondary structure of the segment and local quality of the map. CONCLUSION: Fast and automated calculation of FragFit renders it applicable for implementation of interactive web-applications e.g. to model missing segments, flexible protein parts or hinge-regions into cryo-EM density maps. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-017-1904-5) contains supplementary material, which is available to authorized users. BioMed Central 2017-11-13 /pmc/articles/PMC5683378/ /pubmed/29132296 http://dx.doi.org/10.1186/s12859-017-1904-5 Text en © The Author(s). 2017 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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Ismer, Jochen
Rose, Alexander S.
Tiemann, Johanna K. S.
Hildebrand, Peter W.
A fragment based method for modeling of protein segments into cryo-EM density maps
title A fragment based method for modeling of protein segments into cryo-EM density maps
title_full A fragment based method for modeling of protein segments into cryo-EM density maps
title_fullStr A fragment based method for modeling of protein segments into cryo-EM density maps
title_full_unstemmed A fragment based method for modeling of protein segments into cryo-EM density maps
title_short A fragment based method for modeling of protein segments into cryo-EM density maps
title_sort fragment based method for modeling of protein segments into cryo-em density maps
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5683378/
https://www.ncbi.nlm.nih.gov/pubmed/29132296
http://dx.doi.org/10.1186/s12859-017-1904-5
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