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I-TASSER-MR: automated molecular replacement for distant-homology proteins using iterative fragment assembly and progressive sequence truncation
Molecular replacement (MR) is one of the most common techniques used for solving the phase problem in X-ray crystal diffraction. The success rate of MR however drops quickly when the sequence identity between query and templates is reduced, while the I-TASSER-MR server is designed to solve the phase...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5793832/ https://www.ncbi.nlm.nih.gov/pubmed/28472524 http://dx.doi.org/10.1093/nar/gkx349 |
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author | Wang, Yan Virtanen, Jouko Xue, Zhidong Zhang, Yang |
author_facet | Wang, Yan Virtanen, Jouko Xue, Zhidong Zhang, Yang |
author_sort | Wang, Yan |
collection | PubMed |
description | Molecular replacement (MR) is one of the most common techniques used for solving the phase problem in X-ray crystal diffraction. The success rate of MR however drops quickly when the sequence identity between query and templates is reduced, while the I-TASSER-MR server is designed to solve the phase problem for proteins that lack close homologous templates. Starting from a sequence, it first generates full-length models using I-TASSER by iterative structural fragment reassembly. A progressive sequence truncation procedure is then used for editing the models based on local variations of the structural assembly simulations. Next, the edited models are submitted to MR-REX to search for optimal placements in the crystal unit-cells through replica-exchange Monte Carlo simulations, with the phasing results used by CNS for final atomic model refinement and selection. The I-TASSER-MR algorithm was tested in large-scale benchmark datasets and solved 36% more targets compared to using the best threading templates. The server takes primary sequence and raw crystal diffraction data as input, with output containing annotated phase information and refined structure models. It also allows users to choose between different methods for setting B-factors and the number of models used for phasing. The online server is freely available at http://zhanglab.ccmb.med.umich.edu/I-TASSER-MR. |
format | Online Article Text |
id | pubmed-5793832 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-57938322018-02-06 I-TASSER-MR: automated molecular replacement for distant-homology proteins using iterative fragment assembly and progressive sequence truncation Wang, Yan Virtanen, Jouko Xue, Zhidong Zhang, Yang Nucleic Acids Res Web Server Issue Molecular replacement (MR) is one of the most common techniques used for solving the phase problem in X-ray crystal diffraction. The success rate of MR however drops quickly when the sequence identity between query and templates is reduced, while the I-TASSER-MR server is designed to solve the phase problem for proteins that lack close homologous templates. Starting from a sequence, it first generates full-length models using I-TASSER by iterative structural fragment reassembly. A progressive sequence truncation procedure is then used for editing the models based on local variations of the structural assembly simulations. Next, the edited models are submitted to MR-REX to search for optimal placements in the crystal unit-cells through replica-exchange Monte Carlo simulations, with the phasing results used by CNS for final atomic model refinement and selection. The I-TASSER-MR algorithm was tested in large-scale benchmark datasets and solved 36% more targets compared to using the best threading templates. The server takes primary sequence and raw crystal diffraction data as input, with output containing annotated phase information and refined structure models. It also allows users to choose between different methods for setting B-factors and the number of models used for phasing. The online server is freely available at http://zhanglab.ccmb.med.umich.edu/I-TASSER-MR. Oxford University Press 2017-07-03 2017-05-02 /pmc/articles/PMC5793832/ /pubmed/28472524 http://dx.doi.org/10.1093/nar/gkx349 Text en © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Web Server Issue Wang, Yan Virtanen, Jouko Xue, Zhidong Zhang, Yang I-TASSER-MR: automated molecular replacement for distant-homology proteins using iterative fragment assembly and progressive sequence truncation |
title | I-TASSER-MR: automated molecular replacement for distant-homology proteins using iterative fragment assembly and progressive sequence truncation |
title_full | I-TASSER-MR: automated molecular replacement for distant-homology proteins using iterative fragment assembly and progressive sequence truncation |
title_fullStr | I-TASSER-MR: automated molecular replacement for distant-homology proteins using iterative fragment assembly and progressive sequence truncation |
title_full_unstemmed | I-TASSER-MR: automated molecular replacement for distant-homology proteins using iterative fragment assembly and progressive sequence truncation |
title_short | I-TASSER-MR: automated molecular replacement for distant-homology proteins using iterative fragment assembly and progressive sequence truncation |
title_sort | i-tasser-mr: automated molecular replacement for distant-homology proteins using iterative fragment assembly and progressive sequence truncation |
topic | Web Server Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5793832/ https://www.ncbi.nlm.nih.gov/pubmed/28472524 http://dx.doi.org/10.1093/nar/gkx349 |
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