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A method for probing the mutational landscape of amyloid structure
Motivation: Proteins of all kinds can self-assemble into highly ordered β-sheet aggregates known as amyloid fibrils, important both biologically and clinically. However, the specific molecular structure of a fibril can vary dramatically depending on sequence and environmental conditions, and mutatio...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117379/ https://www.ncbi.nlm.nih.gov/pubmed/21685090 http://dx.doi.org/10.1093/bioinformatics/btr238 |
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author | O'Donnell, Charles W. Waldispühl, Jérôme Lis, Mieszko Halfmann, Randal Devadas, Srinivas Lindquist, Susan Berger, Bonnie |
author_facet | O'Donnell, Charles W. Waldispühl, Jérôme Lis, Mieszko Halfmann, Randal Devadas, Srinivas Lindquist, Susan Berger, Bonnie |
author_sort | O'Donnell, Charles W. |
collection | PubMed |
description | Motivation: Proteins of all kinds can self-assemble into highly ordered β-sheet aggregates known as amyloid fibrils, important both biologically and clinically. However, the specific molecular structure of a fibril can vary dramatically depending on sequence and environmental conditions, and mutations can drastically alter amyloid function and pathogenicity. Experimental structure determination has proven extremely difficult with only a handful of NMR-based models proposed, suggesting a need for computational methods. Results: We present AmyloidMutants, a statistical mechanics approach for de novo prediction and analysis of wild-type and mutant amyloid structures. Based on the premise of protein mutational landscapes, AmyloidMutants energetically quantifies the effects of sequence mutation on fibril conformation and stability. Tested on non-mutant, full-length amyloid structures with known chemical shift data, AmyloidMutants offers roughly 2-fold improvement in prediction accuracy over existing tools. Moreover, AmyloidMutants is the only method to predict complete super-secondary structures, enabling accurate discrimination of topologically dissimilar amyloid conformations that correspond to the same sequence locations. Applied to mutant prediction, AmyloidMutants identifies a global conformational switch between Aβ and its highly-toxic ‘Iowa’ mutant in agreement with a recent experimental model based on partial chemical shift data. Predictions on mutant, yeast-toxic strains of HET-s suggest similar alternate folds. When applied to HET-s and a HET-s mutant with core asparagines replaced by glutamines (both highly amyloidogenic chemically similar residues abundant in many amyloids), AmyloidMutants surprisingly predicts a greatly reduced capacity of the glutamine mutant to form amyloid. We confirm this finding by conducting mutagenesis experiments. Availability: Our tool is publically available on the web at http://amyloid.csail.mit.edu/. Contact: lindquist_admin@wi.mit.edu; bab@csail.mit.edu Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-3117379 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-31173792011-06-17 A method for probing the mutational landscape of amyloid structure O'Donnell, Charles W. Waldispühl, Jérôme Lis, Mieszko Halfmann, Randal Devadas, Srinivas Lindquist, Susan Berger, Bonnie Bioinformatics Ismb/Eccb 2011 Proceedings Papers Committee July 17 to July 19, 2011, Vienna, Austria Motivation: Proteins of all kinds can self-assemble into highly ordered β-sheet aggregates known as amyloid fibrils, important both biologically and clinically. However, the specific molecular structure of a fibril can vary dramatically depending on sequence and environmental conditions, and mutations can drastically alter amyloid function and pathogenicity. Experimental structure determination has proven extremely difficult with only a handful of NMR-based models proposed, suggesting a need for computational methods. Results: We present AmyloidMutants, a statistical mechanics approach for de novo prediction and analysis of wild-type and mutant amyloid structures. Based on the premise of protein mutational landscapes, AmyloidMutants energetically quantifies the effects of sequence mutation on fibril conformation and stability. Tested on non-mutant, full-length amyloid structures with known chemical shift data, AmyloidMutants offers roughly 2-fold improvement in prediction accuracy over existing tools. Moreover, AmyloidMutants is the only method to predict complete super-secondary structures, enabling accurate discrimination of topologically dissimilar amyloid conformations that correspond to the same sequence locations. Applied to mutant prediction, AmyloidMutants identifies a global conformational switch between Aβ and its highly-toxic ‘Iowa’ mutant in agreement with a recent experimental model based on partial chemical shift data. Predictions on mutant, yeast-toxic strains of HET-s suggest similar alternate folds. When applied to HET-s and a HET-s mutant with core asparagines replaced by glutamines (both highly amyloidogenic chemically similar residues abundant in many amyloids), AmyloidMutants surprisingly predicts a greatly reduced capacity of the glutamine mutant to form amyloid. We confirm this finding by conducting mutagenesis experiments. Availability: Our tool is publically available on the web at http://amyloid.csail.mit.edu/. Contact: lindquist_admin@wi.mit.edu; bab@csail.mit.edu Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2011-07-01 2011-06-14 /pmc/articles/PMC3117379/ /pubmed/21685090 http://dx.doi.org/10.1093/bioinformatics/btr238 Text en © The Author(s) 2011. 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 | Ismb/Eccb 2011 Proceedings Papers Committee July 17 to July 19, 2011, Vienna, Austria O'Donnell, Charles W. Waldispühl, Jérôme Lis, Mieszko Halfmann, Randal Devadas, Srinivas Lindquist, Susan Berger, Bonnie A method for probing the mutational landscape of amyloid structure |
title | A method for probing the mutational landscape of amyloid structure |
title_full | A method for probing the mutational landscape of amyloid structure |
title_fullStr | A method for probing the mutational landscape of amyloid structure |
title_full_unstemmed | A method for probing the mutational landscape of amyloid structure |
title_short | A method for probing the mutational landscape of amyloid structure |
title_sort | method for probing the mutational landscape of amyloid structure |
topic | Ismb/Eccb 2011 Proceedings Papers Committee July 17 to July 19, 2011, Vienna, Austria |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117379/ https://www.ncbi.nlm.nih.gov/pubmed/21685090 http://dx.doi.org/10.1093/bioinformatics/btr238 |
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