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Amino acid composition predicts prion activity
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Minhas, Fayyaz ul Amir Afsar, Ross, Eric D. and Ben-Hur, Asa (2017) Amino acid composition predicts prion activity. PLoS Computational Biology, 13 (4). e1005465. doi:10.1371/journal.pcbi.1005465 ISSN 1553-7358.
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Official URL: http://dx.doi.org/10.1371/journal.pcbi.1005465
Abstract
Many prion-forming proteins contain glutamine/asparagine (Q/N) rich domains, and there are conflicting opinions as to the role of primary sequence in their conversion to the prion form: is this phenomenon driven primarily by amino acid composition, or, as a recent computational analysis suggested, dependent on the presence of short sequence elements with high amyloid-forming potential. The argument for the importance of short sequence elements hinged on the relatively-high accuracy obtained using a method that utilizes a collection of length-six sequence elements with known amyloid-forming potential. We weigh in on this question and demonstrate that when those sequence elements are permuted, even higher accuracy is obtained; we also propose a novel multiple-instance machine learning method that uses sequence composition alone, and achieves better accuracy than all existing prion prediction approaches. While we expect there to be elements of primary sequence that affect the process, our experiments suggest that sequence composition alone is sufficient for predicting protein sequences that are likely to form prions. A web-server for the proposed method is available at http://faculty.pieas.edu.pk/fayyaz/prank.html, and the code for reproducing our experiments is available at http://doi.org/10.5281/zenodo.167136.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QD Chemistry Q Science > QP Physiology |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Amino acids, Amino acids -- Composition, Amino acids -- Composition -- Computer simulation | ||||||
Journal or Publication Title: | PLoS Computational Biology | ||||||
Publisher: | Public Library of Science | ||||||
ISSN: | 1553-7358 | ||||||
Official Date: | 10 April 2017 | ||||||
Dates: |
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Volume: | 13 | ||||||
Number: | 4 | ||||||
Article Number: | e1005465 | ||||||
DOI: | 10.1371/journal.pcbi.1005465 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 5 November 2019 | ||||||
Date of first compliant Open Access: | 6 November 2019 | ||||||
RIOXX Funder/Project Grant: |
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