MetAmyl: a METa-predictor for AMYLoid proteins.

The aggregation of proteins or peptides in amyloid fibrils is associated with a number of clinical disorders, including Alzheimer's, Huntington's and prion diseases, medullary thyroid cancer, renal and cardiac amyloidosis. Despite extensive studies, the molecular mechanisms underlying the...

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Autores principales:	Mathieu Emily, Anthony Talvas, Christian Delamarche
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/388ce322da9a4b94bb3b50dd27590e73
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spelling	oai:doaj.org-article:388ce322da9a4b94bb3b50dd27590e732021-11-18T08:45:40ZMetAmyl: a METa-predictor for AMYLoid proteins.1932-620310.1371/journal.pone.0079722https://doaj.org/article/388ce322da9a4b94bb3b50dd27590e732013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24260292/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203The aggregation of proteins or peptides in amyloid fibrils is associated with a number of clinical disorders, including Alzheimer's, Huntington's and prion diseases, medullary thyroid cancer, renal and cardiac amyloidosis. Despite extensive studies, the molecular mechanisms underlying the initiation of fibril formation remain largely unknown. Several lines of evidence revealed that short amino-acid segments (hot spots), located in amyloid precursor proteins act as seeds for fibril elongation. Therefore, hot spots are potential targets for diagnostic/therapeutic applications, and a current challenge in bioinformatics is the development of methods to accurately predict hot spots from protein sequences. In this paper, we combined existing methods into a meta-predictor for hot spots prediction, called MetAmyl for METapredictor for AMYLoid proteins. MetAmyl is based on a logistic regression model that aims at weighting predictions from a set of popular algorithms, statistically selected as being the most informative and complementary predictors. We evaluated the performances of MetAmyl through a large scale comparative study based on three independent datasets and thus demonstrated its ability to differentiate between amyloidogenic and non-amyloidogenic polypeptides. Compared to 9 other methods, MetAmyl provides significant improvement in prediction on studied datasets. We further show that MetAmyl is efficient to highlight the effect of point mutations involved in human amyloidosis, so we suggest this program should be a useful complementary tool for the diagnosis of these diseases.Mathieu EmilyAnthony TalvasChristian DelamarchePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 11, p e79722 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mathieu Emily Anthony Talvas Christian Delamarche MetAmyl: a METa-predictor for AMYLoid proteins.
description	The aggregation of proteins or peptides in amyloid fibrils is associated with a number of clinical disorders, including Alzheimer's, Huntington's and prion diseases, medullary thyroid cancer, renal and cardiac amyloidosis. Despite extensive studies, the molecular mechanisms underlying the initiation of fibril formation remain largely unknown. Several lines of evidence revealed that short amino-acid segments (hot spots), located in amyloid precursor proteins act as seeds for fibril elongation. Therefore, hot spots are potential targets for diagnostic/therapeutic applications, and a current challenge in bioinformatics is the development of methods to accurately predict hot spots from protein sequences. In this paper, we combined existing methods into a meta-predictor for hot spots prediction, called MetAmyl for METapredictor for AMYLoid proteins. MetAmyl is based on a logistic regression model that aims at weighting predictions from a set of popular algorithms, statistically selected as being the most informative and complementary predictors. We evaluated the performances of MetAmyl through a large scale comparative study based on three independent datasets and thus demonstrated its ability to differentiate between amyloidogenic and non-amyloidogenic polypeptides. Compared to 9 other methods, MetAmyl provides significant improvement in prediction on studied datasets. We further show that MetAmyl is efficient to highlight the effect of point mutations involved in human amyloidosis, so we suggest this program should be a useful complementary tool for the diagnosis of these diseases.
format	article
author	Mathieu Emily Anthony Talvas Christian Delamarche
author_facet	Mathieu Emily Anthony Talvas Christian Delamarche
author_sort	Mathieu Emily
title	MetAmyl: a METa-predictor for AMYLoid proteins.
title_short	MetAmyl: a METa-predictor for AMYLoid proteins.
title_full	MetAmyl: a METa-predictor for AMYLoid proteins.
title_fullStr	MetAmyl: a METa-predictor for AMYLoid proteins.
title_full_unstemmed	MetAmyl: a METa-predictor for AMYLoid proteins.
title_sort	metamyl: a meta-predictor for amyloid proteins.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/388ce322da9a4b94bb3b50dd27590e73
work_keys_str_mv	AT mathieuemily metamylametapredictorforamyloidproteins AT anthonytalvas metamylametapredictorforamyloidproteins AT christiandelamarche metamylametapredictorforamyloidproteins
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MetAmyl: a METa-predictor for AMYLoid proteins.

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