Computational design of new Peptide inhibitors for amyloid Beta (aβ) aggregation in Alzheimer's disease: application of a novel methodology.

Alzheimer's disease is the most common form of dementia. It is a neurodegenerative and incurable disease that is associated with the tight packing of amyloid fibrils. This packing is facilitated by the compatibility of the ridges and grooves on the amyloid surface. The GxMxG motif is the major...

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Autores principales:	Gözde Eskici, Mert Gur
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/66201b7b33f74fcf9dc37cb4952362a3
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spelling	oai:doaj.org-article:66201b7b33f74fcf9dc37cb4952362a32021-11-18T07:42:42ZComputational design of new Peptide inhibitors for amyloid Beta (aβ) aggregation in Alzheimer's disease: application of a novel methodology.1932-620310.1371/journal.pone.0066178https://doaj.org/article/66201b7b33f74fcf9dc37cb4952362a32013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23762479/?tool=EBIhttps://doaj.org/toc/1932-6203Alzheimer's disease is the most common form of dementia. It is a neurodegenerative and incurable disease that is associated with the tight packing of amyloid fibrils. This packing is facilitated by the compatibility of the ridges and grooves on the amyloid surface. The GxMxG motif is the major factor creating the compatibility between two amyloid surfaces, making it an important target for the design of amyloid aggregation inhibitors. In this study, a peptide, experimentally proven to bind Aβ40 fibrils at the GxMxG motif, was mutated by a novel methodology that systematically replaces amino acids with residues that share similar chemical characteristics and subsequently assesses the energetic favorability of these mutations by docking. Successive mutations are combined and reassessed via docking to a desired level of refinement. This methodology is both fast and efficient in providing potential inhibitors. Its efficiency lies in the fact that it does not perform all possible combinations of mutations, therefore decreasing the computational time drastically. The binding free energies of the experimentally studied reference peptide and its three top scoring derivatives were evaluated as a final assessment/valuation. The potential of mean forces (PMFs) were calculated by applying the Jarzynski's equality to results of steered molecular dynamics simulations. For all of the top scoring derivatives, the PMFs showed higher binding free energies than the reference peptide substantiating the usage of the introduced methodology to drug design.Gözde EskiciMert GurPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 6, p e66178 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gözde Eskici Mert Gur Computational design of new Peptide inhibitors for amyloid Beta (aβ) aggregation in Alzheimer's disease: application of a novel methodology.
description	Alzheimer's disease is the most common form of dementia. It is a neurodegenerative and incurable disease that is associated with the tight packing of amyloid fibrils. This packing is facilitated by the compatibility of the ridges and grooves on the amyloid surface. The GxMxG motif is the major factor creating the compatibility between two amyloid surfaces, making it an important target for the design of amyloid aggregation inhibitors. In this study, a peptide, experimentally proven to bind Aβ40 fibrils at the GxMxG motif, was mutated by a novel methodology that systematically replaces amino acids with residues that share similar chemical characteristics and subsequently assesses the energetic favorability of these mutations by docking. Successive mutations are combined and reassessed via docking to a desired level of refinement. This methodology is both fast and efficient in providing potential inhibitors. Its efficiency lies in the fact that it does not perform all possible combinations of mutations, therefore decreasing the computational time drastically. The binding free energies of the experimentally studied reference peptide and its three top scoring derivatives were evaluated as a final assessment/valuation. The potential of mean forces (PMFs) were calculated by applying the Jarzynski's equality to results of steered molecular dynamics simulations. For all of the top scoring derivatives, the PMFs showed higher binding free energies than the reference peptide substantiating the usage of the introduced methodology to drug design.
format	article
author	Gözde Eskici Mert Gur
author_facet	Gözde Eskici Mert Gur
author_sort	Gözde Eskici
title	Computational design of new Peptide inhibitors for amyloid Beta (aβ) aggregation in Alzheimer's disease: application of a novel methodology.
title_short	Computational design of new Peptide inhibitors for amyloid Beta (aβ) aggregation in Alzheimer's disease: application of a novel methodology.
title_full	Computational design of new Peptide inhibitors for amyloid Beta (aβ) aggregation in Alzheimer's disease: application of a novel methodology.
title_fullStr	Computational design of new Peptide inhibitors for amyloid Beta (aβ) aggregation in Alzheimer's disease: application of a novel methodology.
title_full_unstemmed	Computational design of new Peptide inhibitors for amyloid Beta (aβ) aggregation in Alzheimer's disease: application of a novel methodology.
title_sort	computational design of new peptide inhibitors for amyloid beta (aβ) aggregation in alzheimer's disease: application of a novel methodology.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/66201b7b33f74fcf9dc37cb4952362a3
work_keys_str_mv	AT gozdeeskici computationaldesignofnewpeptideinhibitorsforamyloidbetaabaggregationinalzheimersdiseaseapplicationofanovelmethodology AT mertgur computationaldesignofnewpeptideinhibitorsforamyloidbetaabaggregationinalzheimersdiseaseapplicationofanovelmethodology
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Computational design of new Peptide inhibitors for amyloid Beta (aβ) aggregation in Alzheimer's disease: application of a novel methodology.

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