Computational models for the prediction of polypeptide aggregation propensity

TitleComputational models for the prediction of polypeptide aggregation propensity
Publication TypeJournal Article
Year of Publication2006
AuthorsCaflisch A.
JournalCurrent Opinion in Chemical Biology
Volume10
Issue5
Pagination437-444
Date Published2006 Oct
Type of ArticleReview Article
ISSN1367-5931
KeywordsChemistry, Physical, Computer Simulation, Models, Chemical, Peptides, Physicochemical Phenomena, Proteins
Abstract

In amyloid fibrils, β-strand conformations of polypeptide chains, or segments thereof, are perpendicular to the fibril axis, but knowledge of their three dimensional structure at atomic level of detail is scarce. Two types of computational approaches have been developed recently for investigating the aggregation propensity of peptides and proteins and identifying the segments most prone to form fibrils (hot spots). The physicochemical properties of the natural amino acids (e.g. β-propensity, hydrophobicity, aromatic content and charge) have been used to derive phenomenological models able to predict changes in aggregation rate upon mutation, as well as absolute rates and hot spots. Applications of these models to entire proteomes have provided evidence that intrinsically disordered proteins are less amyloidogenic than globular proteins. In the second type of approach, amyloidogenic polypeptides have been decomposed into overlapping segments, and atomistic simulations of three or more copies of each segment have been performed to obtain insights into aggregation propensity and structural details of the ordered aggregates (e.g. turn regions).

DOI10.1016/j.cbpa.2006.07.009
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Alternate JournalCurr. Opin. Chem. Biol.
PubMed ID16880001
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