Prediction of Peptide and Protein Propensity for Amyloid Formation
Famlia, C, Dennison, SR, Quintas, A and Phoenix, DA (2015). Prediction of Peptide and Protein Propensity for Amyloid Formation. PLoS ONE. 10.
|Authors||Famlia, C, Dennison, SR, Quintas, A and Phoenix, DA|
Understanding which peptides and proteins have the potential to undergo amyloid formation and what driving forces are responsible for amyloid-like fiber formation and stabilization remains limited. This is mainly because proteins that can undergo structural changes, which lead to amyloid formation, are quite diverse and share no obvious sequence or structural homology, despite the structural similarity found in the fibrils. To address these issues, a novel approach based on recursive feature selection and feed-forward neural networks was undertaken to identify key features highly correlated with the self-assembly problem. This approach allowed the identification of seven physicochemical and biochemical properties of the amino acids highly associated with the self-assembly of peptides and proteins into amyloid-like fibrils (normalized frequency of beta-sheet, normalized frequency of beta-sheet from LG, weights for beta-sheet at the window position of 1, isoelectric point, atom-based hydrophobic moment, helix termination parameter at position j+1 and Delta G degrees values for peptides extrapolated in 0 M urea). Moreover, these features enabled the development of a new predictor (available at http://cran.r-project.org/web/packages/appnn/index.html) capable of accurately and reliably predicting the amyloidogenic propensity from the polypeptide sequence alone with a prediction accuracy of 84.9 % against an external validation dataset of sequences with experimental in vitro, evidence of amyloid formation.
|Keywords||Amyloid; Amino Acid Sequence; Protein Structure, Secondary; Neural Networks (Computer); Amyloidogenic Proteins; Amino Acid Sequence; Amyloid; Amyloidogenic Proteins; Neural Networks (Computer); Protein Structure, Secondary; MD Multidisciplinary; General Science & Technology|
|Publisher||Public Library of Science|
|Digital Object Identifier (DOI)||doi:10.1371/journal.pone.0134679|
|Web address (URL)||https://www.plos.org/|
|04 Jun 2015|
|Publication process dates|
|Deposited||29 Aug 2017|
|Accepted||13 Jun 2015|
CC BY 4.0
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