Amyloids are proteins associated with the number of clinical disorders (e.g., Alzheimer's, Creutzfeldt-Jakob's and Huntington's diseases). Despite their diversity, all amyloid proteins can undergo aggregation initiated by 6- to 15-residue segments called hot spots. Henceforth, amyloids form unique, zipper-like beta-structures, which are often harmful. To find the patterns defining the hot spots, we developed our novel predictor of amyloidogenicity AmyloGram, based on random forests.
AmyloGram is available as R function (predict.ag_model) or
shiny GUI (AmyloGram_gui).
The package is enriched with the benchmark data set pep424.
Burdukiewicz MJ, Sobczyk P, Roediger S, Duda-Madej A, Mackiewicz P, Kotulska M. (2017) Amyloidogenic motifs revealed by n-gram analysis. Scientific Reports 7 https://doi.org/10.1038/s41598-017-13210-9