Bioinformatic assessment of the potential amyloidogenicity of the human and evolutionarily more ancient proteomes.

Publication date: Aug 05, 2026

AmyloGram is a computer program that uses n-gram encoding and a random forest classifier to produce a numerical score between 0 and 1 for the predicted amyloidogenicity of a given protein sequence. In a variety of recent studies, we have used AmyloGram to obtain an overall amyloidogenicity score for members of the human proteome. Of 83,567 full-length canonical human polypeptides, 79. 2% had a score exceeding 0. 7 (the median was 0. 813), consistent with the view that most natural protein sequences contain elements that are in fact potentially amyloidogenic. Here, we first asked whether this operational threshold is supported by orthogonal predictors and curated amyloid proteins, and then whether similarly high scores are also observed in evolutionarily ancient proteomes. For the human proteome, PASTA2 values correlated positively with AmyloGram scores (r2 = 0. 374 for minimum free energy and 0. 321 for average free energy), and proteins with AmyloGram scores ≥0. 7 were significantly enriched for strongly negative PASTA2 values (for average free energy

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Concepts Keywords
Amyloidogenicity AmyloGram
Bioinformatic Amyloid
Forest Amyloid
Free Amyloidogenesis
Amyloidogenic Proteins
Amyloidogenic Proteins
Computational Biology
Evolution, Molecular
evolutionary biology
Humans
protein aggregation
Proteome
Proteome

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