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Related Concept Videos

Amyloid Fibrils03:03

Amyloid Fibrils

Amyloid fibrils are aggregates of misfolded proteins.  Under most circumstances, misfolded proteins are either refolded by chaperone proteins or degraded by the proteasome. However, in the case of a mutation or a disease, these proteins can accumulate to form large clusters and often further assemble to form elongated fibers, called fibrils. 
Amyloid deposits were observed as early as 1639 in the liver and the spleen.   In 1854, Rudolph Virchow performed iodine staining, normally used to...

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Related Experiment Video

Updated: Jun 16, 2026

Selection of Aptamers for Amyloid &beta;-Protein, the Causative Agent of Alzheimer&#39;s Disease
15:23

Selection of Aptamers for Amyloid β-Protein, the Causative Agent of Alzheimer's Disease

Published on: May 13, 2010

Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies.

Maria Pamela C David1, Gisela P Concepcion, Eduardo A Padlan

  • 1Virtual Laboratory of Biomolecular Structures, Marine Science Institute, College of Science, University of the Philippines Diliman, Quezon City 1101, Philippines. maria.pamela.david@gmail.com

BMC Bioinformatics
|February 11, 2010
PubMed
Summary
This summary is machine-generated.

Predicting protein amyloidogenicity is crucial for understanding degenerative diseases. This study shows naive Bayesian classifiers and decision trees can predict amyloid formation in immunoglobulin sequences, with accuracy improving with larger datasets.

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Rapid Generation of Amyloid from Native Proteins In vitro
05:48

Rapid Generation of Amyloid from Native Proteins In vitro

Published on: December 5, 2013

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Last Updated: Jun 16, 2026

Selection of Aptamers for Amyloid &beta;-Protein, the Causative Agent of Alzheimer&#39;s Disease
15:23

Selection of Aptamers for Amyloid β-Protein, the Causative Agent of Alzheimer's Disease

Published on: May 13, 2010

Rapid Generation of Amyloid from Native Proteins In vitro
05:48

Rapid Generation of Amyloid from Native Proteins In vitro

Published on: December 5, 2013

Area of Science:

  • Biochemistry
  • Computational Biology
  • Immunology

Background:

  • All polypeptide backbones can form amyloid fibrils, implicated in degenerative disorders.
  • Protein amino acid composition significantly influences amyloidogenicity under physiological conditions.

Purpose of the Study:

  • To explore the use of a naive Bayesian classifier and a weighted decision tree for predicting the amyloidogenicity of immunoglobulin sequences.
  • To assess the accuracy of these computational methods in identifying sequences prone to forming amyloid fibrils.

Main Methods:

  • Utilized a naive Bayesian classifier and a weighted decision tree for sequence analysis.
  • Employed leave-one-out (LOO) cross-validation and holdout test sets for performance evaluation.
  • Trained classifiers on a dataset of 143 amyloidogenic and non-amyloidogenic immunoglobulin sequences.

Main Results:

  • The Bayesian classifier achieved an average LOO cross-validation accuracy of 60.84% and a holdout accuracy of 61.15%.
  • Augmenting the training set improved LOO accuracy to 81.08%.
  • The decision tree achieved a holdout accuracy of 78.64% for amyloidogenic sequences and 75.00% for non-amyloidogenic sequences, with 89% accuracy on the holdout set for non-amyloidogenic sequences.

Conclusions:

  • Both naive Bayesian classifiers and decision trees show promise for predicting sequence amyloidogenicity.
  • Increasing training set size and incorporating structural/physicochemical data can enhance classifier accuracy.
  • These predictive tools have significant applications in evaluating engineered antibodies and proteins.