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Related Concept Videos

Amyloid Fibrils03:03

Amyloid Fibrils

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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,...
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Related Experiment Video

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Developing machine-learning-based amyloidogenicity predictors with Cross-Beta DB.

Valentin Gonay1,2, Michael P Dunne2, Javier Caceres-Delpiano3

  • 1CRBM UMR 5237 CNRS, Université Montpellier, Montpellier, France.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|January 8, 2025
PubMed
Summary
This summary is machine-generated.

A new database of naturally occurring amyloids and a machine learning (ML) predictor were developed. This predictor, Cross-Beta, shows high accuracy in identifying amyloid-forming proteins, aiding in neurodegenerative disease risk assessment.

Keywords:
GWASamyloidosisartificial intelligencecomputational methodscross‐β structuredatabasemachine‐learning

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Genomics

Background:

  • Protein amyloidogenesis is implicated in various diseases and biological functions.
  • Accurate computational prediction of amyloidogenicity is crucial for understanding these processes.
  • The performance of AI-driven predictors relies heavily on the quality of training data.

Purpose of the Study:

  • To create a high-quality database of naturally occurring cross-β amyloids.
  • To develop and benchmark machine learning (ML) algorithms for predicting protein amyloid-forming potential.
  • To introduce a novel computational tool for assessing amyloidogenicity.

Main Methods:

  • Construction of Cross-Beta DB, a curated database of known cross-β amyloids.
  • Training and evaluation of multiple ML algorithms using the Cross-Beta DB dataset.
  • Development of the Cross-Beta predictor utilizing an Extra Trees ML algorithm.

Main Results:

  • The Cross-Beta predictor achieved superior performance compared to existing methods.
  • The predictor demonstrated a high F1 score of 0.852 and accuracy of 0.844.
  • The developed ML model effectively predicts the amyloid-forming potential of proteins.

Conclusions:

  • The Cross-Beta DB database provides a valuable resource for amyloid research.
  • The Cross-Beta predictor offers a robust tool for identifying amyloidogenic proteins.
  • This advancement may facilitate personalized risk profiling for neurodegenerative diseases and other amyloidoses.