Biochemistry: a new method makes it possible to detect Alzheimer’s disease earlier

Researchers at the Karlsruhe Institute of Technology (KIT) have developed a new method to detect neurodegenerative diseases. The method is effective and simple and can detect the misfolding of proteins underlying the disease at an early stage of the disease. The prediction accuracy is over 99%, KIT said. The associated study was published in April in the specialist journal “Advanced Materials”.

Neurodegenerative diseases such as Alzheimer’s disease or Parkinson’s disease are caused by misfolding of proteins or peptides, that is, by changes in their spatial structure. The cause is the smallest deviation in the chemical composition of biomolecules. According to a study on the drying structure of protein and peptide solutions, these misfoldings can be recognized.

To do this, researchers led by Professor Jörg Lahann analyzed microscopic images of such solutions and evaluated them with deep learning neural networks. Artificial intelligence (AI) was able to derive the underlying biochemical structure of stains left by the drying droplets of peptide solutions on a solid surface. Spot patterns are difficult to distinguish with the naked eye, but are like “fingerprints” for AI, reflecting the structural and spatial identity of a peptide.

Using this method, the researchers classified eight genetic mutations that lead to misfolding of proteins known to be involved in neurodegenerative diseases and can be effectively detected using the method. The technology can identify variants of Alzheimer’s disease in minutes. Since no tedious sample preparation is required, the method enables simple and patient-oriented diagnostics.

In principle, any doctor could use the method and even read the protein models with a smartphone, Lahann told “Research & Teaching”. A clinical application is not possible at the moment, other studies are necessary. According to Lahann, the peptides used in the study correspond to different variants of a peptide that exist in patients and represent different genetic mutations. Whether the method also works reliably with real patient samples remains to be investigated.

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