Peptide 'fingerprint' enables earlier Alzheimer's diagnosis

Neurodegenerative diseases like Alzheimer’s disease or Parkinson’s disease are caused by folding errors (misfolding) in proteins or peptides — ie, changes in their spatial structure as a result of minute deviations in the chemical composition of the biomolecules. Researchers at the Karlsruhe Institute of Technology (KIT) have now developed a simple and effective method for detecting such misfolding at an early stage of the disease.

The biochemical structure of proteins and peptides determines their biological functions. There are many indications that even minute structural or spatial changes can promote the development of diseases, with many neurodegenerative diseases attributed to misfolding of proteins and peptides. Amyloid beta (Aβ42) peptides play a key role in Alzheimer’s disease; they differ in a single amino acid residue and represent hereditary mutants of Alzheimer’s.

Previously, there has not been a simple and accurate method for predicting mutations in proteins. Now, a research group led by Professor Jörg Lahann, at KIT’s Institute of Functional Interfaces (IFG), has created a method for detecting misfolding via the structure of dried protein and peptide solutions, with their results published in the journal Advanced Materials.

“The stain patterns were not only characteristic and reproducible, but also result in a classification of eight mutations with a predictive accuracy of more than 99%,” Lahann said. The group showed that crucial information about the primary and secondary structures of peptides can be gleaned from the stains left behind by drying droplets of peptide solution on a solid surface.

The protein and peptide solutions are precisely placed on glass slides by an automated pipetting system to ensure controlled and reproducible results. The surfaces of the slides were prepared in advance with a hydrophobic polymer coating. To analyse the complex stain patterns from the dried droplets, the researchers acquired images using polarisation microscopy. The images were then analysed with deep-learning neural networks.

“Since the structures are very similar and difficult to distinguish with the naked eye, it was definitely a surprise that the neural networks were so effective,” Lahann said. “The stain patterns of amyloid beta peptides serve as exact fingerprints that reflect the structural and spatial identity of a peptide.”

Neural networks can detect minute differences in the stain patterns from dried peptide solutions. Left: amyloid beta (Aβ42) peptide; right: mutation.

This technology enables the identification of Alzheimer variants with maximum resolution within a few minutes, according to Lahann. The results suggest that a method as simple as drying a droplet of peptide solution on a solid surface can serve as an indicator for minute differences in the primary and secondary structures of peptides. It is also a relatively simple method that requires no elaborate preparation of samples and thus enables simple and patient-friendly diagnosis.

The method has great potential for other applications in medical diagnostics and in the molecular detection of diseases. “Scalable and accurate detection methods for the stratification of conformational and structural protein alterations are urgently needed in order to decode the pathological signatures of diseases like Alzheimer’s and Parkinson’s,” Lahann said.

Top image credit: ©stock.adobe.com/au/quickshooting

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