Karlsruhe Institute of Technology: “Fingerprinting” of peptides allows earlier detection of Alzheimer’s

Neurodegenerative diseases such as Alzheimer’s or Parkinson’s are caused by misfolding of proteins or peptides, i.e. by changes in their spatial structure. The cause is the smallest deviation in the chemical composition of the biomolecules. Researchers at the Karlsruhe Institute of Technology (KIT) have now developed an effective and simple method that can detect such misfolding at an early stage of the disease. According to this, misfolding can be seen in the drying structure of protein and peptide solutions. Microscopic images are analyzed with neural networks, the prediction accuracy is over 99 percent. The results appeared in Advanced Materials .

Proteins and peptides derive their biological functions from their biochemical structure. There is much evidence that even the smallest structural or spatial changes can promote the development of diseases. Numerous neurodegenerative diseases are due to misfolding of peptides and proteins triggered by such changes. Amyloid beta (Aβ42) peptides, which differ in a single amino acid residue and are heritable mutants of Alzheimer’s disease, play an essential role in Alzheimer’s disease.

Until now, there has been no simple and accurate method for predicting mutations in proteins. At the KIT Institute for Functional Interfaces (IFG), Professor Jörg Lahann’s research group has now developed a method to detect misfolding via the drying structure of protein and peptide solutions. “The speckle patterns were not only characteristic and reproducible, but also led to a classification of eight mutations with a prediction accuracy of over 99 percent,” says Lahann, author of the study, describing the results. The group has shown that crucial information about the primary and secondary peptide structures can be derived from the stains left by their drying droplets on a solid surface.

Spot patterns as accurate fingerprints of a peptide

The protein and peptide solutions are precisely applied to the model surfaces with a pipetting robot in order to obtain controlled and reproducible results. These surfaces have previously been provided with a water-repellent polymer coating. In order to analyze the complex spot patterns of the dried droplets, the researchers created images using polarization microscopy. These images were then analyzed using deep learning neural networks.

“Since the structures are very similar and difficult to distinguish with the naked eye, it was quite surprising that the neural networks were so effective,” Jörg Lahann describes the results. “The speckle patterns of amyloid beta peptides serve as precise fingerprints that reflect the structural and spatial identity of a peptide.” According to Lahann, this technology makes it possible to identify individual Alzheimer’s variants with maximum resolution, and within a few minutes.

Simple sample preparation provides fast diagnosis

The results suggest that a method as simple as drying a droplet of peptide solution on a solid surface can serve as an indicator of tiny but structural differences in the primary and secondary structures of peptides. “Scalable and accurate detection methods for the layering of spatial and structural protein changes are urgently needed to decipher pathological diseases such as Alzheimer’s and Parkinson’s disease,” says the scientist. In addition, it is a comparatively simple method that, above all, does not require any complex sample preparation and thus enables simple and patient-oriented diagnostics.