Biochemistry: “Fingerprinting” of peptides allows earlier Alzheimer’s detection
Drying patterns of peptide solutions make effective identification of neurodegenerative diseases possible – Rapid analysis using neural networks
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. This is caused by minute deviations in the chemical composition of the biomolecules. Researchers at the Karlsruhe Institute of Technology (KIT) from the Helmholtz-Research Field Information have now developed an effective and simple method that can detect such misfoldings at an early stage of the disease. According to this method, misfoldings are revealed by the drying structure of protein and peptide solutions. Microscopic images are analyzed with neural networks, and the prediction accuracy is over 99 percent. The results were published in Advanced Materials. (Source: Karlsruhe Institute of Technology – Press releases)
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. In Alzheimer’s disease, amyloid-beta (Aβ42) peptides, which differ in a single amino acid residue and are heritable mutants of Alzheimer’s disease, play a major role.
Until now, a simple and accurate method for predicting mutations in proteins has been lacking. At KIT’s 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 stain patterns were not only characteristic and reproducible, but also led to a classification of eight mutations with a prediction accuracy of more than 99 percent,” Lahann, author of the study, describes the results. The group has shown that crucial information about primary and secondary peptide structures can be inferred from the spots that their drying droplets leave on a solid surface.
Stain patterns as precise fingerprints of a peptide
In this process, the protein and peptide solutions are precisely applied to the model surfaces using a pipetting robot to obtain controlled and reproducible results. These surfaces were previously coated with a water-repellent polymer coating. To analyze the complex stain patterns of the dried droplets, the researchers created images using polarized light 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 neuronal networks were so effective,” Jörg Lahann describes the results. “The stain patterns of amyloid-beta peptides serve as precise fingerprints that reflect the structural and spatial identity of a peptide.” This technology makes it possible to identify individual Alzheimer’s variants with maximum resolution, Lahann says, and within minutes.
Simple sample preparation provides fast diagnosis
The results suggest that a method as simple as drying a droplet of a peptide solution on a solid surface can serve as an indicator of minute but structural differences in the primary and secondary structures of peptides. “Scalable and accurate detection methods for stratification of spatial and structural protein changes are urgently needed to decipher pathological diseases such as Alzheimer’s and Parkinson’s disease,” the scientist says. In addition, the method is comparatively simple and, above all, does not require complex sample preparation, thus enabling simple and patient-oriented diagnostics. Furthermore, the method also offers great potential for other fields of application in medical diagnostics and in the molecular elucidation of diseases.
The original press release can be found at:
Biochemie: „Fingerabdruck“ von Peptiden erlaubt früheren Alzheimer-Nachweis (only in german)
The original publication can be found at (Open Access):
Azam Jeihanipour, Joerg Lahann, Deep-Learning-Assisted Stratification of Amyloid Beta Mutants Using Drying Droplet Patterns, Advanced Materials, 2022, 34(24), 2110404, DOI: 10.1002/adma.202110404
Localization in the Helmholtz Research Field Information:
Helmholtz Research Field Information, Programm 3: Materials Systems Engineering, Topic 3: Adaptive and Bioinstructive Materials Systems
Contact:
Prof. Dr. Joerg Lahann
Head of Department Advanced Polymers and Biomaterials at the Institute of Functional Interfaces (IFG)
Karlsruhe Institute of Technology (KIT)
Phone: +49 721-608-2-5516
E-Mail: joerg.lahann@kit.edu
Contact for this Press Release:
Dr. Sabine Fodi
Press Officer
Karlsruhe Institute of Technology (KIT)
Phone: +49 721 608-41154
E-Mail: sabine.fodi@kit.edu
