A structure-based approach to predict predisposition to amyloidosis.

BACKGROUND: Neurodegenerative diseases and other amyloidoses are linked to the formation of amyloid fibrils. It has been shown that the ability to form these fibrils is coded by the amino acid sequence. Existing methods for the prediction of amyloidogenicity generate an unsatisfactory high number of false positives when tested against sequences of the disease-related proteins. METHODS: Recently, it has been shown that the three-dimensional structure of a majority of disease-related amyloid fibrils contains a ß-strand-loop-ß-strand motif called ß-arch. Using this information, we have developed a novel bioinformatics approach for the prediction of amyloidogenicity. RESULTS: The benchmark results show the superior performance of our method over the existing programs. CONCLUSIONS: As genome sequencing becomes more affordable, our method provides an opportunity to create individual risk profiles for the neurodegenerative, age-related, and other diseases ushering in an era of personalized medicine. It will also be used in the large-scale analysis of proteomes to find new amyloidogenic proteins.

Conformational switching in PolyGln amyloid fibrils resulting from a single amino acid insertion.

The established correlation between neurodegenerative disorders and intracerebral deposition of polyglutamine aggregates motivates attempts to better understand their fibrillar structure. We designed polyglutamines with a few lysines inserted to overcome the hindrance of extreme insolubility and two D-lysines to limit the lengths of ß-strands. One is 33 amino acids long (PolyQKd-33) and the other has one fewer glutamine (PolyQKd-32). Both form well-dispersed fibrils suitable for analysis by electron microscopy. Electron diffraction confirmed cross-ß structures in both fibrils. Remarkably, the deletion of just one glutamine residue from the middle of the peptide leads to substantially different amyloid structures. PolyQKd-32 fibrils are consistently 10-20% wider than PolyQKd-33, as measured by negative staining, cryo-electron microscopy, and scanning transmission electron microscopy. Scanning transmission electron microscopy analysis revealed that the PolyQKd-32 fibrils have 50% higher mass-per-length than PolyQKd-33. This distinction can be explained by a superpleated ß-structure model for PolyQKd-33 and a model with two ß-solenoid protofibrils for PolyQKd-32. These data provide evidence for ß-arch-containing structures in polyglutamine fibrils and open future possibilities for structure-based drug design.

Breaking the amyloidogenicity code: methods to predict amyloids from amino acid sequence.

Numerous studies have shown that the ability to form amyloid fibrils is an inherent property of the polypeptide chain. This has lead to the development of several computational approaches to predict amyloidogenicity by amino acid sequences. Here, we discuss the principles governing these methods, and evaluate them using several datasets. They deliver excellent performance in the tests made using short peptides (~6 residues). However, there is a general tendency towards a high number of false positives when tested against longer sequences. This shortcoming needs to be addressed as these longer sequences are linked to diseases. Recent structural studies have shown that the core element of the majority of disease-related amyloid fibrils is a ß-strand-loop-ß-strand motif called ß-arch. This insight provides an opportunity to substantially improve the prediction of amyloids produced by natural proteins, ushering in an era of personalized medicine based on genome analysis.