Designed Combinatorial Libraries of Novel Amyloid-Like Proteins

Abstract

The deposition of amyloid is associated with several neurodegenerative diseases including Alzheimer’s disease and the prion diseases. To probe the relationship between amino acid sequence and the propensity to form amyloid, we studied a combinatorial library of sequences designed de novo. All sequences in the library were designed to share an identical pattern of alternating polar and nonpolar residues as would be consistent with the formation of amphiphilic β-sheet structure. While the polar/nonpolar patterning was identical for all members of the library, the precise identities of these side chains were not constrained and were varied combinatorially. The de novo sequences were expressed in bacteria and purified. Biophysical characterization revealed that the proteins self-assemble into large oligomers visible by electron microscopy as amyloid-like fibrils. Like natural amyloid, the de novo fibrils are composed of β-sheet secondary structure and bind the diagnostic dye, Congo Red. Thus, an alternating pattern of polar and non polar residues is sufficient to cause designed sequences to assemble into amyloid-like fibrils. This finding prompted us to question the distribution of alternating patterns in the sequences of natural proteins. Analysis of a large database of natural protein sequences revealed that alternating patterns occur less frequently than other patterns with similar compositions. The under-representation of alternating patterns in natural proteins, coupled with the observation that such patterns promote assembly of amyloid-like structures in de novo proteins, suggests that sequences of alternating polar and nonpolar amino acids are inherently amyloidogenic and consequently have been disfavored by evolutionary selection.KeywordsCombinatorial LibraryPrion DiseaseAmyloid FibrilNonpolar Amino AcidNonpolar ResidueThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.