Abstract
Advances in solid-state nuclear magnetic resonance (ssNMR) spectroscopy and cryogenic electron microscopy (cryoEM) have revealed the polymorphic nature of the amyloid state of proteins. Given the association of amyloid with protein misfolding disorders, it is important to understand the principles underlying this polymorphism. To address this problem, we combined computational tools to predict the specific regions of the sequence forming the β-spine of amyloid fibrils with the availability of 30, 83 and 24 amyloid structures deposited in the Protein Data Bank (PDB) and Amyloid Atlas (AAt) for the amyloid β (Aβ) peptide, α-synuclein (αS), and the 4R isoforms of tau, associated with Alzheimer's disease, Parkinson's disease, and various tauopathies, respectively. This approach enabled a statistical analysis of sequences forming β-sheet regions in amyloid polymorphs. We computed for any given sequence residue n the fraction of PDB/AAt structures in which that residue adopts a β-sheet conformation (Fβ(n)) to generate an experimental, structure-based profile of Fβ(n) vs n, which represents the β-conformational preference of any residue in the amyloid state. The peaks in the respective Fβ(n) profiles of the three proteins, corresponding to sequence regions adopting more frequently the β-sheet structural core in the various fibrillar structures, align very well with the peaks identified with five predictive algorithms (ZYGGREGATOR, TANGO, PASTA, AGGRESCAN, and WALTZ). These results indicate that, despite amyloid polymorphism, sequence regions most often forming the structural core of amyloid have high hydrophobicity, high intrinsic β-sheet propensity and low electrostatic charge across the sequence, as rationalised and predicted by the algorithms.