A serendipitous survey of prediction algorithms for amyloidogenicity

The 17- amino acid N-terminal segment of the Huntingtin protein, htt(NT), grows into stable α-helix rich oligomeric aggregates when incubated under physiological conditions. We examined 15 scrambled sequence versions of an htt(NT) peptide for their stabilities against aggregation in aqueous solution at low micromolar concentration and physiological conditions. Surprisingly, given their derivation from a sequence that readily assembles into highly stable α-helical aggregates that fail to convert into β-structure, we found that three of these scrambled peptides rapidly grow into amyloid-like fibrils, while two others also develop amyloid somewhat more slowly. The other 10 scrambled peptides do not detectibly form any aggregates after 100 h incubation under these conditions. We then analyzed these sequences using four previously described algorithms for predicting the tendencies of peptides to grow into amyloid or other β-aggregates. We found that these algorithms-Zyggregator, Tango, Waltz, and Zipper-varied greatly in the number of sequences predicted to be amyloidogenic and in their abilities to correctly identify the amyloid forming members of this scrambled peptide collection. The results are discussed in the context of a review of the sequence and structural factors currently thought to be important in determining amyloid formation kinetics and thermodynamics.