  Hexapeptides are widely applied as a model system for studying
amyloid-forming properties of polypeptides, including proteins. Recently, large
experimental databases have become publicly available with amyloidogenic
labels. Using these datasets for training and testing purposes, one may build
artificial intelligence (AI)-based classifiers for predicting the amyloid state
of peptides. In our previous work (Biomolecules, 11(4) 500, (2021)) we
described the Support Vector Machine (SVM)-based Budapest Amyloid Predictor
(\url{https://pitgroup.org/bap}). Here we apply the Budapest Amyloid Predictor
for discovering numerous amyloidogenic and non-amyloidogenic hexapeptide
patterns with accuracy between 80\% and 84\%, as surprising and succinct novel
rules for further understanding the amyloid state of peptides. For example, we
have shown that for any independently mutated residue (position marked by
``x''), the patterns CxFLWx, FxFLFx, or xxIVIV are predicted to be
amyloidogenic, while those of PxDxxx, xxKxEx, and xxPQxx non-amyloidogenic at
all. We note that each amyloidogenic pattern with two x's (e.g.,CxFLWx)
describes succinctly $20^2=400$ hexapeptides, while the non-amyloidogenic
patterns comprising four point mutations (e.g.,PxDxxx) gives $20^4=160,000$
hexapeptides in total. To our knowledge, no similar applications of artificial
intelligence tools or succinct amyloid patterns were described before the
present work.
