  The amyloid state of proteins is widely studied with relevancy in neurology,
biochemistry, and biotechnology. In contrast with amorphous aggregation, the
amyloid state has a well-defined structure, consisting of parallel and
anti-parallel $\beta$-sheets in a periodically repeated formation. The
understanding of the amyloid state is growing with the development of novel
molecular imaging tools, like cryogenic electron microscopy. Sequence-based
amyloid predictors were developed by using mostly artificial neural networks
(ANNs) as the underlying computational techniques. From a good neural
network-based predictor, it is a very difficult task to identify those
attributes of the input amino acid sequence, which implied the decision of the
network. Here we present a Support Vector Machine (SVM)-based predictor for
hexapeptides with correctness higher than 84\%, i.e., it is at least as good as
the published ANN-based tools. Unlike the artificial neural networks, the
decision of the SVMs are much easier to analyze, and from a good predictor, we
can infer rich biochemical knowledge.
  Availability and Implementation: The Budapest Amyloid Predictor webserver is
freely available at https://pitgroup.org/bap.
