Navigating Homogeneous Paths through Amyloidogenic and Non-Amyloidogenic Hexapeptides
Laszlo Keresztes, Evelin Szogi, Balint Varga, Viktor Farkas, Andras, Perczel, Vince Grolmusz

TL;DR
This paper demonstrates that a computational amyloid predictor can connect any two predicted amyloidogenic or non-amyloidogenic hexapeptides through a short path of similar peptides, revealing a structured landscape of peptide properties.
Contribution
It shows that the Budapest Amyloid Predictor, and similar linear SVM-based models, can connect predicted peptides via short paths in a graph based on single-residue differences.
Findings
Predicted amyloidogenic peptides are connected by paths of length at most 6.
The property holds for both amyloidogenic and non-amyloidogenic predictions.
The result is applicable to any linear SVM-based predictor.
Abstract
Hexapeptides are increasingly applied as model systems for studying the amyloidogenecity properties of oligo- and polypeptides. It is possible to construct 64 million different hexapeptides from the twenty proteinogenic amino acid residues. Today's experimental amyloid databases contain only a fraction of these annotated hexapeptides. For labeling all the possible hexapeptides as "amyloidogenic" or "non-amyloidogenic" there exist several computational predictors with good accuracies. It may be of interest to define and study a simple graph structure on the 64 million hexapeptides as nodes when two hexapeptides are connected by an edge if they differ by only a single residue. For example, in this graph, HIKKLM is connected to AIKKLM, or HIKKNM, or HIKKLC, but it is not connected with an edge to VVKKLM or HIKNPM. In the present contribution, we consider our previously published artificial…
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Taxonomy
TopicsAlzheimer's disease research and treatments · Machine Learning in Bioinformatics · Protein Structure and Dynamics
