学术文献库

Methods from artificial intelligence (AI), in general, and machine learning, in particular, have kept conquering new territories in numerous areas of science. Most of the applications of these techniques are restricted to the classification of large data sets, but new scientific knowledge can seldom be inferred from these tools. Here we show that an AI-based amyloidogenecity predictor can strongly differentiate the border- and the internal hexamers of $β$-pleated sheets when screening all the Protein Data Bank-deposited homology-filtered protein structures. Our main result shows that more than 30\% of internal hexamers of $β$ sheets are predicted to be amyloidogenic, while just outside the border regions, only 3\% are predicted as such. This result may elucidate a general protection mechanism of proteins against turning into amyloids: if the borders of $β$-sheets were amyloidogenic, then the whole $β$ sheet could turn more easily into an insoluble amyloid-structure, characterized by periodically repeated parallel $β$-sheets. We also present that no analogous phenomenon exists on the borders of $α$-helices or randomly chosen subsequences of the studied protein structures.