Structure-based machine-guided mapping of amyloid sequence space reveals uncharted sequence clusters with higher solubilities.

ABSTRACT: The amyloid conformation can be adopted by a variety of sequences, but the precise boundaries of amyloid sequence space are still unclear. The currently charted amyloid sequence space is strongly biased towards hydrophobic, beta-sheet prone sequences that form the core of globular proteins and by Q/N/Y rich yeast prions. Here, we took advantage of the increasing amount of high-resolution structural information on amyloid cores currently available in the protein databank to implement a machine learning approach, named Cordax (https://cordax.switchlab.org), that explores amyloid sequence beyond its current boundaries. Clustering by t-Distributed Stochastic Neighbour Embedding (t-SNE) shows how our approach resulted in an expansion away from hydrophobic amyloid sequences towards clusters of l

SUBMITTER: Louros N 

PROVIDER: S-EPMC7335209 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature