The assembly of proteins and peptides into amyloid fibrils
is causally linked to serious disorders such as Alzheimer’s
disease. Multiple proteins have been shown to prevent
amyloid formation in vitro and in vivo, ranging from highly
specific chaperone–client pairs to completely nonspecific
binding of aggregation-prone peptides. The underlying
interactions remain elusive. Here, we turn to the machine
learning–based structure prediction algorithm AlphaFold2
to obtain models for the nonspecific interactions of
β-lactoglobulin, transthyretin, or thioredoxin 80 with the
model amyloid peptide amyloid β and the highly specific
complex between the BRICHOS chaperone domain of
C-terminal region of lung surfactant protein C and its
polyvaline target. Using a combination of native mass
spectrometry (MS) and ion mobility MS, we show that
nonspecific chaperoning is driven predominantly by hy-
drophobic interactions of amyloid β with hydrophobic
surfaces in β-lactoglobulin, transthyretin, and thioredoxin
80, and in part regulated by oligomer stability. For C-ter-
minal region of lung surfactant protein C, native MS and
hydrogen–deuterium exchange MS reveal that a disor-
dered region recognizes the polyvaline target by forming a
complementary β-strand. Hence, we show that Alpha-
Fold2 and MS can yield atomistic models of hard-to-
capture protein interactions that reveal different
chaperoning mechanisms based on separate ligand
properties and may provide possible clues for specific
therapeutic intervention.