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 non-specific binding of aggregation-prone peptides. The underlying interactions remain elusive. Here, we turn to the machine learning-based structure prediction algorithm AlphaFold2 (AF2) to obtain models for the non-specific interactions of b-lactoglobulin (bLG), transthyretin (TTR), or Thioredoxin 80 (T80) with the model amyloid peptide Amyloid b (Ab), and the highly specific complex between the BRICHOS chaperone domain of lung surfactant protein C (CTC) and its polyvaline target. Using a combination of native mass spectrometry (MS) and ion mobility MS, we show that non-specific chaperoning is driven predominantly by hydrophobic interactions of Ab with hydrophobic surfaces in bLG, TTR, and T80, and in part regulated by oligomer stability. For CTC, native MS and hydrogen-deuterium exchange MS reveal that a disordered region recognizes the polyvaline target by forming a complementary b-strand. Hence, we show that AF2 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.

The dataset contains two folders:

IMMS data in PULSAR file format, which can be analyzed using the freely available PULSAR software package

http://pulsar.chem.ox.ac.uk/download.html

HDX-MS data in .txt format, which can be analyzed using the freely available mMAss software package

http://www.mmass.org