Mass Spectrometry and Machine Learning Reveal Determinants of Client Recognition by Antiamyloid Chaperones
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Mass Spectrometry and Machine Learning Reveal Determinants of Client Recognition by Antiamyloid Chaperones. / Osterlund, Nicklas; Vosselman, Thibault; Leppert, Axel; Graslund, Astrid; Jornvall, Hans; Ilag, Leopold L.; Marklund, Erik G.; Elofsson, Arne; Johansson, Jan; Sahin, Cagla; Landreh, Michael.
I: Molecular & Cellular Proteomics, Bind 21, Nr. 10, 100413, 2022.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Mass Spectrometry and Machine Learning Reveal Determinants of Client Recognition by Antiamyloid Chaperones
AU - Osterlund, Nicklas
AU - Vosselman, Thibault
AU - Leppert, Axel
AU - Graslund, Astrid
AU - Jornvall, Hans
AU - Ilag, Leopold L.
AU - Marklund, Erik G.
AU - Elofsson, Arne
AU - Johansson, Jan
AU - Sahin, Cagla
AU - Landreh, Michael
PY - 2022
Y1 - 2022
N2 - 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 hydrophobic interactions of amyloid β with hydrophobic surfaces in β-lactoglobulin, transthyretin, and thioredoxin 80, and in part regulated by oligomer stability. For C-terminal region of lung surfactant protein C, native MS and hydrogen–deuterium exchange MS reveal that a disordered region recognizes the polyvaline target by forming a complementary β-strand. Hence, we show that AlphaFold2 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.
AB - 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 hydrophobic interactions of amyloid β with hydrophobic surfaces in β-lactoglobulin, transthyretin, and thioredoxin 80, and in part regulated by oligomer stability. For C-terminal region of lung surfactant protein C, native MS and hydrogen–deuterium exchange MS reveal that a disordered region recognizes the polyvaline target by forming a complementary β-strand. Hence, we show that AlphaFold2 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.
KW - PROSURFACTANT PROTEIN-C
KW - BRICHOS DOMAIN
KW - ION MOBILITY
KW - AMYLOID FIBRILLATION
KW - BINDING-SITES
KW - GAS-PHASE
KW - BETA
KW - TRANSTHYRETIN
KW - AGGREGATION
KW - INSIGHTS
U2 - 10.1016/j.mcpro.2022.100413
DO - 10.1016/j.mcpro.2022.100413
M3 - Journal article
C2 - 36115577
VL - 21
JO - Molecular and Cellular Proteomics
JF - Molecular and Cellular Proteomics
SN - 1535-9476
IS - 10
M1 - 100413
ER -
ID: 330734228