Abstract
Amyloid aggregates are pathological hallmarks of many human diseases, but how soluble proteins nucleate to form amyloids is poorly understood. Here, we use combinatorial mutagenesis, a kinetic selection assay, and machine learning to massively perturb the energetics of the nucleation reaction of amyloid-β (Aβ42), the protein that aggregates in Alzheimer's disease. In total, we measure the nucleation rates of >140,000 variants of Aβ42 to accurately quantify the changes in free energy of activation of the reaction for all possible amino acid substitutions in a protein and, in addition, to quantify >600 energetic interactions between mutations. Strong energetic couplings suggest that the Aβ42 nucleation reaction transition state is structured in a short C-terminal region, providing a structural model for the reaction that may initiate Alzheimer's disease. Using this approach it should be possible to reveal the energetic structures of additional amyloid transition states and, in combination with additional selection assays, protein transition states more generally.