Back

Anti-amyloid antibody effects on Aβ-42 protein aggregates profiled using nanospectroscopy

Kummer, N.; Cihova, M.; Nirmalraj, P.

2025-07-21 biophysics
10.1101/2025.07.17.665305 bioRxiv
Show abstract

Anti-amyloid beta (A{beta}) drugs such as aducanumab and lecanemab are designed to clear brain amyloids and slow Alzheimers disease (AD) progression. While immunoassays provide ensemble-level details on anti-A{beta} drug interactions with protein targets, their interfacial effects are largely unknown at a single-particle level. Here, we profile untreated and aducanumab-treated A{beta}-42 protein aggregates from oligomers to fibrils using atomic force microscopy coupled with infrared spectroscopy (nanospectroscopy). Based on the recorded morphological and secondary structure details of aducanumab-treated A{beta}-42 aggregates using nanospectroscopy, we observed a reduction in oligomer prevalence and formation of larger diameter fibril bundles compared to identically prepared untreated-A{beta}-42 peptides. Conversely, controls based on lecanemab did not reveal any quenching of the A{beta}-42 oligomer generation. Moreover, lecanemab was evidenced to bind along the full length of the A{beta}-42 protofibril surface preferentially. Importantly, the structure of A{beta}-42 fibrils did not disassemble in both studies upon the adsorption of aducanumab and lecanemab. Additional experiments were also conducted, such as aggregation kinetic assays and Fourier transform infrared spectroscopy on aducanumab and lecanemab-treated A{beta}-42 protein aggregates that corroborated the findings from nanospectroscopy studies. Our work highlights the usefulness of nanospectroscopy in studying elemental anti-amyloid antibody interactions with protein biomarkers, a requisite for improving Alzheimers disease-modifying treatments.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.