AI Article Synopsis

  • * Researchers used advanced techniques like mutation screening and molecular dynamics to analyze how Aducanumab interacts with amyloid-beta (Aβ), finding that certain regions of the antibody contribute significantly to its binding affinity.
  • * A mutation that was thought to increase Aducanumab's binding actually reduced its stability, while new computationally designed peptides show promise as potential inhibitors of Aβ aggregation, suggesting new directions for Alzheimer's treatment.

Article Abstract

Seven treatments are approved for Alzheimer's disease, but five of them only relieve symptoms and do not alter the course of the disease. Aducanumab (Adu) and lecanemab are novel disease-modifying antiamyloid-β (Aβ) human monoclonal antibodies that specifically target the pathophysiology of Alzheimer's disease (AD) and were recently approved for its treatment. However, their administration is associated with serious side effects, and their use is limited to early stages of the disease. Therefore, drug discovery remains of great importance in AD research. To gain new insights into the development of novel drugs for Alzheimer's disease, a combination of techniques was employed, including mutation screening, molecular dynamics, and quantum biochemistry. These were used to outline the interfacial interactions of the Aducanumab::Aβ complex. Our analysis identified critical stabilizing contacts, revealing up to 40% variation in the affinity of the Adu chains for Aβ depending on the conformation outlined. Remarkably, two complementarity determining regions (CDRs) of the Adu heavy chain (HCDR3 and HCDR2) and one CDR of the Adu light chain (LCDR3) accounted for approximately 77% of the affinity of Adu for Aβ, confirming their critical role in epitope recognition. A single mutation, originally reported to have the potential to increase the affinity of Adu for Aβ, was shown to decrease its structural stability without increasing the overall binding affinity. Mimetic peptides that have the potential to inhibit Aβ aggregation were designed by using computational outcomes. Our results support the use of these peptides as promising drugs with great potential as inhibitors of Aβ aggregation.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11450751PMC
http://dx.doi.org/10.1021/acschemneuro.4c00453DOI Listing

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