  Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks effective
treatment options. Anti-amyloid beta (ABeta) antibodies are the leading drug
candidates to treat AD, but the results of clinical trials have been
disappointing. Introducing rational mutations into anti-ABeta antibodies to
increase their effectiveness is a way forward, but the path to take is unclear.
In this study, we demonstrate the use of computational fragment-based docking
and MMPBSA binding free energy calculations in the analysis of anti-ABeta
antibodies for rational drug design efforts. Our fragment-based docking method
successfully predicted the emergence of the common EFRH epitope, MD simulations
coupled with MMPBSA binding free energy calculations were used to analyze
scenarios described in prior studies, and we introduced rational mutations into
PFA1 to improve its calculated binding affinity towards the pE3-ABeta3-8 form
of ABeta. Two out of four proposed mutations stabilized binding. Our study
demonstrates that a computational approach may lead to an improved drug
candidate for AD in the future.
