Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by gradual memory and cognitive function loss. Currently, there are no effective treatments that can reverse or stabilize the symptoms of the disease . Anti-amyloid beta (ABeta) antibodies are the leading drug candidates to treat AD, but the results of clinical trials over the past two decades have been mostly disappointing. This was due to either low effectiveness or serious side effects that emerged during clinical trials. Introducing rational mutations into anti-ABeta antibodies to increase their effectiveness and remove harmful side reactions is a way forward, but the path to take is unclear since the key structural characteristics that determine the binding affinity and selectivity of anti-ABeta antibodies towards amyloid species are not well understood . In this study, we have taken a computational approach to examine how an anti-ABeta antibody binds to one or more ABeta epitopes in its antigen-combining site. Our unbiased fragment-based docking method successfully predicted the emergence of the key EFRH epitopecommonly observed when ABeta binds to anti-ABeta antibodies. MD simulations coupled with MMPBSA binding free energy calculations were used to analyze scenarios described in prior studies concerning the nature of anti-ABeta antibody binding to various ABeta species. Finally, based on observing our MD trajectories of the PFA1 antibody bound to ABeta_2-7 and pE3ABeta_3-8 ,  we introduced rational mutations into PFA1 in an effort to improve the calculated binding affinity of PFA1 towards the pE3-ABeta_3-8 form of ABeta. Two out of our four proposed mutations stabilized binding. Our study demonstrates that a computational approach can predict beneficial mutations which may lead to an improved drug candidate for AD in the future.
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.
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characterized by gradual memory and cognitive function loss. Currently, there are no effective treatments that can reverse or stabilize the symptoms of the disease
that lacks effective treatment options
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over the past two decades have been mostly disappointing. This was due to either low effectiveness or serious side effects that emerged during clinical trials.
have been disappointing.
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and remove harmful side reactions
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since the key structural characteristics that determine the binding affinity and selectivity of anti-ABeta antibodies towards amyloid species are not well understood
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have taken a computational approach to examine how an
demonstrate the use of computational fragment-based docking and MMPBSA binding free energy calculations in the analysis of
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antibody binds to one or more ABeta epitopes in its antigen-combining site. Our unbiased
antibodies for rational drug design efforts. Our
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key EFRH epitopecommonly observed when ABeta binds to anti-ABeta antibodies.
common EFRH epitope,
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concerning the nature of anti-ABeta antibody binding to various ABeta species. Finally, based on observing our MD trajectories of the PFA1 antibody bound to ABeta_2-7 and pE3ABeta_3-8
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in an effort to improve the
to improve its
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of PFA1 towards the pE3-ABeta_3-8
towards the pE3-ABeta3-8
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our
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can predict beneficial mutations which