Investigation of Anti-Alzheimer’s Mechanisms of Sarsasapogenin Derivatives by Network-Based Combining Structure-Based Methods

Alzheimer’s
disease (AD), a neurodegenerative disease with
no cure, affects millions of people worldwide and has become one of
the biggest healthcare challenges. Some investigated compounds play
anti-AD roles at the cellular or the animal level, but their molecular
mechanisms remain unclear. In this study, we designed a strategy combining
network-based and structure-based methods together to identify targets
for anti-AD sarsasapogenin derivatives (AAs). First, we collected
drug-target interactions (DTIs) data from public databases, constructed
a global DTI network, and generated drug-substructure associations.
After network construction, network-based models were built for DTI
prediction. The best bSDTNBI-FCFP_4 model was further used to predict
DTIs for AAs. Second, a structure-based molecular docking method was
employed for rescreening the prediction results to obtain more credible
target proteins. Finally, in vitro experiments were
conducted for validation of the predicted targets, and Nrf2 showed
significant evidence as the target of anti-AD compound AA13. Moreover,
we analyzed the potential mechanisms of AA13 for the treatment of
AD. Generally, our combined strategy could be applied to other novel
drugs or compounds and become a useful tool in identification of new
targets and elucidation of disease mechanisms. Our model was deployed
on our NetInfer web server (http://lmmd.ecust.edu.cn/netinfer/).