In-silico screening of potential inhibitors of gamma-secretase, a key enzyme of Alzheimer’s disease

Gamma-secretase is a trans-membrane aspartyl protease that consists of four subunits, namely Anterior Pharynx Defective Phenotype (APH-1), Presenilin (PSEN), Nicastrin (Nct) and Presenilin 2 Enhancer (PEN2). Presenilin is identified as the catalytic core of gamma-secretase with the two aspartyl residues at the catalytic site. Gamma-secretase is involved in the ultimate step in the processing of Amyloid Precursor Protein (APP) to yield Amyloid Beta peptide (Aβ). Aβ of various residue lengths is formed that includes the toxic Aβ42. Aggregation of Aβ42 has been identified to contribute to etiology of Alzheimer’s disease (AD). Inhibition of processing of APP by gamma-secretase is a possible intervention strategy for the therapy for AD. Various studies carried out to find inhibitors for gamma-secretase have populated two classes of compounds, Gammasecretase Inhibitors (GSI) and Gamma-secretase Modulators (GSM). Despite these efforts there is a dearth of an effective therapy for AD. This study aimed to find potential inhibitors of gamma-secretase using in-silico screening of DrugBank database. A total of 10 Pharmacophore models were developed from 54 molecules shown to inhibit gammasecretase. The pharmacophore models were used as 3D query in database screening of 6160 drug molecules selected from DrugBank. The list of hits that resulted from the database screening using all the 10 pharmacophore models was compacted to yield 721 unique entries with a fit score over 3.00 on a scale of 4.00 against the pharmacophore model. A QSAR model was developed employing multiple linear regression to calculate the predicted IC50 value for the 721 molecules from screening using pharmacophore models. Docking study was done to calculate the binding energy for 498 molecules with predicted IC50 value under 10000nM. 55 molecules with binding energy, ∆G, lesser than –8.00 kcal/mol are presented as potential inhibitors of gamma-secretase. Thus, this data can be used for further studies for the development of therapy for Alzheimer’s disease.