Computational prognostic evaluation of Alzheimer's drugs from FDA-approved database through structural conformational dynamics and drug repositioning approaches

Abstract

Drug designing is high-priced and time taking process with low success rate. To overcome this obligation, computational drug repositioning technique is being promptly used to predict the possible therapeutic effects of FDA approved drugs against multiple diseases. In this computational study, protein modeling, shape-based screening, molecular docking, pharmacogenomics, and molecular dynamic simulation approaches have been utilized to retrieve the FDA approved drugs against AD. The predicted MADD protein structure was designed by homology modeling and characterized through different computational resources. Donepezil and galantamine were implanted as standard drugs and drugs were screened out based on structural similarities. Furthermore, these drugs were evaluated and based on binding energy (Kcal/mol) profiles against MADD through PyRx tool. Moreover, pharmacogenomics analysis showed good possible associations with AD mediated genes and confirmed through detail literature survey. The best 6 drug (darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar) further docked and analyzed their interaction behavior through hydrogen binding. Finally, MD simulation study were carried out on these drugs and evaluated their stability behavior by generating root mean square deviation and fluctuations (RMSD/F), radius of gyration (Rg) and soluble accessible surface area (SASA) graphs. Taken together, darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar displayed good lead like profile as compared with standard and can be used as possible therapeutic agent in the treatment of AD after in-vitro and in-vivo assessment.

Figure 1

Proposed mechanistic flowchart of repositioned drugs.

Figure 2

The overall protein structure of MADD protein. The figure showed the three main parts, the overall protein structure, its four core domains and couple of disordered regions.

Figure 3

Ramachandran graphs of predicted MADD protein.

Figure 4

QMEAN Quality assessment of MADD protein.

Figure 5

(A–E) SPS analysis of MADD protein to predict their folding and native conformation pattern.

Figure 6

(A) Predicted binding pockets represented by graphs lines and scoring values of death domain of MADD protein. (B) the predicted model of death domain and core part has been depicted in purple color in surface format.

Figure 7

(A) Binding pocket of death domain of MADD protein along with all selected drugs. (B) In detail description, binding of morphine, codeine, quinine, darifenacin, astemizole, nalbuphine, quinidine, aripiprazole, fluspirilene, elacridar, sertindole, vernakalant, sarizotan, fipexide and volinanserin at the active site of target protein.

Figure 8

Binding interaction of best darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar death domain of MADD protein along with all selected drug binding.

Figure 9

RMSD graph of docked complexes of darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar, respectively from 0 to 100 ns.

Figure 10

RMSF graph of darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar docking complexes at 100 ns.

Figure 11

Rg graph of darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar docking complexes from simulation time 0–100 ns.

Figure 12

SASA graph of darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar docking structures from 0 to 100 ns simulation time frame.

Figure 13

Graphical representation of MD protein–ligand Interaction energy trajectories darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar.