Ligand-based pharmacophore modeling of TNF-α to design novel inhibitors using virtual screening and molecular dynamics

Tumor necrosis factor-alpha (TNF-alpha) is one of the promising targets for treating inflammatory (Crohn disease, psoriasis, psoriatic arthritis, rheumatoid arthritis) and various other diseases. Commercially available TNF-alpha inhibitors are associated with several risks and limitations. In the present study, we have identified small TNF-alpha inhibitors usingin silicoapproaches, namely pharmacophore modeling, virtual screening, molecular docking, molecular dynamics simulation and free binding energy calculations. The study yielded better and potent hits that bind to TNF-alpha with significant affinity. The best pharmacophore model generated using LigandScout has an efficient hit rate and Area Under the operating Curve. High throughput virtual screening of SPECS database molecules against crystal structure of TNF-alpha protein, coupled with physicochemical filtration, PAINS test. Virtual hit compounds used for molecular docking enabled the identification of 20 compounds with better binding energies when compared with previously known TNF-alpha inhibitors. MD simulation analysis on 20 virtual identified hits showed that ligand binding with TNF-alpha protein is stable and protein-ligand conformation remains unchanged. Further, 16 compounds passed ADMET analysis suggesting these identified hit compounds are suitable for designing a future class of potent TNF-alpha inhibitors. Communicated by Ramaswamy H. Sarma