Identification of novel TACE inhibitors using DNN based- virtual screening, molecular dynamics and biological evaluation

Abstract

Rheumatoid Arthritis (RA) is a well-known autoimmune inflammatory disease, distressing roughly 1% of the adult population throughout the globe. Many studies have suggested that overexpression of TNF-α, a pro-inflammatory cytokine, is responsible for the progression of RA. Furthermore, inhibition of the shedding rate of TNF-α is regulated by the TACE (TNF-α converting enzyme) protein and, hence is considered as an important therapeutic target for the prevention of progressive synovial joint destruction in rheumatoid arthritis. In the present study, we have proposed a deep neural network (DNN)-based workflow for the virtual screening of compounds towards the identification of potential inhibitors against the TACE proteins. Subsequently, a set of compounds were shortlisted, based on the molecular docking and subjected to the biological evaluation to validate the inhibitory activities of the screened compounds, determine the practical applicability of the DNN-based model, and strengthen the hypothesis. Out of seven, three compounds (BTB10246, BTB10247, and BTB10245) showed significant inhibition at 10 µM and 0.1 µM concentration. These three compounds also showed a stable and significant interaction potential against the TACE protein as compared with the re-docked complex system and can serve as a novel scaffold for further design of new molecules with improved inhibitory activities against TACE. Communicated by Ramaswamy H. Sarma