Computer-aided discovery of potential inhibitors for transthyretin-related amyloidosis

Transthyretin (TTR) is a homotetrameric plasma protein associated with human amyloid diseases. Although Tafamidis has been recently approved for the treatment of TTR familial amyloid polyneuropathy (FAP), there is still a need for more effective drugs in the treatment of TTR amyloidosis diseases. In this study, a computer-aided approach combining molecular docking, virtual screening and molecular dynamics (MD) simulations was employed to identify potent TTR amyloidosis inhibitors from National Cancer Institute (NCI), Maybridge and Asdi databases. A receptor-specific scoring function was also developed using comparative binding energy (COMBINE) method to accurately predict the inhibitory activities for the selected compounds during virtual screening. The developed receptor-specific scoring function demonstrated good predictive ability by yielding strong correlation coefficients between experimental activities and estimated activities for 32 training set and 9 test set compounds, respectively. Moreover, it was successfully applied to rank the candidate compounds from structure-based virtual screening. Finally, three compounds (NSC220163, MFCD00276817 and SPB06319) were identified as potential leads, which exhibited higher predicted inhibitory activities and higher binding affinities in comparison to the Tafamidis. Our results further suggest that halogen bonding interaction plays a crucial role in stabilizing the TTR-inhibitor complex. These results indicate that our computational approach could effectively discover more potent TTR amyloidosis inhibitors, which can be further validate by in vitro and in vivo biological tests. In this study, we combined the molecular docking, receptor-specific scoring function, virtual screening and molecular dynamics (MD) simulations to search potent TTR amyloidosis inhibitors from NCI, Maybridge and Asdi databases. Finally, only three compounds were identified as potential hits.