Design and screening of novel 1,2,4-Triazole-3-thione derivatives as DCN1 inhibitors for anticardiac fibrosis based on 3D-QSAR modeling and molecular dynamics.

OBJECTIVE

Defective in cullin neddylation 1 (DCN1) plays a pivotal role in anticardiac fibrosis by interacting with UBC12 and catalyzing cullin neddylation, which activates cullin-RING E3 ligases (CRLs). As a key modulator of anticardiac fibrosis, DCN1 has emerged as an attractive target for therapeutic intervention. The aim of this study is to design and evaluate novel DCN1 inhibitors using a combination of three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling, molecular docking, and molecular dynamics simulations.

METHODS

A dataset of 47 derivatives was employed to construct Comparative Molecular Field Analysis (COMSIA) model, incorporating steric, electrostatic, hydrophobic, hydrogen bond donor, and acceptor fields to accurately predict compound activity. In silico molecular docking studies, selected compounds were docked with the target protein to evaluate their binding affinity. Additionally, molecular dynamics simulations were performed to assess the stability of the compounds, followed by energy decomposition analysis was used to identify key residues contributing to binding.

RESULTS

The comparative molecular similarity index analysis (COMSIA) model achieved a cross-validated q of 0.553, a non-cross-validated r of 0.959, and an value of 0.766, demonstrating good accuracy and stability in predicting the activity of the compounds. The top compound exhibited a predicted pIC50 of 9.674 and showed strong binding affinity in molecular docking. Molecular dynamics simulations confirmed the stability of the compound at the binding site, while energy decomposition analysis identified key residues essential for binding interaction.

CONCLUSION

This study successfully designed and evaluated novel DCN1 inhibitors using an integrated approach that combines 3D-QSAR modeling, molecular docking, and molecular dynamics simulations. The findings provide an effective computational platform for the design of DCN1 inhibitors and lay a solid foundation for the development of drugs targeting anticardiac fibrosis.