Molecular modeling studies to discover novel mIDH2 inhibitors with high selectivity for the primary and secondary mutants. (June 2020)

• Record Type: Journal Article
• Authors: Yao, Kun; Liu, Haipeng; Liu, Pengyu; Liu, Wenbin; Yang, Jie; Wei, Qingyun; Cao, Peng; Lai, Yisheng
• Is Part Of: Computational biology and chemistry. Volume 86(2020)
• Publication Date: 2020-06
• Subjects: Mutant IDH2 -- Molecular dynamics simulation -- Receptor-ligand interaction-based pharmacophore -- Hierarchical docking -- Virtual screening; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2020.107261
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: A multi-step virtual screening protocol was performed to discover novel mutant IDH2 inhibitors. A receptor-ligand interaction-based pharmacophore (IBP) model was created to conduct the preliminary screening. The hits with high selectivity against wild type IDH2 were obtained according to the docking result. Induced fit docking and MD simulations was used to screen the inhibitors against AG-221-resistant mutants. Abstract: Mutant isocitrate dehydrogenase 2 (mIDH2) is an emerging target for the treatment of cancer. AG-221 is the first mIDH2 inhibitor approved by the FDA for acute myeloid leukemia treatment, but its acquired resistance has recently been observed, necessitating the development of new inhibitor. In this study, a multi-step virtual screening protocol was employed for the analysis of a large database of compounds to identify potential mIDH2 inhibitors. To this end, we firstly utilized molecular dynamics (MD) simulations and binding free energy calculations to elucidate the key factors affecting ligand binding and drug resistance. Based on these findings, the receptor-ligand interaction-based pharmacophore (IBP) model and hierarchical docking-based virtual screening were sequentially carried out to assess 212, 736 compounds from the Specs database. The resulting hits were finally ranked by PAINS filter and ADME prediction and the top compounds were obtained. Among them, six molecules were identified as mIDH2 putative inhibitors with high selectivity by interacting with the capping residue Asp312. Furthermore, subsequent docking and MD experiments demonstrated that compound V2 might have potential inhibitory activity against the AG-221-resistant mutants, thereby making it a promising lead for the development of novel mIDH2 inhibitors.