9v9f | pdb_00009v9f
Methionyl-tRNA synthetase from Staphylococcus aureus in complex with an inhibitor
Structural highlights
FunctionA0A0D6FXK4_STAAU Is required not only for elongation of protein synthesis but also for the initiation of all mRNA translation through initiator tRNA(fMet) aminoacylation.[ARBA:ARBA00003314][HAMAP-Rule:MF_01228] Publication Abstract from PubMedMethionyl-tRNA synthetase (MetRS) plays an critical role in protein translation by catalyzing the attachment of l-methionine (l-Met) to its cognate tRNAMet and has long been recognized as a valuable target for antimicrobial drug development. In this study, a drug repurposing screen of a kinase inhibitor library identified AZD8186, a clinically investigated PI3Kbeta modulator, as a promising inhibitor of Staphylococcus aureus MetRS (SaMetRS). The binding mode of AZD8186 to SaMetRS was elucidated through co-crystallography, and subsequent knowledge-directed ligand optimization resulted in enhanced inhibitory activity and improved synthetic accessibility. Furthermore, we developed a novel conservation-aware and interaction-guided 3D generative AI model, designated DiffDeCIG, to facilitate structure-based drug design. DiffDeCIG modified inhibitors to establish additional interactions preferentially with conserved residues within the active pocket of SaMetRS. The optimal compound, MRS-9, potentially competed with all three substrates of MetRS (ATP, l-Met and tRNAMet), and demonstrated over a 300-fold increase in inhibitory activity relative to AZD8186. Importantly, MRS-9 selectively inhibited type 1 MetRS enzymes, while minimally affecting the tested type 2 MetRSs, including the human MetRS, thereby reducing potential adverse effects. This study reveals a novel triple-site inhibitory mechanism targeting MetRS and highlights an integrated strategy that combines knowledge-directed and AI-guided approaches in drug design. Discovery of a triple-site inhibitor targeting bacterial methionyl-tRNA synthetase through combined drug repurposing screening and generative AI-assisted optimization.,Su J, Qiao A, Huang W, Xu J, Lu F, Zhang H, Deng Q, Zou J, Wang Z, Lei J, Zhou H Nucleic Acids Res. 2026 May 5;54(9):gkag488. doi: 10.1093/nar/gkag488. PMID:42152681[1] From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine. References
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