Abstract
Transcriptional repressor EthR from Mycobacterium tuberculosis is a valuable target for antibiotic booster drugs. We previously reported a virtual screening campaign to identify EthR inhibitors for development. Two ligand binding orientations were often proposed, though only the top scoring pose was utilized for filtering of the large data set. We obtained biophysically validated hits, some of which yielded complex crystal structures. In some cases, the crystallized binding mode and top scoring mode agree, while for others an alternate ligand binding orientation was found. In this contribution, we combine rigid docking, molecular dynamics simulations, and the linear interaction energy method to calculate binding free energies and derive relative binding energies for a number of EthR inhibitors in both modes. This strategy allowed us to correctly predict the most favorable orientation. Therefore, this widely applicable approach will be suitable to triage multiple binding modes within EthR and other potential drug targets with similar characteristics.
Highlights
EthR (Figure 1a) is a well-studied transcriptional repressor from Mycobacterium tuberculosis that represses the expression of the Bayer−Villager mono-oxygenase protein
Ligands inhibiting DNA-binding have been shown to increase ethA expression and significantly boost ethionamide efficacy in in vivo models.1,−4,17 EthR inhibitors are under intense investigation as booster codrugs.[5−9,12−15] Several approaches have been used to explore novel chemotypes for binding the lipophilic EthR ligand site (Figure 1b), including traditional high-throughput library screening,[10] fragment-based drug discovery,[12,16] and our previously reported virtual screening (VS) program based on the GOLD software.[18]
We reported our screen of over 400 000 ligands against EthR, a cohort filtered from an initial starting library in excess of six million compounds derived from the Drugs subset of the ZINC database.[18]
Summary
While automated ligand docking approaches can be combined with molecular dynamics (MD) simulations, explicit MD simulations themselves are not yet feasible for screening libraries containing millions of compounds.[25,26] The combined RDD−MD approach presented here refines the binding poses in explicit solvent (often absent in RRD), and following RDD−MD with the calculation of relative ligand binding energies quantifies the binding event in solution to prioritize favorably binding ligands.[27,28] In this contribution we combine. These three crystal structures highlight the differences between the virtual screening pose used for ligand selection and the experimentally observed binding mode. We hypothesized that had we utilized relative binding energies in selecting which orientation from virtual screening was energetically favorable, we would have correctly predicted the experimental binding mode This suggests that a more quantitative approach is required to evaluate the likelihood of observing different poses. Corroborate the interactions inferred from the crystal structures, emphasizing the lipophilicity-driven nature of the binding to EthR This data can be used to quantitatively evaluate the binding event and help inform design decisions at which positions to try to strengthen hydrogen bond acceptors, for example, or to exploit “weak” hydrogen bonding opportunities such as those seen via G106 with compound 3 or M142 with BDM31343 and compound 85. The crystal structure data for EthR with compound 3 and compound 85 are deposited in the PDB under respective accession codes 6R1P and 6R1S
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