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Molecular Insights into Structural and Ligand Binding Features of Methoprene-Tolerant in Daphnids.

Masashi HiranoKenji ToyotaHiroshi IshibashiNobuaki TominagaTomomi SatoNorihisa TatarazakoTaisen Iguchi
Published in: Chemical research in toxicology (2020)
Juvenile hormone (JH) is an important endocrine factor regulating many biological activities in arthropods. In daphnids, methoprene-tolerant (Met) belongs to a basic helix-loop-helix/Per-Arnt-Sim (bHLH/PAS) family protein which has recently been confirmed as a JH receptor and can bind and be activated by JHs and JH agonists. Although the activation of the JH signaling pathway causes many physiological effects, the molecular basis for the structural feature and ligand binding properties of Daphnia Met are not fully understood. To study the ligand preference in terms of structural features of Daphnia Met, we built in silico homology models of the PAS-B domain of Daphnia Mets from cladoceran crustaceans, Daphnia pulex and D. magna. Structural comparison of two Daphnia Met PAS-B domain models revealed that the volume in the main cavity of D. magna Met was larger than that of D. pulex Met. Compared with insect Met, Daphnia Met had a less hydrophobic cavity due to polar residues in the core-binding site. Molecular docking simulations of JH and its analogs with Daphnia Met indicated that the interaction energies were correlated with each of the experimental values of in vivo JH activities based on male induction and in vitro Met-mediated transactivation potencies. Furthermore, in silico site-directed mutagenesis supported experimental findings that Thr292 in D. pulex Met and Thr296 in D. magna Met substitution to valine contribute to JH selectivity and differential species response. This study demonstrates that in silico simulations of Daphnia Met and its ligands may be a tool for predicting the ligand profile and cross species sensitivity.
Keyphrases
  • tyrosine kinase
  • molecular docking
  • signaling pathway
  • transcription factor
  • molecular dynamics simulations
  • zika virus
  • molecular dynamics
  • single cell
  • deep learning