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Selection of Promising Novel Fragment Sized S. aureus SrtA Noncovalent Inhibitors Based on QSAR and Docking Modeling Studies.

Dmitry A ShulgaKonstantin V Kudryavtsev
Published in: Molecules (Basel, Switzerland) (2021)
Sortase A (SrtA) of Staphylococcus aureus has been identified as a promising target to a new type of antivirulent drugs, and therefore, the design of lead molecules with a low nanomolar range of activity and suitable drug-like properties is important. In this work, we aimed at identifying new fragment-sized starting points to design new noncovalent S. aureus SrtA inhibitors by making use of the dedicated molecular motif, 5-arylpyrrolidine-2-carboxylate, which has been previously shown to be significant for covalent binding SrtA inhibitors. To this end, an in silico approach combining QSAR and molecular docking studies was used. The known SrtA inhibitors from the ChEMBL database with diverse scaffolds were first employed to derive descriptors and interpret their significance and correlation to activity. Then, the classification and regression QSAR models were built, which were used for rough ranking of the virtual library of the synthetically feasible compounds containing the dedicated motif. Additionally, the virtual library compounds were docked into the "activated" model of SrtA (PDB:2KID). The consensus ranking of the virtual library resulted in the most promising structures, which will be subject to further synthesis and experimental testing in order to establish new fragment-like molecules for further development into antivirulent drugs.
Keyphrases
  • molecular docking
  • molecular dynamics simulations
  • staphylococcus aureus
  • molecular dynamics
  • machine learning
  • high resolution
  • deep learning
  • mass spectrometry
  • drug induced
  • cystic fibrosis