Computational strategies to overcome antibiotic resistance: Exploring novel tetramic acids as dual inhibitors

Lead Research Organisation: University of Oxford
Department Name: Oxford Chemistry


The most critical obstacle to the design and development of novel antimicrobial agents is the current rise of multiple antibiotic resistance. The historic paradigm has been the use of monotherapies, exploiting a drug based on a single chemical-scaffold which acts on a single biological target. Examples of such drugs included fluoroquinolones (e.g. moxifloxacin) and beta-lactams (e.g. cephalosporins and penicillins), which target DNA replication and cell wall synthesis, respectively. As a means of combatting resistance, the focus has turned to the development of drugs acting on multiple targets belonging to divergent metabolic pathways. The latter may be subdivided into monotherapeutic and duotherapeutic classes. Monotherapeutic drugs can be single scaffolds or hybrids built from two pre-existing drugs (e.g. trimethoprim-ciprofloxacin).
The present study focuses upon the use of single scaffold to design novel inhibitors, specifically, the tetramic acid (TA) scaffold. TAs are found in a broad range of antibacterial natural products, including streptolydigin, reutericyclin and signermycin B, and these inspire synthetic efforts to generate diverse TA libraries harbouring the potential for antibiotic development. Moreover, a library of variably substituted monocyclic and bicyclic 3-carboxamide TAs and bicyclic 3-acyl and 3-enamine TAs reported by Moloney et al., was found to exhibit dual inhibition of bacterial UPPS and RNAP, involved in peptidoglycan synthesis of the bacterial cell wall and RNA transcription respectively.

This study aims to rationalise dual inhibition of UPPS and RNAP by members of a small TA sublibrary. To meet this objective, the following key aspects will be fulfilled:

1. Identification of binding sites for TAs in UPPS and RNAP, in the absence of any co-crystal structures. This will be based on calculated relative binding free energies (delta-Gbind).
2. Elucidation of the basis for binding, van der Waals and/or electrostatic interactions, for representative TA structures.
3. Comparative study of the binding modes in UPPS vs. RNAP and identification of structural motifs critical for dual inhibition in construction of a SAR.
4. Use of experimental binding data to construct and validate a machine learning (ML) model which classifies newly synthesised members of the TA library for binding affinity in UPPS and RNAP, thereby informing future design efforts.
The knowledge gained in this work will help to rationalise existing results and guide future design efforts through the identification of the key structural features necessary for optimal antimicrobial potency.

This project falls within the EPSRC Healthcare Technologies research area.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/R512333/1 30/09/2017 30/03/2022
1947857 Studentship EP/R512333/1 30/09/2017 30/07/2021 Niamh Jimenez