This project is one of a number that are in competition for funding from the ‘GW4 BioMed2 MRC Doctoral Training Partnership’ for entry in October 2022.
SUPERVISORY TEAM:
Lead supervisor: Dr Marc van der Kamp, School of Biochemistry (Bristol)
Co-supervisors: Dr Jim Spencer (Bristol), Prof Adrian Mulholland (Bristol), Dr Mark Toleman (Cardiff University)
Rising antibiotic resistance is a major problem for human health. Resistance to β-lactams, the single most important antibiotic class, usually arises through their breakdown by β-lactamases (BLs). Many BL producing bacteria are multi-drug resistant and may cause untreatable infections. Worryingly, new BL variants conferring resistance are detected frequently. As bacteria usually harbour multiple BLs, it is often not clear which BLs cause resistance against which β-lactam antibiotics, and, importantly, how. Using multi-scale computer simulations, we have calculated the efficiency of β-lactam acyl-enzyme formation and breakdown for a number of known BLs, thereby predicting whether and how they confer resistance to specific β-lactams (e.g. [1]). By genomic analysis of clinical isolates (e.g. [2]) as well as lab experiments (e.g. [3]), insights are obtained into the evolution of BLs with enhanced antibiotic breakdown activity.
This multidisciplinary project aims to combine these strands to predict and understand the activity of newly arising BLs against key β-lactam antibiotics (cephalosporins, carbapenems). Computational assays based on multi-scale simulations will be used to assess formation and breakdown of the acyl-enzymes of 1) recently discovered, clinically relevant serine BLs; and 2) BLs with increased resistance obtained in the lab (e.g. by our international collaborators). This is challenging, as exact structures of these variants are typically not available and different reaction mechanisms will need to be explored. We will use recent advances in AI structure prediction (e.g. DeepMind’s AlphaFold2), but also determine structures of selected acylenzymes experimentally (which cannot yet be predicted by AI). Notably, we will predict new putative resistance-conferring BL variants from computational screening of mutations at key positions. Computational predictions of antibiotic breakdown by selected BLs and variants will be validated by experimental determination of β-lactam hydrolysis using state-of-the-art kinetic methods.
The project will provide training in cutting-edge techniques in multiple disciplines (computational chemistry, molecular biology/biochemistry, clinical microbiology/genomics) using high-end facilities in the context of a highly collaborative AMR research environment. The project will benefit from Bristol’s excellent resources for high-performance computing (incl. one of the UK’s largest university computer clusters).
Insights obtained into the mechanisms behind the gain of β-lactam hydrolysis activity in novel BL variants will help anticipate new resistance challenges. This can inform both the use of existing antibiotics and the possible development of new beta-lactam antibiotics that might better evade future BL-conferred resistance. To accelerate knowledge transfer, findings will be discussed with our network of local, national and international collaborators prior to publication. We will also exploit the broad interest in antimicrobial resistance through public engagement activities.
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