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Anticipating antimicrobial resistance: predicting the resistance-spectrum of emerging lactamase variants using atomistic simulation and experiment

Anticipating antimicrobial resistance: predicting the resistance-spectrum of emerging lactamase variants using atomistic simulation and experiment
预测抗菌药物耐药性:使用原子模拟和实验预测新兴内酰胺酶变体的耐药谱
批准号:
2767481
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金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2022
资助国家:
英国
项目状态:
未结题
起止时间:
2022 至 --

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英文摘要
Rising antibiotic resistance is a major problem for human health. Resistance to lactams, the single most important antibiotic class, usually arises through their breakdown by 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 lactam antibiotics, and, importantly, how. Using multi-scale computer simulations, we have previously calculated the efficiency of lactam acyl-enzyme formation and breakdown for a number of known BLs, thereby predicting whether and how they confer resistance to specific lactams. Genomic analysis of clinical isolates as well as lab experiments can identify new BL variants and thereby provide insights into the evolution of BLs with enhanced antibiotic breakdown activity. In this atomistic simulation-led multidisciplinary project, the main aim will be to understand increased activity of newly arising BLs against key lactam antibiotics (e.g. cephalosporins, carbapenems), and then predict potential new variants. 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) serine BLs with increased resistance obtained in the lab (e.g. by our international collaborators). This is challenging, as exact structures of these variants (in complex with antibiotics) are often not available and different reaction mechanisms will need to be explored. Notably, we will use the insights gained and protocols developed to 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 then be validated by experimental determination of lactam hydrolysis.
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