The rise of antibiotic resistance threatens modern medicine; to combat it new diagnostic methods are required. Sequencing the whole genome of a pathogen offers the potential to accurately determine which antibiotics will be effective to treat a patient. A key limitation of this approach is that it cannot classify rare or previously unseen mutations. Here we demonstrate that alchemical free energy methods, a well-established class of methods from computational chemistry, can successfully predict whether mutations inStaphylococcus aureusdihydrofolate reductase confer resistance to trimethoprim. We also show that the method is quantitatively accurate by calculating how much the most common resistance-conferring mutation, F99Y, reduces the binding free energy of trimethoprim and comparing predicted and experimentally measured minimum inhibitory concentrations for seven different mutations. Finally, by considering up to 32 free energy calculations for each mutation, we estimate its specificity and sensitivity.

中文翻译：

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金黄色葡萄球菌对甲氧苄啶抗药性的稳健预测

抗生素耐药性的上升威胁着现代医学。为了与之抗衡，需要新的诊断方法。对病原体的整个基因组进行测序提供了准确确定哪些抗生素将有效治疗患者的潜力。该方法的主要局限性在于它无法对稀有或先前未见的突变进行分类。在这里，我们证明了炼金术自由能方法是计算机化学领域公认的一种方法，可以成功预测金黄色葡萄球菌二氢叶酸还原酶中的突变是否赋予对甲氧苄啶的抗性。通过计算最常见的赋予抗性的突变F99Y的数量，我们还表明该方法在定量上是准确的，降低了甲氧苄啶的结合自由能，并比较了预测和实验测量的七个不同突变的最小抑制浓度。最后，通过考虑每个突变的多达32个自由能计算，我们估计了其特异性和敏感性。