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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Predicting resistance mutations using protein design algorithms
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Predicting resistance mutations using protein design algorithms

机译：使用蛋白质设计算法预测抗性突变

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摘要

Drug resistance resulting from mutations to the target is an unfortunate common phenomenon that limits the lifetime of many of the most successful drugs. In contrast to the investigation of mutations after clinical exposure, it would be powerful to be able to incorporate strategies early in the development process to predict and overcome the effects of possible resistance mutations. Here we present a unique prospective application of an ensemble-based protein design algorithm, K~*, to predict potential resistance mutations in dihydrofolate reductase from Staphylococcus aureus using positive design to maintain catalytic function and negative design to interfere with binding of a lead inhibitor. Enzyme inhibition assays show that three of the four highly-ranked predicted mutants are active yet display lower affinity (18-, 9-, and 13-fold) for the inhibitor. A crystal structure of the top-ranked mutant enzyme validates the predicted conformations of the mutated residues and the structural basis of the loss of potency. The use of protein design algorithms to predict resistance mutations could be incorporated in a lead design strategy against any target that is susceptible to mutational resistance.
机译：由靶基因突变引起的耐药性是一种不幸的普遍现象，它限制了许多最成功药物的寿命。与临床暴露后的突变调查相反，能够在开发过程的早期纳入策略以预测和克服可能的抗药性突变的影响将非常有力。在这里，我们提出了基于整体的蛋白质设计算法K〜*的独特前瞻性应用，以预测金黄色葡萄球菌的二氢叶酸还原酶中的潜在耐药性突变，使用阳性设计来维持催化功能，而阴性设计来干扰铅抑制剂的结合。酶抑制试验表明，四个高度预测的突变体中的三个具有活性，但对抑制剂的亲和力较低（18、9和13倍）。排名最高的突变酶的晶体结构验证了突变残基的预期构象以及效力丧失的结构基础。可以将蛋白质设计算法用于预测抗性突变，并将其结合到针对易受突变抗性影响的任何靶标的领先设计策略中。

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