Drug resistance is of increasing concern, especially during the treatments of infectious diseases and cancer. To accelerate the drug discovery process in combating issues of drug resistance, here we developed a computational and experimental strategy to predict drug resistance mutations. Using BCR-ABL as a case study, we successfully recaptured the clinically observed mutations that confer resistance imatinib, nilotinib, dasatinib, bosutinib, and ponatinib. We then experimentally tested the predicted mutants in vitro. We found that although all mutants showed weakened binding strength as expected, the binding constants alone were not a good indicator of drug resistance. Instead, the half-maximal inhibitory concentration (IC50) was shown to be a good indicator of the incidence of the predicted mutations, together with change in catalytic efficacy. Our suggested strategy for predicting drug-resistance mutations includes the computational prediction and in vitro selection of mutants with increased IC50 values beyond the drug safety window.

中文翻译：

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计算和实验相结合的策略可确定赋予针对BCR-ABL融合蛋白的药物耐药性的突变。

耐药性越来越引起人们的关注，尤其是在传染病和癌症的治疗过程中。为了加快抗药性问题中的药物发现过程，在这里我们开发了一种计算和实验策略来预测抗药性突变。使用BCR-ABL作为案例研究，我们成功地重新获得了临床观察到的赋予伊马替尼，尼洛替尼，达沙替尼，波舒替尼和ponatinib耐药性的突变。然后，我们在体外实验测试了预测的突变体。我们发现，尽管所有突变体均显示出弱于预期的结合强度，但仅结合常数并不是良好的耐药性指标。取而代之的是，半最大抑制浓度（IC50）被证明是预测突变发生率以及催化功效变化的良好指示。