Assessment of a Computational Approach to Predict Drug Resistance Mutations for HIV, HBV and SARS-CoV-2.
Assessment of a Computational Approach to Predict Drug Resistance Mutations for HIV, HBV and SARS-CoV-2.
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对一种预测人类免疫缺陷病毒（HIV）、乙型肝炎病毒（HBV）及严重急性呼吸综合征冠状病毒2（SARS-CoV-2）耐药突变的计算方法的评估
DOI：
10.3390/molecules27175413
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发表时间：
2022-08-24
期刊：
影响因子：
4.6
通讯作者：
Schinazi, Raymond F.
中科院分区：
文献类型：
--
作者：
Patel, Dharmeshkumar;Ono, Suzane K.;Bassit, Leda;Verma, Kiran;Amblard, Franck;Schinazi, Raymond F.
关键词：
病毒耐药性是一个世界性的问题，降低了抗病毒药物的有效性。药物靶向蛋白的突变是耐药性出现的主要机制。鉴定耐药突变对阐明耐药机制和提出有希望的治疗策略以对抗耐药是至关重要的。然而，耐药突变的实验鉴定是具有挑战性的，费力且耗时。因此，有效和节省时间的计算结构为基础的方法来预测耐药突变是必不可少的，并在药物发现研究的高度兴趣。然而，这些方法依赖于结合自由能的准确估计，这与计算成本间接相关。为了实现这一目标，我们开发了一个计算工作流程来预测任何已知结构的病毒蛋白的耐药性突变。该方法可以通过残基扫描和Prime MM-GBSA计算定性预测由于突变引起的结合自由能的变化。为了测试这种方法，我们预测了由（-）-FTC选择的HIV-RT中的耐药突变，并证明了对临床突变的准确识别。此外，我们预测了HBV核心蛋白中GLP-26和SARS-CoV-2 3CLpro中Nirmatrelvir的耐药突变。对HBV核心蛋白中GLP-26的两个预测耐药突变和三个预测敏感性突变进行了诱变实验，证实了预测的准确性。
Viral resistance is a worldwide problem mitigating the effectiveness of antiviral drugs. Mutations in the drug-targeting proteins are the primary mechanism for the emergence of drug resistance. It is essential to identify the drug resistance mutations to elucidate the mechanism of resistance and to suggest promising treatment strategies to counter the drug resistance. However, experimental identification of drug resistance mutations is challenging, laborious and time-consuming. Hence, effective and time-saving computational structure-based approaches for predicting drug resistance mutations are essential and are of high interest in drug discovery research. However, these approaches are dependent on accurate estimation of binding free energies which indirectly correlate to the computational cost. Towards this goal, we developed a computational workflow to predict drug resistance mutations for any viral proteins where the structure is known. This approach can qualitatively predict the change in binding free energies due to mutations through residue scanning and Prime MM-GBSA calculations. To test the approach, we predicted resistance mutations in HIV-RT selected by (-)-FTC and demonstrated accurate identification of the clinical mutations. Furthermore, we predicted resistance mutations in HBV core protein for GLP-26 and in SARS-CoV-2 3CLpro for nirmatrelvir. Mutagenesis experiments were performed on two predicted resistance and three predicted sensitivity mutations in HBV core protein for GLP-26, corroborating the accuracy of the predictions.
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