Predicting drug resistance of the HIV-1 protease using molecular interaction energy components.
Predicting drug resistance of the HIV-1 protease using molecular interaction energy components.
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DOI：
10.1002/prot.22192
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发表时间：
2009-03
影响因子：
2.9
通讯作者：
Wang, Wei
中科院分区：
文献类型：
--
作者：
Hou, Tingjun;Zhang, Wei;Wang, Jian;Wang, Wei
耐药性严重影响艾滋病治疗的效果。因此，准确预测病毒的耐药突变体对于开发有效的药物和设计治疗方案特别有用。在这项研究中，我们应用了一种基于结构的计算方法来预测HIV-1蛋白酶的突变体对FDA批准的七种药物的耐药性。通过计算药物和蛋白酶残基之间的分子相互作用能分量（MIECs），分析了蛋白酶-药物相互作用的能量模式。支持向量机（SVM）的MIECs上训练分类蛋白酶突变体到耐药和非耐药类别。交叉验证的测试集的高预测精度表明，MIEC成功地表征了药物与HIV-1蛋白酶之间的相互作用界面。我们对一种新批准的药物达芦那韦（TMC 114）进行了概念验证研究，在公共领域没有耐药性数据。与安普那韦相比，我们的分析表明，地瑞那韦可能更有效地对抗耐药性。为了定量估计药物的结合亲和力并研究蛋白酶残基对引起抗性的贡献，使用偏最小二乘法（PLS）在MIEC上训练线性回归模型。MIEC-PLS模型也取得了令人满意的预测精度。回归模型中MIEC的拟合系数分析揭示了重要的耐药突变，并揭示了这些突变导致耐药的机制。我们的研究证明了使用MIEC表征蛋白酶-药物相互作用的优势。我们相信，MIEC-SVM和MIEC-PLS可以帮助设计新的药物或治疗方案的组合，以对抗HIV-1蛋白酶耐药菌株。
Drug resistance significantly impairs the efficacy of AIDS therapy. Therefore, precise prediction of resistant viral mutants is particularly useful for developing effective drugs and designing therapeutic regimen. In this study, we applied a structure-based computational approach to predict mutants of the HIV-1 protease resistant to the seven FDA approved drugs. We analyzed the energetic pattern of the protease-drug interaction by calculating the molecular interaction energy components (MIECs) between the drug and the protease residues. Support vector machines (SVMs) were trained on MIECs to classify protease mutants into resistant and nonresistant categories. The high prediction accuracies for the test sets of cross-validations suggested that the MIECs successfully characterized the interaction interface between drugs and the HIV-1 protease. We conducted a proof-of-concept study on a newly approved drug, darunavir (TMC114), on which no drug resistance data were available in the public domain. Compared with amprenavir, our analysis suggested that darunavir might be more potent to combat drug resistance. To quantitatively estimate binding affinities of drugs and study the contributions of protease residues to causing resistance, linear regression models were trained on MIECs using partial least squares (PLS). The MIEC-PLS models also achieved satisfactory prediction accuracy. Analysis of the fitting coefficients of MIECs in the regression model revealed the important resistance mutations and shed light into understanding the mechanisms of these mutations to cause resistance. Our study demonstrated the advantages of characterizing the protease-drug interaction using MIECs. We believe that MIEC-SVM and MIEC-PLS can help design new agents or combination of therapeutic regimens to counter HIV-1 protease resistant strains.
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DOI：
10.1073/pnas.92.7.2484
发表时间：
1995-03-28
影响因子：
11.1
作者：
KEMPF, DJ;MARSH, KC;NORBECK, DW
通讯作者：
NORBECK, DW
影响因子：
2.9
作者：
Chen, YZ;Gu, XL;Cao, ZW
通讯作者：
Cao, ZW
影响因子：
8
作者：
Shenderovich, MD;Kagan, RM;Ramnarayan, K
通讯作者：
Ramnarayan, K
影响因子：
3
作者：
Duan, Y;Wu, C;Kollman, P
通讯作者：
Kollman, P
影响因子：
5.6
作者：
Hou, Tingjun;Zhang, Wei;Wang, Wei
通讯作者：
Wang, Wei