Department of Bioinformatics, Center of Medical Biotechnology, University of Duisburg-Essen, Universitaetsstr. 2, 45117 Essen, Germany.
BMC Bioinformatics. 2010 Jan 20;11:37. doi: 10.1186/1471-2105-11-37.
Maturation inhibitors are a new class of antiretroviral drugs. Bevirimat (BVM) was the first substance in this class of inhibitors entering clinical trials. While the inhibitory function of BVM is well established, the molecular mechanisms of action and resistance are not well understood. It is known that mutations in the regions CS p24/p2 and p2 can cause phenotypic resistance to BVM. We have investigated a set of p24/p2 sequences of HIV-1 of known phenotypic resistance to BVM to test whether BVM resistance can be predicted from sequence, and to identify possible molecular mechanisms of BVM resistance in HIV-1.
We used artificial neural networks and random forests with different descriptors for the prediction of BVM resistance. Random forests with hydrophobicity as descriptor performed best and classified the sequences with an area under the Receiver Operating Characteristics (ROC) curve of 0.93 +/- 0.001. For the collected data we find that p2 sequence positions 369 to 376 have the highest impact on resistance, with positions 370 and 372 being particularly important. These findings are in partial agreement with other recent studies. Apart from the complex machine learning models we derived a number of simple rules that predict BVM resistance from sequence with surprising accuracy. According to computational predictions based on the data set used, cleavage sites are usually not shifted by resistance mutations. However, we found that resistance mutations could shorten and weaken the alpha-helix in p2, which hints at a possible resistance mechanism.
We found that BVM resistance of HIV-1 can be predicted well from the sequence of the p2 peptide, which may prove useful for personalized therapy if maturation inhibitors reach clinical practice. Results of secondary structure analysis are compatible with a possible route to BVM resistance in which mutations weaken a six-helix bundle discovered in recent experiments, and thus ease Gag cleavage by the retroviral protease.
成熟抑制剂是一类新的抗逆转录病毒药物。贝伐单抗(BVM)是该类抑制剂中第一个进入临床试验的物质。虽然 BVM 的抑制功能已经得到很好的证实,但作用机制和耐药性的分子机制还不是很清楚。已知 CS p24/p2 和 p2 区域的突变可导致对 BVM 的表型耐药。我们已经研究了一组已知对 BVM 表现出表型耐药的 HIV-1 p24/p2 序列,以测试是否可以从序列预测 BVM 耐药性,并确定 HIV-1 中 BVM 耐药性的可能分子机制。
我们使用人工神经网络和不同描述符的随机森林来预测 BVM 耐药性。以疏水性为描述符的随机森林表现最佳,其对序列的分类在接收器操作特征(ROC)曲线下的面积为 0.93 +/- 0.001。对于收集的数据,我们发现 p2 序列位置 369 到 376 对耐药性的影响最大,位置 370 和 372 特别重要。这些发现与其他最近的研究部分一致。除了复杂的机器学习模型外,我们还得出了一些简单的规则,可以根据序列准确预测 BVM 耐药性。根据使用的数据集进行的计算预测,切割位点通常不会因耐药突变而移位。然而,我们发现耐药突变可以缩短和削弱 p2 中的α-螺旋,这暗示了一种可能的耐药机制。
我们发现可以从 p2 肽的序列很好地预测 HIV-1 中的 BVM 耐药性,如果成熟抑制剂进入临床实践,这可能对个体化治疗有用。二级结构分析的结果与一种可能的耐药途径相兼容,在该途径中,突变削弱了最近实验中发现的六螺旋束,从而减轻了逆转录病毒蛋白酶对 Gag 的切割。