PDF(Pubmed)
Abstract：
Drug resistance of pathogens, including viruses, is one of the reasons for decreased efficacy of therapy. Considering the impact of HIV type 1 (HIV-1) on the development of progressive immune dysfunction and the rapid development of drug resistance, the analysis of HIV-1 resistance is of high significance. Currently, a substantial amount of data has been accumulated on HIV-1 drug resistance that can be used to build both qualitative and quantitative models of HIV-1 drug resistance. Quantitative models of drug resistance can enrich the information about the efficacy of a particular drug in the scheme of antiretroviral therapy. In our study, we investigated the possibility of developing models for quantitative prediction of HIV-1 resistance to eight protease inhibitors based on the analysis of amino acid sequences of HIV-1 protease for 900 virus variants. We developed random forest regression (RFR), support vector regression (SVR), and self-consistent regression (SCR) models using binary vectors containing values from 0 or 1, depending on the presence of a specific peptide fragment in each amino acid sequence as independent variables, while fold ratio, reflecting the level of resistance, was the predicted variable. The SVR and SCR models showed the highest predictive performances. The models built demonstrate reasonable performances for eight out of nine (R2 varied from 0.828 to 0.909) protease inhibitors, while R2 for predicting tipranavir fold ratio was lower (R2 was 0.642). We believe that the developed approach can be applied to evaluate drug resistance of molecular targets of other viruses where appropriate experimental data are available.
摘要：
病原菌耐药性,包括病毒,是治疗疗效下降的原因之一。考虑到HIV1型（HIV-1）对进行性免疫功能障碍的发展和耐药性的快速发展的影响，HIV-1耐药性的分析具有重要意义。目前,已经积累了大量关于HIV-1耐药性的数据,这些数据可用于构建HIV-1耐药性的定性和定量模型.耐药性的定量模型可以丰富有关抗逆转录病毒治疗方案中特定药物功效的信息。在我们的研究中,我们基于900种病毒变体的HIV-1蛋白酶的氨基酸序列分析,研究了建立定量预测HIV-1对8种蛋白酶抑制剂耐药性的模型的可能性.我们开发了随机森林回归(RFR)，支持向量回归(SVR)，和自洽回归（SCR）模型，使用包含0或1值的二元向量，取决于每个氨基酸序列中特定肽片段的存在作为独立变量，而折叠比，反映了抵抗的程度，是预测变量。SVR和SCR模型显示出最高的预测性能。建立的模型证明了九种蛋白酶抑制剂中的八种（R2从0.828变化到0.909）的合理性能，而预测替普那韦的R2倍数较低（R2为0.642）。我们相信，所开发的方法可以应用于评估其他病毒分子靶标的耐药性，只要有适当的实验数据。
