Department of Bioinformatics, Institute of Biomedical Chemistry, 10-7, Pogodinskaya Street, Moscow 119121, Russia.
Viruses. 2024 Jul 15;16(7):1132. doi: 10.3390/v16071132.
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 (R varied from 0.828 to 0.909) protease inhibitors, while R for predicting tipranavir fold ratio was lower (R 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.
病原体（包括病毒）的耐药性是治疗效果降低的原因之一。鉴于 HIV 1 型（HIV-1）对进行性免疫功能障碍发展的影响以及耐药性的快速发展，分析 HIV-1 耐药性具有重要意义。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。耐药性定量模型可以丰富特定药物在抗逆转录病毒治疗方案中的疗效信息。在我们的研究中，我们基于对 900 种病毒变异株的 HIV-1 蛋白酶氨基酸序列的分析，研究了开发基于 8 种蛋白酶抑制剂的 HIV-1 耐药性定量预测模型的可能性。我们使用包含 0 或 1 值的二进制向量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，这些二进制向量的独立变量取决于每个氨基酸序列中是否存在特定的肽片段，而反映耐药水平的预测变量是比值比。SVR 和 SCR 模型表现出最高的预测性能。对于八种（R 从 0.828 到 0.909）蛋白酶抑制剂中的九种，所建立的模型表现出合理的性能，而预测替拉那韦比值比的 R 值较低（R 为 0.642）。我们相信，所开发的方法可以应用于评估其他病毒的分子靶标耐药性，只要有适当的实验数据。
