Human T-cell Leukemia Virus type 1 (HTLV-1) is a human retrovirus responsible for leukaemia in 5 to 10% of infected individuals. Among the viral proteins, Tax has been described as directly involved in virus-induced leukemogenesis. Tax is therefore an interesting therapeutic target. However, its 3D structure is still unknown and this hampers the development of drug-design-based therapeutic strategies. Several algorithms are available that can be used to predict the structure of proteins, particularly with the recent appearance of artificial intelligence (AI)-driven pipelines. Here, we review how the structure of Tax is predicted by several algorithms using distinct modelling strategies. We discuss the consequences for the understanding of Tax structure/function relationship, and more generally for the use of structure models for modular and/or flexible proteins, which are frequent in retroviruses.

中文翻译：

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“这只是一个模型”：当蛋白质结构预测需要实验验证时，HTLV-1 Tax Protein 的案例

人类 T 细胞白血病病毒 1 型 (HTLV-1) 是一种人类逆转录病毒，导致 5% 至 10% 的感染者罹患白血病。在病毒蛋白中，Tax 被描述为直接参与病毒诱导的白血病发生。因此，税收是一个有趣的治疗目标。然而，其 3D 结构仍然未知，这阻碍了基于药物设计的治疗策​​略的发展。有多种算法可用于预测蛋白质的结构，特别是随着最近人工智能 (AI) 驱动管道的出现。在这里，我们回顾了如何使用不同的建模策略通过几种算法来预测税收的结构。我们讨论了理解Tax结构/功能关系的后果，更广泛地讨论了模块化和/或柔性蛋白质结构模型的使用的后果，这在逆转录病毒中很常见。