INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France.
INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France.
Int J Mol Sci. 2022 Mar 28;23(7):3721. doi: 10.3390/ijms23073721.
VH, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies. The number of experimentally solved VH structures has significantly improved recently, which is of great help, because it offers the ability to directly work on 3D structures to humanise or improve them. Unfortunately, most VHs do not have 3D structures. Thus, it is essential to find alternative ways to get structural information. The methods of structure prediction from the primary amino acid sequence appear essential to bypass this limitation. This review presents the most extensive overview of structure prediction methods applied for the 3D modelling of a given VH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of VHs. A brief explanation of each methodology is supplied, and pertinent examples of their usage are provided. Finally, we present a structure prediction case study of a recently solved VH structure. According to some recent studies and the present analysis, AlphaFold 2 and NanoNet appear to be the best tools to predict a structural model of VH from its sequence.
VH，即骆驼科单链抗体的 VH 结构域，由于与经典的哺乳动物抗体相比具有显著的物理化学优势，因此是非常有前途的治疗剂。最近，实验解决的 VH 结构数量显著增加，这非常有帮助，因为它提供了直接处理 3D 结构以对其进行人源化或改进的能力。不幸的是，大多数 VH 没有 3D 结构。因此，找到获取结构信息的替代方法至关重要。从一级氨基酸序列预测结构的方法似乎是克服这一限制的必要手段。本综述对应用于给定 VH 序列的 3D 建模的结构预测方法进行了最广泛的概述（总共 21 种）。除了历史概述外，它还旨在展示模型软件程序如何塑造 VH 的结构预测。对每种方法都提供了简要说明，并提供了它们使用的相关示例。最后，我们展示了最近解决的 VH 结构的结构预测案例研究。根据一些最新的研究和本分析，AlphaFold 2 和 NanoNet 似乎是从序列预测 VH 结构模型的最佳工具。
