VHH Structural Modelling Approaches: A Critical Review - Université Paris Cité Access content directly
Journal Articles International Journal of Molecular Sciences Year : 2022

VHH Structural Modelling Approaches: A Critical Review


VHH, 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 VHH 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 VHHs 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 VHH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of VHHs. 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 VHH 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 VHH from its sequence.
Fichier principal
Vignette du fichier
Vishwakarma_Vattekatte_Shinada et al._Article_VHH Structural..._2022.pdf (2.54 Mo) Télécharger le fichier
Origin : Publisher files allowed on an open archive

Dates and versions

hal-03665842 , version 1 (12-05-2022)





Poonam Vishwakarma, Akhila Melarkode Vattekatte, Nicolas Shinada, Julien Diharce, Carla Martins, et al.. VHH Structural Modelling Approaches: A Critical Review. International Journal of Molecular Sciences, 2022, 23 (7), pp.3721. ⟨10.3390/ijms23073721⟩. ⟨hal-03665842⟩
65 View
153 Download



Gmail Facebook Twitter LinkedIn More