Discrete analysis of camelid variable domains: sequences, structures, and in-silico structure prediction - Inserm - Institut national de la santé et de la recherche médicale
Article Dans Une Revue PeerJ Année : 2020

Discrete analysis of camelid variable domains: sequences, structures, and in-silico structure prediction

Jean-Philippe Meyniel
  • Fonction : Auteur
Alain Malpertuy
  • Fonction : Auteur

Résumé

Antigen binding by antibodies requires precise orientation of the complementarity- determining region (CDR) loops in the variable domain to establish the correct contact surface. Members of the family Camelidae have a modified form of immunoglobulin gamma (IgG) with only heavy chains, called Heavy Chain only Antibodies (HCAb). Antigen binding in HCAbs is mediated by only 3 CDR loops from the single variable domain (VHH) at the N-terminus of each heavy chain. This feature of the VHH, along with other important features, e.g. easy expression, small size, thermo-stability and hydrophilicity, made them promising candidates for therapeutics and diagnostics. Thus, to design better VHH domains, it is important to thoroughly understand their sequence and structure characteristics and relationships. In this study sequence, characteristics of VHH have been analysed in depth, along with their structural features using innovative approaches, namely a structural alphabet. An elaborate summary of various studies proposing structural models of VHHs showed diversity in the algorithms used. Finally, a case study to elucidate the differences in structural models from single and multiple templates is presented. In this case study, along with the above-mentioned aspects of VHH, an exciting view of various factors in structure prediction of VHH, like template framework selection, is also discussed.
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Dates et versions

inserm-02907323 , version 1 (27-07-2020)

Identifiants

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Akhila Melarkode Vattekatte, Nicolas Ken Shinada, Tarun J Narwani, Floriane Noël, Olivier Bertrand, et al.. Discrete analysis of camelid variable domains: sequences, structures, and in-silico structure prediction. PeerJ, 2020, 8, pp.e8408. ⟨10.7717/peerj.8408⟩. ⟨inserm-02907323⟩
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