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VHH Structural Modelling Approaches: A Critical Review.

Poonam Vishwakarma1,2, Akhila Melarkode Vattekatte1,2, Nicolas Shinada3

  • 1INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France.

International Journal of Molecular Sciences
|April 12, 2022
PubMed
Summary

Camelid single-chain antibodies (VHH) show therapeutic promise. This review details 21 structure prediction methods, identifying AlphaFold 2 and NanoNet as top tools for modeling VHH structures from amino acid sequences.

Keywords:
Complementarity Determining Regionsantibodiesdeep learningdockingframeworkshomology and comparative modellingsecondary structuresequence–structure relationshipsingle-domain antibodythreading

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Area of Science:

  • Biotechnology
  • Structural Biology
  • Immunology

Background:

  • Camelid single-chain antibodies (VHH) offer physicochemical advantages over traditional antibodies, making them promising therapeutic agents.
  • While experimental VHH structures aid in their improvement, most lack available 3D structural data.
  • Predicting VHH structures from amino acid sequences is crucial for overcoming data limitations.

Purpose of the Study:

  • To provide a comprehensive overview of VHH structure prediction methods.
  • To analyze the evolution and impact of modeling software on VHH structural predictions.
  • To evaluate the efficacy of various structure prediction tools for VHH sequences.

Main Methods:

  • Review and analysis of 21 different VHH structure prediction methodologies.
  • Historical overview of structure prediction techniques.
  • Case study of a recent VHH structure prediction.

Main Results:

  • Identification of AlphaFold 2 and NanoNet as the most effective tools for VHH 3D structure modeling.
  • Demonstration of how software has advanced VHH structural prediction.
  • Detailed explanations and examples of diverse prediction methodologies.

Conclusions:

  • Structure prediction is essential for advancing VHH therapeutic applications.
  • AlphaFold 2 and NanoNet represent the current state-of-the-art for VHH sequence-based structure modeling.
  • Further research and application of these prediction tools will facilitate VHH development.