Nanobody engineering: computational modelling and design for biomedical and therapeutic applications

Area of Science:

• Biotechnology and Biomedical Research
• Immunology and Protein Engineering

Background:

• Nanobodies are the smallest functional antibody fragments, derived from camelid heavy-chain-only antibodies.
• They possess unique structural and functional properties making them valuable for various biomedical applications.

Purpose of the Study:

• To provide a comprehensive review of nanobody structural characteristics, functional properties, and computational design approaches.
• To highlight the advantages of nanobodies over conventional antibodies and discuss advancements in computational methods for their optimization.

Main Methods:

• Review of existing literature on nanobody structure, function, and computational design.
• Analysis of computational methods for nanobody modeling, epitope prediction, and affinity maturation.
• Examination of a case study involving computational design for a nanobody-based immunosensor (Quenchbody).

Main Results:

• Nanobodies exhibit unique antigen-binding domains with critical roles for complementarity-determining regions in target recognition.
• Advantages include small size, stability, and solubility, enabling economical antigen capture for diagnostics, therapeutics, and biosensing.
• Advancements in computational methods facilitate nanobody modeling, epitope prediction, and affinity maturation, improving antigen-binding mechanisms.

Conclusions:

• Nanobodies represent transformative tools in biotechnology and biomedical research.
• Computational design strategies significantly enhance nanobody performance, as exemplified by the Quenchbody.
• This review offers insights and a roadmap for future nanobody applications in healthcare and diagnostics.