ModiBodies: A computational method for modifying nanobodies in nanobody-antigen complexes to improve binding affinity...

Area of Science:

• Biotechnology
• Structural Biology
• Immunology

Background:

• Nanobodies are single-domain antibody fragments with high stability and compact size, making them valuable for antigen recognition.
• Their complementarity-determining regions (CDRs) can be engineered to mimic human immunoglobulin binding surfaces.

Purpose of the Study:

• To develop and validate a benchmark algorithm for optimizing nanobody binding affinity.
• To identify optimal amino acid mutations within CDRs for enhanced protein-nanobody interactions.

Main Methods:

• Utilized 3D structures of existing protein-nanobody complexes as starting points.
• Employed successive mutations on CDR domains and molecular dynamics simulations to assess binding energies.
• Validated the algorithm using benchmark complexes: MDM4-VH9, fructose-bisphosphate aldolase, and human lysozyme.

Main Results:

• The algorithm successfully identified mutations that improved binding energies compared to known complexes.
• Demonstrated the potential for generating improved nanobodies more efficiently.

Conclusions:

• The proposed algorithm offers a rapid and efficient method for designing high-affinity nanobodies.
• This computational approach complements traditional experimental methods, reducing the need for extensive libraries and experimentation.