Development and experimental validation of computational methods for human antibody affinity enhancement.

Area of Science:

• Biochemistry
• Computational Biology
• Immunology

Background:

• Antibody affinity is critical for therapeutic efficacy and specificity.
• Traditional antibody affinity maturation is laborious, time-consuming, and has a low success rate.
• Existing computational methods struggle to identify beneficial mutations in antibody complementarity-determining regions (CDRs), often leading to expression and immunogenicity issues.

Purpose of the Study:

• To develop effective computational methods for enhancing antibody affinity against avian influenza virus.
• To pinpoint beneficial mutations within CDRs while mitigating expression and immunogenicity challenges.
• To accelerate the antibody design process through precise computational assistance.

Main Methods:

• Construction of a complementarity-determining region (CDR) library with evolutionary information guiding mutation positions and types.
• Development of a statistical potential methodology based on antibody-antigen amino acid interactions to predict affinity-enhanced antibodies.
• Application of molecular dynamics simulations and experimental validation for identified mutations.

Main Results:

• A point mutation was identified that enhanced antibody affinity 2.5-fold, achieving 2 nM affinity.
• A predictive model for antibody-antigen interactions demonstrated strong performance with an AUC of 0.83 and precision of 0.89.
• A novel approach combining affinity-enhancing mutations and iterative optimization (similar to Monte Carlo) was proposed.

Conclusions:

• The study presents effective computational methods for rapid and accurate antibody affinity enhancement.
• The developed approach addresses critical issues of antibody expression and immunogenicity.
• This work significantly advances computational-assisted antibody design for improved therapeutic development.