A consensus protocol for the in silico optimisation of antibody fragments

Area of Science:

• Biochemistry
• Computational Biology
• Immunology

Background:

• Single domain antibodies (nanobodies, VHHs) are valuable tools in research and therapeutics.
• Improving the binding affinity of VHHs is crucial for their efficacy.
• Current optimization methods can be labor-intensive and time-consuming.

Purpose of the Study:

• To present a novel in silico mutagenetic protocol for enhancing the binding affinity of single domain antibodies.
• To validate the protocol's effectiveness using an anti-HER2 VHH as a benchmark.
• To establish a computational framework for antibody optimization.

Main Methods:

• Iterative in silico mutagenesis of the antibody's interacting region.
• Evaluation of binding affinity using multiple scoring functions on molecular dynamics trajectories.
• Consensus-based decision-making algorithm for mutation acceptance/rejection.
• Benchmarking on an anti-HER2 VHH using a complementary determining region (CDR) evolution.

Main Results:

• The optimized VHH mutant demonstrated significantly enhanced experimental binding affinity compared to the wild-type.
• The in silico protocol successfully improved the VHH's affinity through targeted mutations.
• The method provides a reliable and efficient approach for VHH optimization.

Conclusions:

• The presented in silico mutagenetic protocol is effective for improving single domain antibody binding affinity.
• This computational approach can be applied to optimize various protein binders, including peptides and antibody fragments.
• The protocol offers a powerful tool for accelerating antibody engineering and drug discovery efforts.