Inference and design of antibody specificity: From experiments to models and back.

Area of Science:

• Biotechnology
• Protein Engineering
• Immunology

Background:

• Achieving high binding specificity in proteins is crucial for biotechnology and medicine but remains a significant engineering challenge.
• Existing experimental methods like in vitro selection have limitations in library size and specificity control.
• High-throughput sequencing and computational analysis offer enhanced control over antibody specificity.

Purpose of the Study:

• To computationally design antibodies with customized specificity profiles.
• To demonstrate the ability to discriminate between chemically similar ligands and epitopes.
• To engineer antibodies for specific high affinity or cross-specificity.

Main Methods:

• Utilizing phage display experiments and high-throughput sequencing.
• Applying a biophysical model to identify and disentangle different antibody-ligand binding modes.
• Computational design informed by experimental selection data against multiple ligands.

Main Results:

• The computational model successfully distinguished binding modes for chemically similar ligands.
• Experimentally validated the design of antibodies with customized specificity.
• Demonstrated the creation of antibodies with specific high affinity or broad cross-specificity.

Conclusions:

• Leveraging biophysical models learned from multi-ligand selections enables the design of proteins with tailored specificity.
• This approach extends the capabilities of protein engineering beyond antibody design.
• Offers a powerful strategy for developing highly specific binders for diverse applications.