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Related Experiment Video

Updated: Aug 9, 2025

Scalable High Throughput Selection From Phage-displayed Synthetic Antibody Libraries

Scalable High Throughput Selection From Phage-displayed Synthetic Antibody Libraries

Published on: January 17, 2015

Toward real-world automated antibody design with combinatorial Bayesian optimization.

Asif Khan¹, Alexander I Cowen-Rivers², Antoine Grosnit³

¹School of Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK.

Cell Reports Methods

|February 23, 2023

Summary

Designing therapeutic antibodies is advanced using AntBO, a computational framework. This method efficiently identifies optimal complementarity determining region 3 (CDRH3) sequences for high binding affinity and developability, outperforming extensive experimental methods.

Keywords:

Bayesian optimization Gaussian processes combinatorial Bayesian optimization computational antibody design machine learning protein engineering structural biology

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Published on: January 14, 2020

Area of Science:

Immunology and Biochemistry
Computational Biology and Bioinformatics
Protein Engineering and Antibody Design

Background:

Antibodies are crucial for molecular recognition, with the complementarity determining region 3 (CDRH3) of the variable heavy chain largely dictating antigen specificity.
Developing therapeutic antibodies requires precise CDRH3 design for optimal antigen binding, high specificity, and good developability.
The vast sequence space of CDRH3 makes exhaustive searching for optimal binders computationally infeasible.

Purpose of the Study:

To introduce AntBO, a combinatorial Bayesian optimization framework for *in silico* antibody design.
To leverage a CDRH3 trust region within AntBO to optimize antibody developability scores.
To demonstrate AntBO's capability in designing high-affinity and developable antibodies computationally.

Main Methods:

Development of AntBO, a combinatorial Bayesian optimization framework.
Integration of a CDRH3 trust region to guide the design process towards favorable developability.
Application of AntBO for *in silico* antibody design against 159 different antigens.

Main Results:

AntBO successfully designed antibodies with superior binding sequences compared to millions of experimentally derived CDRH3s, achieved in under 200 computational calls.
The framework identified highly potent CDRH3 sequences with remarkable affinity in as few as 38 protein designs.
AntBO demonstrated effectiveness without requiring prior domain-specific knowledge, highlighting its versatility.

Conclusions:

AntBO represents a significant advancement towards practical and efficient *in vitro* antibody design through computational methods.
The framework accelerates the discovery of therapeutic antibodies with enhanced specificity, affinity, and developability.
AntBO's ability to navigate complex sequence spaces efficiently makes it a valuable tool for antibody engineering.