Antibodies are canonically Y-shaped multimeric proteins capable of highly
specific molecular recognition. The CDRH3 region located at the tip of variable
chains of an antibody dominates antigen-binding specificity. Therefore, it is a
priority to design optimal antigen-specific CDRH3 regions to develop
therapeutic antibodies. However, the combinatorial nature of CDRH3 sequence
space makes it impossible to search for an optimal binding sequence
exhaustively and efficiently using computational approaches. Here, we present
\texttt{AntBO}: a combinatorial Bayesian optimisation framework enabling
efficient \textit{in silico} design of the CDRH3 region. Ideally, antibodies
are expected to have high target specificity and developability. We introduce a
CDRH3 trust region that restricts the search to sequences with favourable
developability scores to achieve this goal. For benchmarking, \texttt{AntBO}
uses the \texttt{Absolut!} software suite as a black-box oracle to score the
target specificity and affinity of designed antibodies \textit{in silico} in an
unconstrained fashion~\citep{robert2021one}. The experiments performed for
$159$ discretised antigens used in \texttt{Absolut!} demonstrate the benefit of
\texttt{AntBO} in designing CDRH3 regions with diverse biophysical properties.
In under $200$ calls to black-box oracle, \texttt{AntBO} can suggest antibody
sequences that outperform the best binding sequence drawn from 6.9 million
experimentally obtained CDRH3s and a commonly used genetic algorithm baseline.
Additionally, \texttt{AntBO} finds very-high affinity CDRH3 sequences in only
38 protein designs whilst requiring no domain knowledge. We conclude
\texttt{AntBO} brings automated antibody design methods closer to what is
practically viable for in vitro experimentation.

提出了用于优化抗体 CDRH3 区域的贝叶斯优化框架 AntBO，该框架使用 Absolut！软件来评估抗体对特定抗原的特异性和亲和力，并通过引入 CDRH3 信任区域来限制具有有利发展性得分的序列搜索，最终在仅使用少量黑盒函数调用的情况下，在 159 个抗原和 38 个蛋白质设计中发现了高达 69 万个实验得到的序列和常用遗传算法基线的最佳结合序列。