ABSTRACT

Antibodies are one of the predominant treatment modalities for various diseases. To improve the characteristics of a lead antibody, such as antigen-binding affinity and stability, we conducted comprehensive substitutions and exhaustively explored their sequence space. However, it is practically unfeasible to evaluate all possible combinations of mutations owing to combinatorial explosion when multiple amino acid residues are incorporated. It was recently reported that a machine-learning guided protein engineering approach such as Thompson sampling (TS) has been used to efficiently explore sequence space in the framework of Bayesian optimization. For TS, over-exploration occurs when the initial data are biasedly distributed in the vicinity of the lead antibody. We handle a large-scale virtual library that includes numerous mutations. When the number of experiments is limited, this over-exploration causes a serious issue. Thus, we conducted Monte Carlo Thompson sampling (MTS) to balance the exploration-exploitation trade-off by defining the posterior distribution via the Monte Carlo method and compared its performance with TS in antibody engineering. Our results demonstrated that MTS largely outperforms TS in discovering desirable candidates at an earlier round when over-exploration occurs on TS. Thus, the MTS method is a powerful technique for efficiently discovering antibodies with desired characteristics when the number of rounds is limited.

摘要

抗体是多种疾病的主要治疗方式之一。为了改善先导抗体的特性，例如抗原结合亲和力和稳定性，我们进行了全面的替换并详尽地探索了它们的序列空间。然而，由于掺入多个氨基酸残基时的组合爆炸，评估所有可能的突变组合实际上是不可行的。最近有报道称，汤普森采样（TS）等机器学习引导的蛋白质工程方法已被用于在贝叶斯优化框架下有效地探索序列空间。对于 TS，当初始数据偏向分布在先导抗体附近时，就会发生过度探索。我们处理一个包含大量突变的大型虚拟库。当实验数量有限时，这种过度探索会导致严重的问题。因此，我们进行了蒙特卡罗汤普森采样（MTS），通过蒙特卡罗方法定义后验分布来平衡探索与利用的权衡，并将其与抗体工程中的 TS 性能进行比较。我们的结果表明，当 TS 发生过度探索时，MTS 在早期发现理想候选者方面远远优于 TS。因此，MTS 方法是一种在轮数有限的情况下有效发现具有所需特性的抗体的强大技术。