The successful discovery of novel biological therapeutics by selection requires highly diverse libraries of candidate sequences that contain a high proportion of desirable candidates. Here we propose the use of computationally designed factorizable libraries made of concatenated segment libraries as a method of creating large libraries that meet an objective function at low cost. We show that factorizable libraries can be designed efficiently by representing objective functions that describe sequence optimality as an inner product of feature vectors, which we use to design an optimization method we call stochastically annealed product spaces (SAPS). We then use this approach to design diverse and efficient libraries of antibody CDR-H3 sequences with various optimized characteristics.

中文翻译：

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用于高效治疗发现的超高多样性可分解文库


通过选择成功发现新型生物疗法需要高度多样化的候选序列文库，其中包含高比例的所需候选序列。在这里，我们建议使用由连接段库组成的计算设计的可分解库作为以低成本创建满足目标函数的大型库的方法。我们证明，通过将目标函数表示为特征向量的内积来描述序列最优性，可以有效地设计可分解库，我们用它来设计一种称为随机退火乘积空间（SAPS）的优化方法。然后，我们使用这种方法设计具有各种优化特性的多样化、高效的抗体 CDR-H3 序列文库。