Protein therapeutic design and property prediction are frequently hampered by data scarcity. Here we propose a new model, DyAb, that addresses these issues by leveraging a pair-wise representation to predict differences in protein properties, rather than absolute values. DyAb is built on top of a pre-trained protein language model and achieves a Spearman rank correlation of up to 0.85 on binding affinity prediction across molecules targeting three different antigens (EGFR, IL-6, and an internal target), given as few as 100 training data. We employ DyAb in two design contexts: as a ranking model to score combinations of known mutations, and combined with a genetic algorithm to generate new sequences. Our method consistently generates novel antibody candidates with high binding rates, including designs that improve on the binding affinity of the lead molecule by more than ten-fold. DyAb represents a powerful tool for engineering therapeutic protein properties in low data regimes common in early-stage drug development.

中文翻译：

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DyAb：在低数据范围内进行基于序列的抗体设计和性质预测


蛋白质治疗设计和特性预测经常受到数据稀缺的阻碍。在这里，我们提出了一种新模型 DyAb，它通过利用成对表示来预测蛋白质特性的差异，而不是绝对值来解决这些问题。DyAb 建立在预先训练的蛋白质语言模型之上，在给定 100 个训练数据的情况下，在靶向三种不同抗原（EGFR、IL-6 和内部靶标）的分子之间的结合亲和力预测中实现了高达 0.85 的 Spearman 秩相关性。我们在两种设计环境中使用 DyAb：作为排名模型对已知突变的组合进行评分，并与遗传算法相结合以生成新序列。我们的方法始终如一地生成具有高结合率的新型候选抗体，包括将先导分子的结合亲和力提高十倍以上的设计。DyAb 是一种强大的工具，可在早期药物开发中常见的低数据状态下设计治疗性蛋白质特性。