Antibodies often undergo substantial engineering en route to the generation of a therapeutic candidate with good developability properties. Characterization of antibody libraries has shown that retaining native-like sequence improves the overall quality of the library. Motivated by recent advances in deep learning, we developed a bi-directional long short-term memory (LSTM) network model to make use of the large amount of available antibody sequence information, and use this model to quantify the nativeness of antibody sequences. The model scores sequences for their similarity to naturally occurring antibodies, which can be used as a consideration during design and engineering of libraries. We demonstrate the performance of this approach by training a model on human antibody sequences and show that our method outperforms other approaches at distinguishing human antibodies from those of other species. We show the applicability of this method for the evaluation of synthesized antibody libraries and humanization of mouse antibodies.

中文翻译：

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使用长短期记忆网络量化抗体序列的天然性。

在产生具有良好可开发性的治疗候选物的过程中，抗体通常经过大量工程改造。抗体文库的表征表明，保留天然样序列可提高文库的整体质量。基于深度学习的最新进展，我们开发了双向长期短期记忆（LSTM）网络模型，以利用大量可用的抗体序列信息，并使用该模型来量化抗体序列的天然性。该模型对序列与天然存在抗体的相似性进行评分，可以在文库的设计和工程过程中考虑使用。我们通过在人类抗体序列上训练模型来证明这种方法的性能，并表明我们的方法在区分人类抗体与其他物种的抗体方面优于其他方法。我们展示了这种方法的适用性，以评估合成的抗体库和小鼠抗体的人源化。