AbLang: An antibody language model for completing antibody sequences
AbLang: An antibody language model for completing antibody sequences
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AbLang：用于完成抗体序列的抗体语言模型
DOI：
10.1101/2022.01.20.477061
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Olsen T
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文献类型：
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作者：
Olsen T
一般的蛋白质语言模型已经被证明可以将蛋白质序列的语义总结为对最先进的预测方法有用的表示。然而，对于抗体特异性问题，例如恢复由于测序错误而丢失的残基，仅对抗体进行训练的模型可能更强大。抗体是为数不多的几种蛋白质类型之一，这种语言模型需要大量的序列数据，例如在观察抗体空间（OAS）数据库中。结果介绍了一种基于OAS数据库中抗体序列训练的语言模型AbLang。我们通过使用AbLang来恢复抗体序列数据中缺失的残基，这是b细胞受体库测序的一个关键问题，例如，超过40%的OAS序列缺少前15个氨基酸，从而证明了AbLang的功能。与使用IMGT种系或通用蛋白语言模型ESM-1b相比，AbLang能更好地恢复缺失的抗体序列残基。此外，AbLang不需要了解抗体的种系，并且比ESM-1b快7倍。可用性和实现ablang是一个python包，可在https://github.com/oxpig/AbLang.Supplementary上获得information补充数据可在bioinformatics Advancesonline上获得。
MotivationGeneral protein language models have been shown to summarize the semantics of protein sequences into representations that are useful for state-of-the-art predictive methods. However, for antibody specific problems, such as restoring residues lost due to sequencing errors, a model trained solely on antibodies may be more powerful. Antibodies are one of the few protein types where the volume of sequence data needed for such language models is available, e.g. in the Observed Antibody Space (OAS) database.ResultsHere, we introduce AbLang, a language model trained on the antibody sequences in the OAS database. We demonstrate the power of AbLang by using it to restore missing residues in antibody sequence data, a key issue with B-cell receptor repertoire sequencing, e.g. over 40% of OAS sequences are missing the first 15 amino acids. AbLang restores the missing residues of antibody sequences better than using IMGT germlines or the general protein language model ESM-1b. Further, AbLang does not require knowledge of the germline of the antibody and is seven times faster than ESM-1b.Availability and implementationAbLang is a python package available at https://github.com/oxpig/AbLang.Supplementary informationSupplementary data are available atBioinformatics Advancesonline.