Language model-based B cell receptor sequence embeddings can effectively encode receptor specificity.
Language model-based B cell receptor sequence embeddings can effectively encode receptor specificity.
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DOI：
10.1093/nar/gkad1128
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发表时间：
2024-01-25
影响因子：
14.9
通讯作者：
中科院分区：
文献类型：
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作者：
B细胞受体（bcr）的高通量测序越来越多地应用于研究抗体的巨大多样性。学习有生物学意义的BCR序列嵌入有利于预测建模。针对bcr已经开发了几种嵌入方法，但没有直接的性能基准测试。此外，输入序列长度和配对链信息对预测的影响还有待探讨。我们评估了多个嵌入模型在预测BCR序列特性和受体特异性方面的性能。尽管模型架构存在差异，但大多数嵌入都能有效捕获BCR序列的属性和特异性。bcr特异性嵌入在预测特异性方面略优于一般的蛋白质语言模型。此外，结合全长重链和配对轻链序列，提高了所有嵌入的预测性能。本研究提供了对BCR嵌入特性的见解，以改善抗体分析和发现的下游预测应用。
High throughput sequencing of B cell receptors (BCRs) is increasingly applied to study the immense diversity of antibodies. Learning biologically meaningful embeddings of BCR sequences is beneficial for predictive modeling. Several embedding methods have been developed for BCRs, but no direct performance benchmarking exists. Moreover, the impact of the input sequence length and paired-chain information on the prediction remains to be explored. We evaluated the performance of multiple embedding models to predict BCR sequence properties and receptor specificity. Despite the differences in model architectures, most embeddings effectively capture BCR sequence properties and specificity. BCR-specific embeddings slightly outperform general protein language models in predicting specificity. In addition, incorporating full-length heavy chains and paired light chain sequences improves the prediction performance of all embeddings. This study provides insights into the properties of BCR embeddings to improve downstream prediction applications for antibody analysis and discovery.
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影响因子：
8.7
作者：
Corrie BD;Marthandan N;Zimonja B;Jaglale J;Zhou Y;Barr E;Knoetze N;Breden FMW;Christley S;Scott JK;Cowell LG;Breden F
通讯作者：
Breden F
DOI：
10.1002/pro.4205
发表时间：
2022-01
期刊：
Protein science : a publication of the Protein Society
影响因子：
--
作者：
Olsen TH;Boyles F;Deane CM
通讯作者：
Deane CM
影响因子：
64.8
作者：
Turner JS;Zhou JQ;Han J;Schmitz AJ;Rizk AA;Alsoussi WB;Lei T;Amor M;McIntire KM;Meade P;Strohmeier S;Brent RI;Richey ST;Haile A;Yang YR;Klebert MK;Suessen T;Teefey S;Presti RM;Krammer F;Kleinstein SH;Ward AB;Ellebedy AH
通讯作者：
Ellebedy AH
DOI：
10.1109/tpami.2021.3095381
发表时间：
2022-10-01
影响因子：
23.6
作者：
Elnaggar, Ahmed;Heinzinger, Michael;Rost, Burkhard
通讯作者：
Rost, Burkhard
影响因子：
64.8
作者：
通讯作者：
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