Repertoire Builder: high-throughput structural modeling of B and T cell receptors
Repertoire Builder: high-throughput structural modeling of B and T cell receptors
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DOI：
10.1039/c9me00020h
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发表时间：
2019-08-01
影响因子：
3.6
通讯作者：
Standley, Daron M.
中科院分区：
文献类型：
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作者：
Schritt, Dimitri;Li, Songling;Standley, Daron M.
Repertoire Builder（https：//sysimm.org/rep_builder/）是一种用于从B细胞受体（BCR）或T细胞受体（TCR）的氨基酸序列生成其原子分辨率三维模型的方法。它目前能够在大约30分钟内处理多达10（4）个序列的批次。这种性能是通过应用最初开发用于系统发育分析的多序列比对延伸技术来实现的互补决定区（CDR）环的模板选择问题。在可比条件下，BCR中CDRH 3环的实验确定结构的平均全原子均方根偏差（RMSD）显著低于测试的第三方高通量建模方法，包括ABodyBuilder，PigsPro和LYRA。对于TCR，当将Repertoire Builder与TCR模型和LYRA进行比较时，观察到类似的趋势。我们还发现，即使使用CDRH 3环细化，Repertoire Builder模型误差通常也低于我们早期的Kotai Antibody Builder产生的误差。然而，在一部分病例中，可以通过Repertoire Builder评分较差来区分，Kotai Antibody Builder或Rosetta Antibody（两者都利用广泛的结构抽样）的改进平均改善了第三条重链CDR（CDRH 3）RMSD。总之，这些结果表明，与其他方法相比，Repertoire Builder使用的MSA扩展方法在速度和准确性之间取得了有利的平衡。此外，我们的结论是，更敏感的评分，而不是扩展的结构抽样，需要进一步提高BCR和TCR建模的准确性。
Repertoire Builder (https://sysimm.org/rep_builder/) is a method for generating atomic-resolution, three-dimensional models of B cell receptors (BCRs) or T cell receptors (TCRs) from their amino acid sequences. It is currently capable of handling batches of up to 10(4) sequences in approximately 30 minutes. This performance was achieved by applying a multiple sequence alignment extension technique originally developed for phylogenetic analysis to the template selection problem of complementarity determining region (CDR) loops. Under comparable conditions, average all-atom root-mean square deviations (RMSDs) from experimentally-determined structures of CDRH3 loops in BCRs were significantly lower than tested third-party high-throughput modeling methods, including ABodyBuilder, PigsPro, and LYRA. For TCRs, similar trends were observed when Repertoire Builder was compared with TCRmodel and LYRA. We also found that Repertoire Builder model errors were, in general, lower than those produced by our earlier Kotai Antibody Builder, even when CDRH3 loop refinement was used. However, in a subset of cases, which could be distinguished by poor Repertoire Builder scores, refinement by Kotai Antibody Builder or Rosetta Antibody, both of which utilize extensive structural sampling, improved the third heavy chain CDR (CDRH3) RMSD on average. Taken together, these results indicate that the MSA extension approach used by Repertoire Builder resulted in a favorable balance between speed and accuracy when compared to alternative methods. Furthermore, we conclude that more sensitive scoring, rather than extended structural sampling, is needed to further improve the accuracy of BCR and TCR modeling.