Differential performance of RoseTTAFold in antibody modeling.
Differential performance of RoseTTAFold in antibody modeling.
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RoseTTAFold 在抗体建模中的差异化性能。
DOI：
10.1093/bib/bbac152
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发表时间：
2022
影响因子：
9.5
通讯作者：
Feng,Zhiwei
中科院分区：
文献类型：
--
作者：
Liang,Tianjian;Jiang,Chen;Yuan,Jiayi;Othman,Yasmin;Xie,Xiang-Qun;Feng,Zhiwei
抗体对生命至关重要，了解其结构有助于理解抗体-抗原识别机制。精确的抗体结构预测长期以来一直是一个核心挑战，尤其是H3环预测的准确性。尽管最近取得了进展，但现有方法无法实现原子精度，特别是当这些方法所需的同源结构不可用时。最近，基于深度学习的算法RoseTTAFold在预测蛋白质3D结构方面取得了显着的突破。为了评估RoseTTAFold的抗体建模能力，我们首先检索30种抗体的序列作为测试集，并使用RoseTTAFold对其3D结构进行建模。然后，我们以不同的方式将 RoseTTAFold 构建的模型与 SWISS-MODEL 构建的模型进行比较，其中我们将全局模型质量估计 (GMQE) 分层为三个不同的范围。结果表明，RoseTTAFold 在对大多数 CDR 环进行建模时可以取得与 SWISS-MODEL 类似的结果，尤其是 GMQE 分数低于 0.8 的模板。此外，我们还比较了RoseTTAFold、SWISS-MODEL和ABodyBuilder建模的结构。简而言之，RoseTTAFold可以准确预测抗体的3D结构，但其准确性不如其他两种方法。然而，RoseTTAFold 在 H3 循环建模方面表现出比 ABodyBuilder 更高的准确性，并且与 SWISS-MODEL 相当。最后，我们讨论了当前 RoseTTAFold 的局限性和潜在改进，这可能有助于进一步提高 RoseTTAFold 抗体建模的准确性。
Antibodies are essential to life, and knowing their structures can facilitate the understanding of antibody–antigen recognition mechanisms. Precise antibody structure prediction has been a core challenge for a prolonged period, especially the accuracy of H3 loop prediction. Despite recent progress, existing methods cannot achieve atomic accuracy, especially when the homologous structures required for these methods are not available. Recently, RoseTTAFold, a deep learning-based algorithm, has shown remarkable breakthroughs in predicting the 3D structures of proteins. To assess the antibody modeling ability of RoseTTAFold, we first retrieved the sequences of 30 antibodies as the test set and used RoseTTAFold to model their 3D structures. We then compared the models constructed by RoseTTAFold with those of SWISS-MODEL in a different way, in which we stratified Global Model Quality Estimate (GMQE) into three different ranges. The results indicated that RoseTTAFold could achieve results similar to SWISS-MODEL in modeling most CDR loops, especially the templates with a GMQE score under 0.8. In addition, we also compared the structures modeled by RoseTTAFold, SWISS-MODEL and ABodyBuilder. In brief, RoseTTAFold could accurately predict 3D structures of antibodies, but its accuracy was not as good as the other two methods. However, RoseTTAFold exhibited better accuracy for modeling H3 loop than ABodyBuilder and was comparable to SWISS-MODEL. Finally, we discussed the limitations and potential improvements of the current RoseTTAFold, which may help to further the accuracy of RoseTTAFold’s antibody modeling.