NanoNet: Rapid and accurate end-to-end nanobody modeling by deep learning.
NanoNet: Rapid and accurate end-to-end nanobody modeling by deep learning.
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DOI：
10.3389/fimmu.2022.958584
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发表时间：
2022
影响因子：
7.3
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中科院分区：
文献类型：
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抗体是一类快速增长的治疗药物。最近，单结构域骆驼科VHH抗体及其识别纳米抗体结构域（Nb）作为全长抗体的具有成本效益的高度稳定的替代物出现。越来越需要基于共享共同折叠且互补决定区（CDR）不同的可变结构域的准确结构建模的高通量表位作图。我们开发了一个深度学习的端到端模型NanoNet，给定一个序列，直接产生整个VH结构域的骨架和Cβ原子的3D坐标。对于Nb测试集，NanoNet对于最可变的CDR3环实现了3.16 μ m的平均RMSD，对于CDR1、CDR2环分别实现了2.65 μ m、1.73 μ m的平均RMSD。抗体VH结构域的准确度甚至更高：CDR3的RMSD为2.38，CDR1、CDR2环的RMSD分别为0.89和0.96。NanoNet运行时间允许在标准CPU计算机上在不到4小时内生成100万个纳米抗体结构，从而实现高通量结构建模。NanoNet可以在GitHub上找到：
Antibodies are a rapidly growing class of therapeutics. Recently, single domain camelid VHH antibodies, and their recognition nanobody domain (Nb) appeared as a cost-effective highly stable alternative to full-length antibodies. There is a growing need for high-throughput epitope mapping based on accurate structural modeling of the variable domains that share a common fold and differ in the Complementarity Determining Regions (CDRs). We develop a deep learning end-to-end model, NanoNet, that given a sequence directly produces the 3D coordinates of the backbone and Cβ atoms of the entire VH domain. For the Nb test set, NanoNet achieves 3.16Å average RMSD for the most variable CDR3 loops and 2.65Å, 1.73Å for the CDR1, CDR2 loops, respectively. The accuracy for antibody VH domains is even higher: 2.38Å RMSD for CDR3 and 0.89Å, 0.96Å for the CDR1, CDR2 loops, respectively. NanoNet run times allow generation of ∼1M nanobody structures in less than 4 hours on a standard CPU computer enabling high-throughput structure modeling. NanoNet is available at GitHub: