Evaluating deep learning based structure prediction methods on antibody-antigen complexes

Samuel Fromm

doi:10.1093/bioinformatics/btag136

2026-04-01

PRE

Samuel Fromm

Marko Ludaic

Arne Elofsson *

DOI：10.1093/bioinformatics/btag136

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动机 AlphaFold2显著提高了蛋白质复合物结构的预测精度。然而，对于缺乏协同进化信号的相互作用（如宿主-病原体和抗体-抗原相互作用），其准确性较低。为解决此限制，已开发两种策略：大量采样和用不依赖协同进化的pairformer替换evoformer（如AlphaFold3中引入的），从而增强网络的构象推理能力。结果本研究在未见过的抗体-抗原复合物上评估了结构预测方法。我们发现，增加采样量可提高生成正确蛋白质模型的机会，大致呈对数线性关系。然而，AlphaFold的内部质量评估常无法识别每个目标的最佳预测结构，导致顶级模型相比最佳模型的性能显著下降。对所有方法而言，识别最佳模型仍是重大挑战。我们还证明，AlphaFold3优于AlphaFold2、Boltz-1和Chai-1。此外，AlphaFold3在缺乏与训练集结构相似性的复合物上性能显著下降，表明其已在一定程度上学会检测远程结构相似性。可用性与实现所有代码可从github.com/samuelfromm/abag-benchmark-set/获取，数据来自DOI: 10.5281/zenodo.17978681，该仓库也包含代码。

Keyword:

AlphaFold3

antibody-antigen complexes

structure prediction

deep learning

coevolution

期刊

Bioinformatics

IF:

5.4

论文数:

1.4K

被引数:

17.9W

机构

stockholm university

学者数:

1.9K

论文数: 1.1K

被引数: 0

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