Singh Amar, Copeland Matthew M, Kundrotas Petras J, Vakser Ilya A
Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA.
Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, USA.
Proteins. 2025 Jun 6. doi: 10.1002/prot.26853.
In recent years, the field of structural biology has seen remarkable advancements, particularly in modeling of protein tertiary and quaternary structures. The AlphaFold deep learning approach revolutionized protein structure prediction by achieving near-experimental accuracy on many targets. This paper presents a detailed account of structural modeling of oligomeric targets in Round 55 of CAPRI by combining deep learning-based predictions (AlphaFold2 multimer pipeline) with traditional docking techniques in a hybrid approach to protein-protein docking. To complement the AlphaFold models generated for the given oligomeric state of the targets, we built docking predictions by combining models generated for lower-oligomeric states-dimers for trimeric targets and trimers/dimers for tetrameric targets. In addition, we used a template-based docking procedure applied to AlphaFold predicted structures of the monomers. We analyzed the clustering of the generated AlphaFold models, the confidence in the prediction of intra- and inter-chain residue-residue contacts, and the correlation of the AlphaFold predictions stability with the quality of the submitted models.
近年来，结构生物学领域取得了显著进展，尤其是在蛋白质三级和四级结构建模方面。AlphaFold深度学习方法通过在许多目标上实现接近实验精度，彻底改变了蛋白质结构预测。本文详细介绍了在CAPRI第55轮中，通过将基于深度学习的预测（AlphaFold2多聚体管道）与传统对接技术相结合的混合方法，对寡聚体目标进行结构建模的过程。为了补充针对目标给定寡聚状态生成的AlphaFold模型，我们通过组合为较低寡聚状态生成的模型（三聚体目标的二聚体模型以及四聚体目标的三聚体/二聚体模型）来构建对接预测。此外，我们对应用于AlphaFold预测的单体结构的基于模板的对接程序进行了分析。我们分析了生成的AlphaFold模型的聚类情况、链内和链间残基-残基接触预测的置信度，以及AlphaFold预测稳定性与提交模型质量之间的相关性。
