Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen, Germany.
Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.
Proteins. 2021 Dec;89(12):1687-1699. doi: 10.1002/prot.26171. Epub 2021 Jul 14.
The application of state-of-the-art deep-learning approaches to the protein modeling problem has expanded the "high-accuracy" category in CASP14 to encompass all targets. Building on the metrics used for high-accuracy assessment in previous CASPs, we evaluated the performance of all groups that submitted models for at least 10 targets across all difficulty classes, and judged the usefulness of those produced by AlphaFold2 (AF2) as molecular replacement search models with AMPLE. Driven by the qualitative diversity of the targets submitted to CASP, we also introduce DipDiff as a new measure for the improvement in backbone geometry provided by a model versus available templates. Although a large leap in high-accuracy is seen due to AF2, the second-best method in CASP14 out-performed the best in CASP13, illustrating the role of community-based benchmarking in the development and evolution of the protein structure prediction field.
将最先进的深度学习方法应用于蛋白质建模问题，已经将 CASP14 的“高精度”类别扩展到了涵盖所有目标。在之前 CASP 中用于高精度评估的指标基础上，我们评估了所有提交模型的小组的性能，这些小组至少提交了 10 个不同难度等级的目标，并使用 AMPLE 评估了由 AlphaFold2 (AF2) 生成的模型的有用性，将其作为分子置换搜索模型。受提交给 CASP 的目标的定性多样性的驱动，我们还引入了 DipDiff，作为一种新的度量标准，用于衡量模型相对于可用模板提供的骨架几何形状的改进。尽管由于 AF2 的出现，高精度方面取得了巨大的飞跃，但在 CASP14 中排名第二的方法比在 CASP13 中排名第一的方法要好，这说明了基于社区的基准测试在蛋白质结构预测领域的发展和演变中的作用。
