Lensink Marc F, Raouraoua Nessim, Brysbaert Guillaume, Velankar Sameer, Wodak Shoshana J, Bonvin Alexandre M J J
University of Lille, CNRS UMR8576 UGSF, Unite de Glycobiologie Structurale et Fonctionnelle, Lille, France.
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK.
Proteins. 2025 Jul 14. doi: 10.1002/prot.70018.
We report on the 8th CAPRI Evaluation period, capturing the assessment of CAPRI Rounds 47 to 55 (excluding the CASP and COVID-related Rounds), which have witnessed the transition to AI-driven prediction tools such as AlphaFold and related alternatives. The prediction Rounds in this evaluation are characterized by a high level of difficulty due to various factors, including the nature of the targets, the intricacy of the interfaces to be predicted, and conformational changes. A total of 11 targets encompassing 21 interfaces, mostly in the difficult prediction category, were evaluated. While a retrospective analysis reveals a strong performance of AlphaFold on those targets, human predictors still outperform AI on difficult targets, particularly those involving antibodies and nucleic acids. Almost 25 years after its birth, CAPRI remains a vibrant and collaborative initiative with active participation from approximately 50 predictor and scorer groups and 10 servers. Continued contributions from experimentalists providing targets to such blind experiments, and further advances in AI, sampling strategies, and improvement in scoring methods will be key to overcoming remaining structural prediction challenges in complex biomolecular systems.
我们报告了第8个CAPRI评估期，涵盖了对第47至55轮CAPRI预测（不包括CASP和与COVID相关的轮次）的评估，在此期间见证了向诸如AlphaFold等人工智能驱动的预测工具及相关替代工具的转变。本次评估中的预测轮次因各种因素而具有较高难度，包括靶标的性质、待预测界面的复杂性以及构象变化。总共评估了11个靶标，涵盖21个界面，其中大部分属于难度较大的预测类别。虽然回顾性分析显示AlphaFold在这些靶标上表现出色，但在难度较大的靶标上，尤其是涉及抗体和核酸的靶标，人类预测者的表现仍优于人工智能。在诞生近25年后，CAPRI仍然是一个充满活力的合作项目，约有50个预测和评分小组以及10个服务器积极参与。实验人员持续为这类盲法实验提供靶标，以及人工智能、采样策略的进一步发展和评分方法的改进，将是克服复杂生物分子系统中剩余结构预测挑战的关键。
