Dube Namita, Ramelot Theresa A, Benavides Tiburon L, Huang Yuanpeng J, Moult John, Kryshtafovych Andriy, Montelione Gaetano T
Dept of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, 12180 USA.
Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA.
bioRxiv. 2025 Sep 2:2025.09.02.673835. doi: 10.1101/2025.09.02.673835.
The CASP16 Ensemble Prediction experiment assessed advances in methods for modeling proteins, nucleic acids, and their complexes in multiple conformational states. Targets included systems with experimental structures determined in two or three states, evaluated by direct comparison to experimental coordinates, as well as domain-linker-domain (D-L-D) targets assessed against statistical models from NMR and SAXS data. This paper focuses on the former class of multi-state targets. Ten ensembles were released as community challenges, including ligand-induced conformational changes, protein-DNA complexes, a trimeric protein, a stem-loop RNA, and multiple oligomeric states of a single RNA. For five targets, some groups produced reasonably accurate models of both reference states (best TM-score >0.75). However, with the exception of one protein-ligand complex (T1214), where an apo structure was available as a template, predictors generally failed to capture key structural details distinguishing the states. Overall, accuracy was significantly lower than for single-state targets in other CASP experiments. The most successful approaches generated multiple AlphaFold2 models using enhanced multiple sequence alignments and sampling protocols, followed by model quality based selection. While the AlphaFold3 server performed well on several targets, individual groups outperformed it in specific cases. By contrast, predictions for one protein-DNA complex, three RNA targets, and multiple oligomeric RNA states consistently fell short (TM-score <0.75). These results highlight both progress and persistent challenges in multi-state prediction. Despite recent advances, accurate modeling of conformational ensembles, particularly RNA and large multimeric assemblies, remains a critical frontier for structural biology.
CASP16集成预测实验评估了在对处于多种构象状态的蛋白质、核酸及其复合物进行建模的方法方面取得的进展。目标包括具有在两种或三种状态下确定的实验结构的系统，通过与实验坐标直接比较进行评估，以及针对来自核磁共振（NMR）和小角X射线散射（SAXS）数据的统计模型评估的结构域-连接子-结构域（D-L-D）目标。本文重点关注前一类多状态目标。作为社区挑战发布了十个集成模型，包括配体诱导的构象变化、蛋白质-DNA复合物、三聚体蛋白、茎环RNA以及单个RNA的多种寡聚状态。对于五个目标，一些团队对两种参考状态都生成了相当准确的模型（最佳TM分数>0.75）。然而，除了一个蛋白质-配体复合物（T1214），其无配体结构可作为模板外，预测器通常未能捕捉到区分这些状态的关键结构细节。总体而言，准确性显著低于其他CASP实验中的单状态目标。最成功的方法是使用增强的多序列比对和采样协议生成多个AlphaFold2模型，然后基于模型质量进行选择。虽然AlphaFold3服务器在几个目标上表现良好，但个别团队在特定情况下表现优于它。相比之下，对一个蛋白质-DNA复合物、三个RNA目标和多个寡聚RNA状态的预测始终不足（TM分数<0.75）。这些结果突出了多状态预测方面的进展和持续挑战。尽管最近取得了进展，但对构象集合，特别是RNA和大型多聚体组装体进行准确建模，仍然是结构生物学的一个关键前沿领域。
