发表机构
KAIST（韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对单一共折叠模型性能不稳定问题，提出SoupFold，通过映射并融合多个模型表示，在不重训练的情况下，在蛋白质-蛋白质和配体预测上达到最先进性能。
AI 中文摘要
共折叠模型发展迅速，但没有任何单一模型能在所有生物分子复合物上始终表现最佳。这引发了一个问题：独立训练的共折叠模型是否编码了可在共折叠模型之间转移的互补信息。我们引入了SoupFold，它通过学习共折叠模型表示空间之间的简单映射来改进共折叠预测。在推理时，SoupFold从其他共折叠模型转移并整合表示，以更新用于结构预测的表示。重要的是，这不会重新训练共折叠模型。我们使用AlphaFold3、Protenix、ESMFold2和OpenDDE在FoldBench的蛋白质-蛋白质和蛋白质-配体预测任务上评估了SoupFold。通过组合它们的表示，SoupFold在蛋白质-蛋白质和蛋白质-配体结构预测上均达到了最先进的性能，表明独立训练的共折叠模型编码了可有效跨模型转移的互补信息。
英文摘要
Co-folding models such as AlphaFold3, Protenix, ESMFold2, and OpenDDE have advanced rapidly, yet no single model consistently performs best across all biomolecular complexes. In this paper, we show that their pair representations encode complementary information that can be transferred across models to improve structure prediction. We introduce SoupFold, which combines pair representations from multiple co-folding models in a common representation space and generates structures from the combined representation. Importantly, SoupFold does not retrain the co-folding models and learns only simple mappings to transfer representations across models. We evaluate SoupFold on antibody-antigen, protein-protein, protein-ligand, molecular glue, GPCR, and oligomeric complex prediction using AlphaFold3, Protenix, ESMFold2, and OpenDDE. By combining representations across models, SoupFold improves over individual co-folding models across the considered benchmarks.
