标题： AlphaFold3中在生物过程残基中存在无序的固有无序蛋白质中的幻觉 显示英文标题

标题： Hallucinations in AlphaFold3 for Intrinsically Disordered Proteins with disorder in Biological Process Residues

摘要： 蛋白质结构预测在引入AlphaFold3后取得了显著进展，这是一种基于扩散的模型，能够预测蛋白质、核酸、小分子和离子之间的复杂生物分子相互作用。 尽管AlphaFold3在折叠蛋白质中表现出高准确性，但其在固有无序蛋白质（IDPs）上的表现仍较少被探索。IDPs占人类蛋白质组的30至40%，在转录、信号传导和疾病中起着关键作用。 本研究评估了AlphaFold3对IDPs的预测，重点是固有无序区域（IDRs），数据来自DisProt数据库中的72种蛋白质。 预测结果是在多个随机种子和集成输出下生成的，并将残基级别的pLDDT分数与实验性无序注释进行了比较。 我们的分析显示，32%的残基与DisProt不一致，百分比代表了幻觉现象，即AlphaFold3在无序区域错误地预测有序或反之。 此外，10%的残基表现出上下文驱动的不一致，表明AlphaFold3隐式地包含了稳定的结构假设。 重要的是，18%与生物过程相关的残基出现了幻觉，这引发了在药物发现和疾病研究中的下游影响的担忧。 这些发现突显了AlphaFold3在建模IDRs方面的局限性，需要在pLDDT之外改进幻觉度量标准，并强调整合实验性无序数据以提高预测可靠性的重要性。 显示英文摘要

摘要： Protein structure prediction has advanced significantly with the introduction of AlphaFold3, a diffusion-based model capable of predicting complex biomolecular interactions across proteins, nucleic acids, small molecules, and ions. While AlphaFold3 demonstrates high accuracy in folded proteins, its performance on intrinsically disordered proteins (IDPs), which comprise 30 to 40 percent of the human proteome and play critical roles in transcription, signaling, and disease, remains less explored. This study evaluated AlphaFold3's predictions of IDPs with a focus on intrinsically disordered regions (IDRs) using 72 proteins curated from the DisProt database. Predictions were generated across multiple random seeds and ensemble outputs, and residue-level pLDDT scores were compared with experimental disorder annotations. Our analysis reveals that 32 percent of residues are misaligned with DisProt, with percent representing hallucinations where AlphaFold3 incorrectly predicts order in disordered regions or vice versa. Additionally, 10 percent of residues exhibited context-driven misalignment, suggesting that AlphaFold3 implicitly incorporates stable structural assumptions. Importantly, 18 percent of residues associated with biological processes showed hallucinations, raising concerns about downstream implications in drug discovery and disease research. These findings highlight the limitations of AlphaFold3 in modeling IDRs, the need for refined hallucination metrics beyond the pLDDT, and the importance of integrating experimental disorder data to improve prediction reliability.