AlphaFold 3 解决了 D-的蛋白质折叠问题吗?
bioRxiv : the preprint server for biology
|April 1, 2025
概括
AlphaFold 3 (AF3) 难以预测D-的结构,D-结合剂的性违规率为51%,未能保持指定的立体中心. 这表明AF3在设计D-治疗中不可靠.
科学领域:
- 计算生物学和结构生物信息学.
- 蛋白质设计和药物发现.
背景情况:
- 由于稳定性和生物可用性的提高,D-类比L-类具有治疗优势.
- 准确预测性和结合姿势对于D-的计算设计至关重要.
- AlphaFold 3 (AF3) 是一个深度学习模型,旨在预测化学结构,包括性中心.
研究的目的:
- 评估AlphaFold 3在预测D-的折叠,性和结合姿势方面的准确性.
- 评估AF3在模拟D-和L-蛋白的异构体复合物的性能.
主要方法:
- 通过使用AF3进行了3,255次实验,对D-的明确D-立体中心输入.
- 评估了AF3对性,折叠和结合的预测,与实证数据相比呈现出准确性.
- 评估了增加预测种子对奇拉性违规率的影响.
主要成果:
- 对于D-结剂,AF3表现出高达51%的性违规率,与各种性分子报告的4.4%显著偏离.
- 预测显示错误的折叠和结合姿势,D-经常误导L-蛋白结合口袋.
- AF3信心指标未能与预测准确度相关联,无法区分正确与不正确的模型.
结论:
- 阿尔法折叠3是D-性性,折叠和结合姿势在异体性复合体中的一个糟糕的预测器.
- 该模型的性能可与预测D-立体化学的随机机会相提并论.
- 为了准确的计算预测D-结构和相互作用,需要进一步开发.
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