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在557个Mac1-ligand复合体和三个虚拟屏幕上对共同折叠进行了大规模的前性评估

Jongbin Kim, Galen J Correy, Brendan W Hall

bioRxiv : the preprint server for biology

|January 9, 2026

概括

深度学习的共同折叠方法在预测药物发现的联结蛋白质结构方面表现有前途. 虽然AlphaFold3和其他人准确地预测了姿势,但将它们与对接分数集成可能会改善击中优先级.

科学领域:

计算化学是一种计算化学.
结构生物学是结构生物学.
药物发现药物发现

背景情况:

准确预测连接体-蛋白质复合体和亲和度排名对于药物发现至关重要.
深度学习的共同折叠方法提供了潜在的解决方案,但需要严格的,独立的评估.

研究的目的:

通过使用独立的测试集,评估深度学习共折叠方法在预测联结蛋白质结构方面的性能.
评估共折叠预测与实验结合亲和力和强度之间的相关性.
为了确定是否共同折叠得分可以改善虚拟选中的命中优先级.

主要方法:

在557个SARS-CoV-2Mac1-ligand复合体上测试AlphaFold3,Boltz-2和Chai-1,确定了训练后的截止时间.
分析蛋白质构造变化在连接物结合后的复制.
与实验功效相关联的共同折叠预测 (姿势信心,亲和力).
使用共折叠得分对抗AmpCβ-lactamase,多巴胺D4和σ2受体,恢复对接击命中榜单.

主要成果:

AlphaFold3,Boltz-2和Chai-1在实验结构的2 Å RMSD内复制了超过50%的连接体姿势.
同折叠方法没有显著回顾常见的蛋白质构造变化.
来自AF3和Chai-1的联结体构成信心与实验功效有很弱的相关性;博尔茨-2亲和力预测显示出更强的相关性.
在区分真联体与假阳性方面,AF3姿势信心低于对接分数或Boltz-2亲和力.

结论:

同折叠方法证明了对联结蛋白质结构的独立预测能力.
将基于物理的对接与深度学习的共同折叠方法相结合,可能会提高药物发现管道中的击中优先级和优化.

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