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Updated: Sep 13, 2025

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通过引导AlphaFold2与双电子电子共振 (DEER) 距离分布来建模蛋白质构造组合

Tianqi Wu¹, Richard A Stein^1,2, Te-Yu Kao^2,3

¹Center for Applied AI for Protein Dynamics, Vanderbilt University, Nashville, TN, USA.

Nature communications

|August 2, 2025

概括

我们开发了DEERFold,它是一种修改过的AlphaFold2,它使用来自双电子共振 (DEER) 光谱的实验距离分布来预测蛋白质结构. 这种方法提高了形状预测的准确性和实验效率.

科学领域:

计算生物学是一种计算生物学.
结构生物学是结构生物学.
生物物理学的生物物理.

背景情况:

预测蛋白质结构对于理解生物功能至关重要.
准确预测蛋白质构成组合仍然是一个挑战.
实验技术,如双电子共振 (DEER) 光谱,提供了距离分布.

研究的目的:

开发一种改进的AlphaFold2 (DEERFold) 集成实验距离分布,以改善蛋白质结构预测.
评估DEERFold能够预测膜传送器中的形状变化的能力.
评估DEERFold的可通用性,用于预测各种蛋白质中的 conformational ensembles.

主要方法:

在OpenFold平台上微调AlphaFold2,使用结构不同的蛋白质.
将DEER光谱中的实验距离分布纳入网络架构.
基准测试 DEERFold 与膜载体和大量可溶性和膜蛋白的实验数据相比.

主要成果:

根据DEER的距离分布,DEERFold成功地预测了膜传送器中的结构切换.
修改后的AlphaFold2在预测各种蛋白质的构造组合方面表现出普遍性.
内在的AlphaFold2性能降低了距离分布的数量和精度要求,增加了实验吞吐量.

结论:

德尔福德有效地将实验距离分布整合到蛋白质结构预测中.
该DEERFold框架增强了对蛋白质构成组合的预测.
这种方法可以推广到其他提供距离分布约束的实验方法.

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