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评估由AlphaFold预测的leucine拉链结构的有效性

Isobel Mitic^1,2, Keiran Rowell³, Thomas Litfin³

¹Department of Molecular Medicine, School of Biomedical Sciences, University of New South Wales, Sydney, New South Wales, Australia.

Protein science : a publication of the Protein Society

|December 23, 2025

概括

人工智能 (AI) 准确预测蛋白质结构,包括氨酸拉链二次体,但可能会高估不太可能相互作用的信心. 这突出了人工智能.

科学领域:

结构生物学是结构生物学.
计算生物学是一种计算生物学.
遗传学是一个遗传学.

背景情况:

AP-1转录因子通过通过氨酸拉链域通过二元化调节细胞通路.
分化特异性是由转录因子亲和力和度决定的,控制基因表达.
人工智能驱动的蛋白质结构预测为研究蛋白质相互作用提供了新的途径.

研究的目的:

调查AlphaFold模拟leucine拉链域和预测二元接口的能力.
评估AlphaFold在区分可能和不可能的蛋白质与蛋白质相互作用方面的准确性.
评估AI在结构生物学中的能力和局限性.

主要方法:

利用AlphaFold2和AlphaFold3进行基于AI的蛋白质结构预测.
在Fos-Jun AP-1二元模型上测试了AlphaFold的性能.
分析了超过2000个经过实验验证的人类氨酸拉链序列.

主要成果:

阿尔法福德成功地预测了高度自信的氨酸拉链二极管.
人工智能模型预测了FosB同位体的形成,尽管已知in vivo的静电排斥.
证明了AlphaFold对高可靠性但可能不准确的结构预测的潜力.

结论:

AlphaFold可以高可靠性地模拟leucine拉链域.
人工智能预测可能并不总是符合实验生物约束,如静电学.
了解人工智能的局限性对于准确的结构生物学应用至关重要.

关键词:

阿尔法折叠多元体的多元体在AlphaFold2中,我们使用了AlphaFold2.博尔茨-1-1 的情况.在PAE的基础上,PAE是PAE.静电相互作用静电相互作用 ipTM ipTM 的意思是氨酸拉链的拉链 pLDDTTT 这就是为什么.质量评估质量评估的质量评估.

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