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用NA-MPNNN进行RNA序列设计和蛋白质-DNA特异性预测

Andrew Kubaney^1,2,3, Andrew Favor^1,2,3, Lilian McHugh^1,3,4

¹Institute for Protein Design, University of Washington, Seattle, WA 98105, USA.

bioRxiv : the preprint server for biology

|November 19, 2025

概括

一个新的深度学习模型,核酸MPNN (NA-MPNN),统一了RNA序列设计和蛋白质-DNA结合预测. 这种统一的方法提高了性能,并扩大了生物聚合物结构设计和特异性预测中的应用.

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.
机器学习机器学习

背景情况:

RNA序列设计和蛋白质-DNA结合特异性预测是反向折叠的问题.
现有的方法是任务特定的,限制了统一的深度学习应用程序.
一个单一的模型可以利用更大的数据集并提供更广泛的适用性.

研究的目的:

为核酸反折叠引入一个统一的深度学习模型.
开发一个传递信息的神经网络,用于生物聚合物图形表示.
改进RNA序列设计和蛋白质-DNA结合特异性预测.

主要方法:

开发了核酸MPNN (NA-MPNN),一个传递信息的神经网络.
在统一的生物聚合物图形表示中处理的蛋白质,DNA和RNA.
将NA-MPNN应用于RNA序列设计和蛋白质-DNA特异性预测任务.

主要成果:

在RNA序列设计方面,NA-MPNN的性能优于以前的方法.
在固定码头蛋白-DNA特异性预测中,NA-MPNN实现了卓越的性能.
证明了该模型在不同核酸和蛋白质-DNA相互作用任务中的有效性.

结论:

NA-MPNN为核酸反折叠提供了一个统一的深度学习框架.
该模型显示了与现有方法相比的显著改进.
对于 de novo RNA 结构设计和 DNA 结合特异性预测,NA-MPNN 是广泛适用的.

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The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms a 5′ to 3′ phosphodiester linkage.
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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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