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表面折叠:通过整合表面和结构信息来实现蛋白质反向折叠的统一模型

Darong Li¹, Lian Shen¹, Meijia Song¹

¹Department of Computer Science and Technology Xiamen, Xiamen University, Xiamen, 361005, China.

Bioinformatics (Oxford, England)

|December 19, 2025

概括

SurfFold是一个新的反向折叠框架,通过整合表面和骨干数据来改善蛋白质序列预测. 这种方法提高了药物发现和同源蛋白质设计的准确性.

科学领域:

计算生物学计算生物学
结构生物信息学结构生物信息学
蛋白质设计蛋白质设计

背景情况:

准确的蛋白质序列预测对于药物发现至关重要.
当前的逆折叠模型忽视了关键的表面生物化学特性,限制了预测准确度.
蛋白质表面数据提供了功能上的限制,仅靠骨干结构无法捕获.

研究的目的:

开发一种新的反向折叠框架,SurfFold,它集成了各种蛋白质数据,用于增强序列预测.
通过将表面和侧链信息与骨干结构一起结合起来,提高蛋白质序列预测的准确性.
为了验证SurfFold在预测蛋白序列和设计同源蛋白质方面的有效性.

主要方法:

SurfFold框架集成了蛋白质骨干结构,表面信息和侧链结构细节.
引入了一个表示对齐模块,以有效地融合结构和表面表示.
该模型在CATH4.2数据集和通过同源结构逆折叠实验进行了评估.

主要成果:

SurfFold在CATH4.2数据集上实现了最先进的性能,用于蛋白质序列预测.
实验结果证实了SurfFold内部集成模块的有效性.
SurfFold在同源蛋白质设计方面表现出强大的能力,优于现有的方法.

结论:

整合表面和侧链信息显著提高了反向折叠的准确性.
SurfFold代表了一种用于蛋白质序列预测和设计的新有效方法.
该框架有望促进药物发现和蛋白质工程应用.

关键词:

反向的折叠方式蛋白质的骨干结构,蛋白质表面结构.侧链结构信息侧链结构信息

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