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在CASP16中准确地预测生物分子结构,并对最先进的预测器进行优化输入

Wenkai Wang¹, Yuxian Luo¹, Zhenling Peng¹

¹MOE Frontiers Science Center for Nonlinear Expectations, State Key Laboratory of Cryptography and Digital Economy Security, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China.

|August 5, 2025

概括

深度学习推进了生物分子结构预测. CASP16的结果显示了蛋白质域的高精度,但蛋白质多分子和RNA结构仍然存在挑战,突出了未来研究的领域.

科学领域:

计算生物学计算生物学
结构生物学结构生物学
生物信息学是一种生物信息学.

背景情况:

深度学习显著提高了生物分子结构预测的准确性.
CASP16 (蛋白质结构预测的批判性评估) 竞赛为评估结构预测方法提供了一个基准.

研究的目的:

评估CASP16.16中蛋白质和RNA结构预测的最先进的深度学习模型.
确定当前结构预测技术的成功领域和挑战.

主要方法:

使用了先进的结构预测器,包括AlphaFold2,AlphaFold3,trRosettaX2和trRosettaRNA2.
优化了输入数据,通过删除内在无序的区域来预测蛋白质结构.
调整的二次结构输入用于RNA结构预测.

主要成果:

在CASP16中获得了顶级排名:第1位的蛋白质域 (服务器组),第2位的蛋白质多元体和第4位的RNA单元体.
证明了蛋白质域结构预测的高准确性.
在预测高质量的蛋白质多分子结构 (<25%) 中观察到的局限性.
在这项研究中发现优化的trRosettaRNA2在RNA结构预测方面表现优于AlphaFold3.

结论:

蛋白质域结构预测已经达到很高的准确度.
预测蛋白质多重体和RNA结构仍然是一个重大挑战.
预计在蛋白质多分子和RNA结构预测方法方面将在未来取得进展.

关键词:

在CASP16中,CASP16是CASP16的代码.预测RNA结构预测RNA结构蛋白质结构预测蛋白质结构预测蛋白质蛋白质复合体蛋白质蛋白质复合体

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