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Updated: Jan 20, 2026

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对AlphaFold和ESMFold对单体和二元蛋白的预测准确性的比较评估

Sanjeet Kumar Mahtha¹, Sureshkumar Venkadesan^1,2, Debasisa Mohanty¹

¹Bioinformatics Center, BRIC-National Institute of Immunology, New Delhi 110067, India.

NAR genomics and bioinformatics

|January 19, 2026

概括

在预测蛋白质结构方面,AlphaFold3和AlphaFold2显示出高准确度,表现优于ESMFold,特别是在二维蛋白质方面. 一个新的门户网站,ProModEv,可以访问这些蛋白质模型评估结果.

科学领域:

计算生物学是一种计算生物学.
结构生物信息学结构生物信息学
在蛋白质科学中的机器学习

背景情况:

准确的蛋白质结构预测对于理解生物功能至关重要.
深度学习和大型语言模型已经推进了蛋白质结构预测.
与实验数据对比预测工具对性能评估至关重要.

研究的目的:

为了评估和比较AlphaFold2,AlphaFold3和ESMFold的预测准确度.
评估这些工具对具有挑战性的单质和二质蛋白标的性能.
为蛋白质模型评估的系统分析提供资源.

主要方法:

利用来自蛋白质数据库 (2022-2024) 的实验性衍生蛋白质结构.
选择具有低序列身份 (<40%) 和查询覆盖率 (<70%) 到非同类先前结构的具有挑战性的目标.
基于基准的AlphaFold2,AlphaFold3和ESMFold对单体和二元蛋白质的预测准确性.

主要成果:

AlphaFold2和AlphaFold3实现了88%的单体蛋白质和77%的二元蛋白质的准确性.
ESMFold准确地预测了76%的单体蛋白和41%的二元蛋白.
对于X射线和冷EM结构,AlphaFold和ESMFold对单体蛋白质的准确性分别为95%和83%.

结论:

对于单质蛋白与二元蛋白的ML工具的预测准确性存在显著差异.
AlphaFold3和AlphaFold2表现出卓越的性能,特别是在二维蛋白质结构预测方面.
ProModEv门户提供可访问的基准测试数据用于蛋白质模型评估.

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