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Alpha&ESMhFolds:一个用于比较人类参考蛋白质组AlphaFold2和ESMFold模型的Web服务器

Matteo Manfredi¹, Castrense Savojardo¹, Georgii Iardukhin¹

¹Biocomputing Group, Dept. of Pharmacy and Biotechnology, University of Bologna, Italy.

Journal of molecular biology

|May 8, 2024

概括

我们创建了Alpha&ESMhFolds,这是一个比较AlphaFold2和ESMFold蛋白质模型的数据库. 它表明AlphaFold2模型略好一些,但这两种方法都有局限性,特别是没有PDB数据.

科学领域:

结构生物信息学结构生物信息学
计算生物学是一种计算生物学.
蛋白质建模模型

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.
AlphaFold2和ESMFold是领先的基于AI的蛋白质结构预测工具.
需要与实验数据直接比较他们的输出.

研究的目的:

开发一个全面的数据库来比较AlphaFold2和ESMFold预测的蛋白质模型.
通过与实验确定的PDB结构进行比较来评估模型质量.
评估两个预测方法的性能和局限性.

主要方法:

创建了Alpha&ESMhFolds数据库,其中包含来自AlphaFold2和ESMFold的42,942个人类蛋白质组预测.
集成了2900个PDB结构,以便在可用的情况下进行直接比较 (≥70%的序列覆盖率).
使用TM-score进行预测模型与PDB结构的定量比较.

主要成果:

优质的预测模型 (TM-score >0.6) 与可用的PDB结构信息相关.
当AlphaFold2模型叠加在PDB结构上时,与ESMFold模型相比,其准确性略高.
大约55%的数据库具有TM-score<0.6的模型,这表明了显著的变化和局限性.

结论:

阿尔法和ESMhFolds数据库为蛋白质结构预测方法的比较分析提供了宝贵的资源.
虽然AlphaFold2显示了轻微的优势,但这两种方法都有局限性,特别是在缺乏实验结构数据的情况下.
这些发现突出了AlphaFold2和ESMFold的不同输出,并强调了持续需要提高预测准确性和验证的需求.

关键词:

我们的数据库数据库数据库数据库.嵌入式嵌入式嵌入式模型叠加的叠加.蛋白质语言模型蛋白质结构预测蛋白质结构预测

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