使用局部结构嵌入的蛋白质功能位点注释
Alexander Derry1, Alp Tartici2, Russ B Altman1,2,3
1Department of Biomedical Data Science, Stanford University, Stanford, CA 94305.
概括
我们开发了一种新的方法, 预测蛋白质的功能, 并识别关键的残留物. 这种方法有助于注释具有未知的功能的蛋白质,特别是在大规模的蛋白质组分析中.
科学领域:
- 蛋白质组学
- 生物信息学
- 计算生物学
背景情况:
- 蛋白质数据库正在迅速扩大,导致许多蛋白质具有未知的或模糊的功能.
- 目前的机器学习方法很难将全球蛋白质功能与特定的残留物联系起来.
研究的目的:
- 引入PARSE (通过残留物特定丰富的蛋白质注释) 一种基于知识的新方法,用于预测蛋白质功能和注释残留物水平的功能位点.
- 解决当前方法在将全球功能与特定残留物的限制.
主要方法:
- PARSE将当地蛋白质结构环境的预训练嵌入与统计技术相结合.
- 它同时进行全局函数和残留级别注释的预测.
- 该方法基于知识,不需要监督培训,可以对罕见的函数进行一次性预测.
主要成果:
- 对于酶催化功能的预测,PARSE的性能与最先进的方法相美或更高 (F1得分约为85%).
- 对于已识别的功能,PARSE提供了更精确的残留水平注释.
- 该方法使用AlphaFold结构成功预测了来自"黑暗蛋白质体"的细菌金属蛋白酶,尽管序列和折叠分离,但仍显示了保存的催化位点.
结论:
- PARSE提供了一种强大的工具,用于注释蛋白质的功能,并识别功能关键的残留物,特别是具有未知的或罕见功能的蛋白质.
- 它能够利用本地结构信息,并在没有监督培训的情况下运行,为大规模蛋白质组数据集的功能发现开辟了新的途径.
- 该方法证明了局部结构表示在发现新功能时的实用性,即使在蛋白质中有不同的序列和折叠.
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