评估序列和结构相似度指标,用于预测共享的对等函数
Olivier Dennler1,2,3, Colm J Ryan1,2,3
1School of Medicine, University College Dublin, Dublin 4, D04 V1W8, Ireland.
NAR genomics and bioinformatics
|April 28, 2025
概括
新的蛋白质相似度指标,包括结构和语言模型嵌入,比单独的序列身份更好地预测基因对比函数. 结合这些新的方法可以提高功能预测的准确性.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 基因重复是新基因进化的主要驱动因素,产生常常保留类似功能的类似基因.
- 蛋白序列标识是功能相似性的常见代理,有助于预测对象之间的共享功能.
- 新兴的蛋白质表示,如蛋白质语言模型 (PLM) 嵌入和AlphaFold预测结构,为评估功能相似性提供了新的途径.
研究的目的:
- 为了评估超越序列身份的替代蛋白质相似度指标,以预测共享的对比功能.
- 为了确定结构性或基于PLM的相似度量是否可以超越或补充序列身份.
- 为了研究上下文特征的影响,如同类学,预测共享的方程函数.
主要方法:
- 使用两种物种 (酵母,人类) 的各种相似度量 (序列相同性,PLM嵌入,结构相似性) 的比较.
- 基于蛋白质-蛋白质相互作用和合成致命性的共享功能评估.
- 整合的上下文特征代表跨物种和物种内部的同质性.
主要成果:
- 其他相似度指标,包括基于结构和PLM的方法,在预测共享的对应函数方面表现出了很好的前景.
- 这些新的指标并没有冗余的序列标识,并且在结合时可以改善预测.
- 整合上下文同类特征显著提高了预测共享paralog功能的准确性.
结论:
- 替代蛋白质相似度指标捕捉了不仅仅由序列身份表示的功能方面.
- 将序列标识与结构,PLM和上下文同类特征相结合,为预测对应函数提供了更全面的方法.
- 这些发现表明,通过比较基因组学,可以改进理解基因功能和进化过程的方法.
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