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使用现象学和机器学习对裂变酵母蛋白的广泛功能概况

María Rodríguez-López¹, Nicola Bordin², Jon Lees^2,3

¹University College London, Institute of Healthy Ageing and Department of Genetics, Evolution & Environment, London, United Kingdom.

|October 3, 2023

概括

这项研究使用酵母中的现象学和机器学习来发现成千上万种未表征的蛋白质的功能,包括在人类中保存的许多蛋白质,从而推进生物研究.

科学领域:

* 分子生物学 * 分子生物学
* 系统生物学系统生物学
* 计算生物学 * 计算生物学

背景情况:

*大量的蛋白质缺乏功能性特征,阻碍了生物研究.
* 了解蛋白质功能对于推进各种生物学科至关重要.

研究的目的:

* 应用现象学和机器学习来系统地描述Schizosaccharomyces pombe*中的蛋白质功能.
* 确定未表征和"优先未研究"蛋白质的新功能,包括在人类中保存的蛋白质.

主要方法:

*在131种不同的条件下,对3509种非必要的基因删除突变体进行了殖民地生长表型的测试.
* 构建了表型相关性网络,通过"因关联而有罪"来推断蛋白质功能.
* 采用机器学习 (NET-FF) 集成蛋白质网络和同类学数据来预测基因本体学 (GO) 术语.

主要成果:

* 获得了3492个突变的表型数据,揭示了124个"优先未研究"蛋白质的功能线索.
* 超过900种蛋白质与氧化应激抵抗有关.
*产生了56594个高得分的GO预测,其中1675个用于783个基因的新奇预测,其中47个用于23个优先未研究的蛋白质.

结论:

* 综合现象学和机器学习为发现蛋白质功能提供了强大的资源.
* 实验验证证了细胞衰老中的新型蛋白质作用.
*这项研究显著扩大了S. pombe蛋白质组的功能注释,并确定了未来研究的关键目标.

关键词:

在S. pombe.细胞表型细胞表型计算生物学是计算生物学.计算预测的计算预测.功能性基因组学功能性基因组学基因本体学基因本体学遗传学遗传学遗传学是一个基因组学就是基因组学.机器学习是机器学习.系统生物学系统生物学未知蛋白质未知蛋白质

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