使用PHILHARMONIC解码非模型生物的功能互动体.
Samuel Sledzieski1, Charlotte Versavel2, Rohit Singh3
1Center for Computational Biology, Flatiron Institute, New York, NY, USA.
bioRxiv : the preprint server for biology
|November 18, 2024
概括
PHILHARMONIC使用深度学习和集群在非模型生物中推断蛋白质-蛋白质相互作用网络. 这种方法揭示了功能模块,并注释了未表征的蛋白质,使生物发现成为可能.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 基因组学就是基因组学.
背景情况:
- 蛋白与蛋白相互作用 (PPI) 网络对于理解细胞功能至关重要.
- 现有的PPI数据库主要涵盖了经过充分研究的模型生物.
- 对于大多数物种,实验数据很少,这限制了网络分析.
研究的目的:
- 开发一种用于推断非模型生物体中PPI网络的计算方法.
- 在缺乏实验互动数据的物种中发现功能关系和生物组织.
- 通过网络分析,使未表征蛋白质的功能注释成为可能.
主要方法:
- 结合深度学习以进行新的网络推断与光谱聚类.
- 开发了ReCIPE算法来重新连接断开的集群.
- 使用hmmscan和GODomainMiner进行基于远程同类学的功能注释.
- 应用了"通过关联的功能"来赋予未表征的蛋白质功能.
主要成果:
- 成功推断了珊瑚中的功能性蛋白质-蛋白质相互作用网络 (P. 达米科尼斯 (Damicornis),它的共生体 (C. 果 (Goreaui) 和果 (D. 黑色巨 (Melanogaster) 的意思是黑色巨 (Melanogaster).
- 确定了高度连贯的功能模块,并将功能分配给以前未被描述的蛋白质.
- 在P.中,推断集群和基因共同表达之间有强烈的相关性. 达米科尼斯 (Damicornis) 是一个
- 发现了参与珊瑚温度调节的聚类,包括新的蛋白质注释.
结论:
- PHILHARMONIC提供了一个可访问的,端到端的解决方案,用于在非模型生物体中的生物发现.
- 该方法产生了强大的功能模块,并促进了假设生成.
- 能够从测序的蛋白质组中进行大规模的功能注释和网络分析.
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