蛋白质的功能注释用于非模型物种的信号网络推断
Lisa Van den Broeck1, Dinesh Kiran Bhosale2, Kuncheng Song3
1Plant and Microbial Biology Department and NC Plant Sciences Initiative, North Carolina State University, Raleigh, NC, 27695, USA. lfvanden@ncsu.edu.
Nature communications
|August 3, 2023
概括
我们开发了一个神经网络,从序列中预测蛋白质的功能,使得在豆类等植物中发现新的信号通路. 这种方法识别了关键的温度调节器和酶-基质相互作用,而没有先前的数据.
科学领域:
- 分子生物学分子生物学
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
背景情况:
- 了解复杂生物系统中的细胞反应至关重要,但由于调节蛋白的功能注释有限,在非模型物种中具有挑战性.
- 现有的方法通常依赖于已知的蛋白质动机或相互作用数据,阻碍了新发现.
研究的目的:
- 开发一种新的计算方法,直接从氨基酸序列中预测蛋白质功能.
- 为了能够推断非模型植物物种,特别是大豆 (Glycine max) 的酸化信号级联.
- 确定植物温度反应的关键调节者.
主要方法:
- 开发一个多层神经网络,从序列数据中确定蛋白质的功能.
- 使用发达的神经网络在Glycine max中对激酶和酸酶的注释.
- 将神经网络预测与贝叶斯推理和高分辨率蛋白组学集成在一起,以推断信号级联.
- 这种方法适用于Oryza sativa,Zea mays,Sorghum bicolor和Triticum aestivum.
主要成果:
- 在Glycine max.中成功注释了激酶和酸酶.
- 在大豆中推断的冷诱导酸化信号级联.
- 确定了Glyma.10G173000 (TOI5) 和Glyma.19G007300 (TOT3) 作为关键温度调节器.
- 证明了酶基质相互作用的 de novo 识别,独立于已知的动机或相互作用数据.
- 在多种植物物种中验证了神经网络的概括性和可扩展性.
结论:
- 发达的神经网络为非模型生物中的功能性蛋白质注释提供了一个强大的工具.
- 信号推断方法有助于在植物中发现新的调节机制.
- 这项研究显著提高了我们研究植物对温度等环境线索的反应的能力.
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