Functional annotation of proteins for signaling network inference in non-model species.
Functional annotation of proteins for signaling network inference in non-model species.
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DOI：
10.1038/s41467-023-40365-z
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发表时间：
2023-08-03
影响因子：
16.6
通讯作者：
Sozzani, Rosangela
中科院分区：
文献类型：
--
作者：
van den Broeck, Lisa;Bhosale, Dinesh Kiran;Song, Kuncheng;Fonseca de Lima, Cassio Flavio;Ashley, Michael;Zhu, Tingting;Zhu, Shanshuo;van de Cotte, Brigitte;Neyt, Pia;Ortiz, Anna C.;Sikes, Tiffany R.;Aper, Jonas;Lootens, Peter;Locke, Anna M.;De Smet, Ive;Sozzani, Rosangela
分子生物学旨在了解复杂生物系统中的细胞反应和调控动力学。然而，由于调节蛋白的功能注释不佳，这些研究在非模式物种中仍然具有挑战性。为了克服这一局限性，我们开发了一个多层神经网络，它直接根据蛋白质序列确定蛋白质的功能。我们对大豆中的激酶和磷酸酶进行了注释。我们使用神经网络的功能注释、贝叶斯推理原理和高分辨率磷酸蛋白质组学来推断大豆在低温下的磷酸化信号级联，并确定Glyma.10G173000(TOI5)和Glyma.19G007300(TOT3)是关键的温度调节因子。重要的是，信号级联推理不依赖于已知的激酶基序或相互作用数据，从而能够从头识别激酶-底物相互作用。总之，我们的神经网络表现出泛化和可伸缩性，因此我们将我们的预测扩展到水稻、玉米、高粱和普通小麦。综上所述，我们开发了一种针对非模式物种的信号推断方法，利用我们预测的激酶和磷酸酶。人工智能网络被用来生成对蛋白质功能的高精度预测。对调控蛋白身份的预测被用来创建调控网络，并发现复杂的生物系统。
Molecular biology aims to understand cellular responses and regulatory dynamics in complex biological systems. However, these studies remain challenging in non-model species due to poor functional annotation of regulatory proteins. To overcome this limitation, we develop a multi-layer neural network that determines protein functionality directly from the protein sequence. We annotate kinases and phosphatases in Glycine max. We use the functional annotations from our neural network, Bayesian inference principles, and high resolution phosphoproteomics to infer phosphorylation signaling cascades in soybean exposed to cold, and identify Glyma.10G173000 (TOI5) and Glyma.19G007300 (TOT3) as key temperature regulators. Importantly, the signaling cascade inference does not rely upon known kinase motifs or interaction data, enabling de novo identification of kinase-substrate interactions. Conclusively, our neural network shows generalization and scalability, as such we extend our predictions to Oryza sativa, Zea mays, Sorghum bicolor, and Triticum aestivum. Taken together, we develop a signaling inference approach for non-model species leveraging our predicted kinases and phosphatases. An artificial-intelligence network is used to generate highly accurate predictions of proteins’ functionality. The predictions on the identity of regulatory proteins is used to create regulatory networks and make discoveries about complex biological systems.
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