一个基于蛋白质序列的深度转移学习框架,用于识别人类蛋白质组全方位的二维基因酶-基质相互作用.
Yuan Liu1, Dianke Li1,2, Xin Zhang1
1State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.
Nature communications
|May 28, 2024
概括
研究人员开发了TransDSI,这是一种使用蛋白质序列预测二维基酶-基质相互作用 (DSI) 的新方法. 这种方法可以识别关键的蛋白质区域,并有助于发现癌症治疗的潜在药物标.
科学领域:
- 生物化学 生物化学
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
背景情况:
- 蛋白质无化对于细胞过程至关重要,由无化酶 (E3s) 和双化酶 (DUBs) 调节.
- 与E3基质相互作用相比,deubiquitinase-基质相互作用 (DSIs) 的理解较少,这阻碍了对ubiquitination调节的全面了解.
研究的目的:
- 开发一种初步方法,仅使用蛋白序列信息来预测未知的DSI.
- 确定涉及DSI的关键蛋白质区域,并提供功能性见解.
- 为癌症治疗中药物发现提供潜在的途径.
主要方法:
- 引入了TransDSI,一种基于蛋白质序列的方法,利用蛋白质层级的进化信息.
- 集成了一个可解释的模块,以精确确定DSI预测的关键蛋白质区域.
- 通过交叉验证,独立测试和湿实验室实验来验证预测.
主要成果:
- 与现有的机器学习策略相比,TransDSI表现出优越的性能.
- 成功预测了FOXP3的DUB (USP11,USP20) 和USP22的基板 (AR,p53),经过实验验证.
- 确定了新的监管DSIs,为蛋白质提供了新的功能视角.
结论:
- 即使训练数据有限,TransDSI也是预测DSI的有效工具.
- 该方法增强了对DUB功能和蛋白质调节的理解.
- 这些发现支持TransDSI在识别潜在的癌症药物点和指导精准医学方面的实用性.
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