HydRA:深度学习模型用于从蛋白相互作用协会背景和蛋白质序列中预测RNA结合能力
Wenhao Jin1, Kristopher W Brannan1, Katannya Kapeli2
1Department of Cellular and Molecular Medicine, University of Califorinia, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine and UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA, USA.
Molecular cell
|July 8, 2023
概括
我们开发了HydRA,这是一种用于识别RNA结合蛋白 (RBPs) 及其域的新工具. HydRA精确预测RNA结合能力,扩大我们对基因调节和疾病的理解.
科学领域:
- 分子生物学分子生物学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- RNA结合蛋白 (RBPs) 是基因表达的关键调节者,它们的功能障碍与人类疾病有关.
- 识别RBP及其RNA结合域 (RBD) 对于理解细胞过程至关重要.
- 目前的方法难以识别缺乏正规RBD的RBP,限制了全面的RBP目录.
研究的目的:
- 开发一种高度特定和敏感的计算工具,用于预测RNA结合能力.
- 为了识别超出正规RBDs的新型RNA结合域.
- 加速发现RBPs及其功能的全面目录.
主要方法:
- 开发了HydRA,这是一个混合组合分类器,集成蛋白质相互作用网络和序列模式.
- 利用机器学习模型,包括支持向量机 (SVM),卷积神经网络 (CNN) 和基于变压器的蛋白质语言模型.
- 采用阻塞映射用于域检测和增强的CLIP (eCLIP) 来验证RNA目标和结合活性.
主要成果:
- 在预测RNA结合能力方面,HydRA表现出无与伦比的特异性和灵敏性.
- 确定了数百个未经表征的RNA结合相关域.
- eCLIP实验验证了HydRA的预测,证实了RNA标和新域的结合活性.
结论:
- HydRA显著推进了RBP及其相关领域的识别.
- 扩大已知的RNA结合相关域的多样性,对于理解基因调节至关重要.
- 为构建全面的RBP目录和调查与RBP相关的疾病提供了一个强大的工具.
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