通过深度学习模型发现核定位信号宇宙,具有可解释的注意力单元
Yi-Fan Li1, Xiaoyong Pan1, Hong-Bin Shen1
1Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
Patterns (New York, N.Y.)
|June 27, 2025
概括
NLSExplorer使用蛋白质语言模型准确预测核定位信号 (NLS),提高了超过10%的检测,并透露了跨物种的核运输景观.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 分子生物学分子生物学
背景情况:
- 核定位信号 (NLS) 对于蛋白质运输到核中至关重要.
- 准确预测NLS对于理解细胞功能至关重要.
- 现有的NLS预测方法在性能和可解释性方面存在局限性.
研究的目的:
- 介绍NLSExplorer,这是一个用于NLS预测的可解释计算工具.
- 为了提高NLS检测的准确性和效率.
- 探索NLS景观和跨多种物种的核运输特征.
主要方法:
- 开发了NLSExplorer,一种利用蛋白质语言模型的可解释方法.
- 利用提取的核特异性站点信息来增强NLS检测.
- 应用NLSExplorer到瑞士-Prot数据库中的核局部化蛋白质.
主要成果:
- 与现有方法相比,NLSExplorer 在F1得分上取得了超过10%的改善.
- 该工具成功识别了NLS和其他核运输细分市场.
- 分析显示了416种物种的潜在NLS景观和特征.
结论:
- NLSExplorer是NLS预测的一个强大而准确的工具.
- 该研究提供了对NLS景观和核运输机制的见解.
- 通过NLSExplorer,可以更轻松地检测特征性蛋白质域和蛋白质基因.
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