人工智能:通过人工智能扩展的质谱图书馆探索更多的蛋白质组
Lewis Y Geer1, Joel Lapin1,2, Douglas J Slotta1
1Mass Spectrometry Data Center, National Institute of Standards and Technology, Biomolecular Measurement Division, 100 Bureau Dr., Gaithersburg, Maryland 20899, United States.
Journal of proteome research
|May 26, 2023
概括
使用神经网络预测谱可以改善蛋白质组识别. 这种方法提高了82%的准确性,并增加了8%的标识,有助于发现改性和非特异性分裂.
科学领域:
- 蛋白质组学是指蛋白质组学.
- 计算生物学 计算生物学
- 生物化学 生物化学
背景情况:
- 在复杂的生物样本中识别是具有挑战性的,因为分子换.
- 目前用于类鉴定的序列搜索算法可能会导致假阳性/假阴性.
- 谱图书馆搜索提供高灵敏度和特异性,但很难在全蛋白质组范围内进行扩展.
研究的目的:
- 开发一种方法来创建全面的光谱库,以改进的识别.
- 利用神经网络来预测完整的谱.
- 为了提高蛋白质组分析的准确性和范围.
主要方法:
- 利用神经网络来预测完整的谱,包括修饰的.
- 生成预测的光谱库,以取代从序列记录中的简化光谱.
- 应用了这些预测库来从大规模的序列搜索中重新评分匹配.
主要成果:
- 复制显著改善了真假标识的分离82%的显著改善.
- 在标识方面实现了整体8%的增加.
- 观察到非特异分离的21%增加和的17%增加.
结论:
- 神经网络预测的光谱库为全面的蛋白质组分析提供了可扩展的解决方案.
- 这种方法提高了标识的准确性和产量,特别是在改性和非典型上.
- 该方法促进了生物样本中复杂的生物分子的识别.
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