深度学习使蛋白质识别从前体MS信息的前体MS信息
Yameng Dai1, Yi Yang2, Enhui Wu1
1Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai 200000, China.
Journal of proteome research
|August 21, 2024
概括
这项研究介绍了MonoMS1,一种新的计算方法,通过利用MS1特征与并联质谱法 (MS/MS) 一起提高蛋白质组分析. 这种方法显著改善了和蛋白质的识别,特别是复杂和低丰度样本.
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
- 质谱测量质量谱测量
背景情况:
- 双重质谱法 (MS/MS) 是蛋白质组分析的主要技术.
- MS/MS经常低利用MS1的特征,导致不完整的识别,特别是在低丰度或广泛动态范围的样本中.
- 补充MS/MS与MS1特征提供了一个有前途的战略,以提高蛋白质覆盖范围.
研究的目的:
- 开发和验证MonoMS1,一种用于MS1特征识别的新型计算方法.
- 改进复杂蛋白质样本中和蛋白质的全面识别.
- 为了证明MonoMS1在增强低丰度和广泛动态范围样本的蛋白质组分析中的实用性.
主要方法:
- 开发了MonoMS1,集成深度学习用于保留时间,离子移动性和可检测性预测.
- 使用基于MS1特征识别的基于后勤回归的评分.
- 将MonoMS1方法应用于各种蛋白质组数据集,包括大肠杆菌,人体血清和单细胞蛋白质组样本.
主要成果:
- MonoMS1显著提高了在*大肠杆菌*数据集中的MS1特征识别.
- 该方法在很大程度上补充了基于MS/MS的识别在人血清 (广泛的动态范围) 和单细胞 (低丰度) 蛋白质样本中.
- 通过整合MS1特征,证明了增强的和蛋白质覆盖率.
结论:
- MonoMS1为MS1特征识别在蛋白质组分析中提供了一种强大的新方法.
- 这种方法显著提高了复杂和具有挑战性的蛋白质组样本的识别能力.
- MonoMS1代表了蛋白质组研究的宝贵进步,特别是在需要深度覆盖的领域.
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