CoSpred:机器学习工作流程来预测蛋白质学中的协奏质谱.
Liang Xue1, Shivani Tiwary2, Mykola Bordyuh1
1Machine Learning and Computational Sciences, Pfizer Worldwide R&D, Cambridge, Massachusetts, USA.
Proteomics
|June 30, 2025
概括
深度学习模型,如完整光谱预测器 (CoSpred),通过生成准确的理论光谱来增强质谱中的和蛋白质识别. 这改善了未观察到的遗传变异的光谱库.
科学领域:
- 蛋白质组学是指蛋白质组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 基于质谱的蛋白质组学对于识别和蛋白质至关重要.
- 当前的光谱图书馆存在局限性,特别是在未观察到的蛋白质/遗传变异方面.
- 深度学习 (DL) 具有提高光谱库完整性和准确性的潜力.
研究的目的:
- 引入完整频谱预测器 (CoSpred),一个用户友好的,端到端的机器学习工作流.
- 为了能够从使用DL的序列预测完整的MS/MS光谱.
- 增强光谱图书馆的生成,以改善和蛋白质的识别.
主要方法:
- 开发了CoSpred,一个机器学习工作流程,包括预处理,训练和推理.
- 利用变压器编码器架构来预测骨干和非骨干离子.
- 设计了一个模块化工作流程,允许集成替代机器学习模型.
主要成果:
- CoSpred 便于为机器学习模型创建定制的训练数据集.
- 工作流可以预测完整的MS/MS光谱,提高光谱库的准确性.
- 展示了将最先进的机器学习纳入蛋白质组学研究的途径.
结论:
- CoSpred使高级机器学习能力更容易被蛋白质组学科学家所使用.
- 工作流有助于生成更全面的光谱库.
- 改进的光谱库可以导致更高的和蛋白质识别率,特别是对于新型变异.
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