基于深度学习的质谱数据蛋白质鉴定算法优化

Rui Xu, M. Bai, K. Shu, Yilong Liang, Yun-ping Zhu, Cheng Chang
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引用次数: 0

摘要

蛋白质序列数据库搜索是霰弹枪蛋白质组学中最常用的蛋白质鉴定方法之一。在传统的蛋白质序列数据库中,通常需要首先构建每个肽的理论谱,目前只考虑质荷比信息。然而,与同位素峰强度相关的信息被忽略了。由于近年来人工智能技术的快速发展,基于深度学习的MS/MS谱预测工具在提高蛋白质序列数据库检索的灵敏度和准确性方面显示出了很高的准确性和巨大的潜力。在本研究中,我们使用深度学习模型(pDeep2)来预测所有肽的理论质谱,并将其应用于数据库搜索工具(DeepNovo),从而提高了肽鉴定的灵敏度和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Protein Identification Algorithm Optimization for Mass Spectrometry Data using Deep Learning
Protein sequence database search is one of the most commonly used methods for protein identification in shotgun proteomics. In tradition, searching a protein sequence database is usually required to construct the theoretical spectrum for each peptide at first, which only considers the information of mass-to-charge ratio at present. However, the information related to isotope peak intensity is neglected. Thanks to the rapid development of artificial intelligence technique in recent years, deep learning-based MS/MS spectrum prediction tools have showed a high accuracy and great potentials to improve the sensitivity and accuracy of protein sequence database searching. In this study, we used a deep learning model (pDeep2) to predict the theoretical mass spectrum of all peptides and applied it to a database searching tool (DeepNovo), thus improving the sensitivity and accuracy of peptide identification.
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