Proteomics, the study of all the proteins in biological systems, is becoming a data‐rich science. Protein sequences and structures are comprehensively catalogued in online databases. With recent advancements in tandem mass spectrometry (MS) technology, protein expression and post‐translational modifications (PTMs) can be studied in a variety of biological systems at the global scale. Sophisticated computational algorithms are needed to translate the vast amount of data into novel biological insights. Deep learning automatically extracts data representations at high levels of abstraction from data, and it thrives in data‐rich scientific research domains. Here, a comprehensive overview of deep learning applications in proteomics, including retention time prediction, MS/MS spectrum prediction, de novo peptide sequencing, PTM prediction, major histocompatibility complex‐peptide binding prediction, and protein structure prediction, is provided. Limitations and the future directions of deep learning in proteomics are also discussed. This review will provide readers an overview of deep learning and how it can be used to analyze proteomics data.

中文翻译：

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蛋白质组学中的深度学习。

蛋白质组学是对生物系统中所有蛋白质的研究，正在成为一门数据丰富的科学。蛋白质序列和结构在在线数据库中进行了全面编目。随着串联质谱 (MS) 技术的最新进展，可以在全球范围内研究各种生物系统中的蛋白质表达和翻译后修饰 (PTM)。需要复杂的计算算法将大量数据转化为新颖的生物学见解。深度学习自动从数据中提取高抽象级别的数据表示，并且在数据丰富的科学研究领域蓬勃发展。本文全面概述了深度学习在蛋白质组学中的应用，包括保留时间预测、MS/MS 谱预测、从头肽测序、PTM 预测、主要组织相容性复合物-肽结合预测和蛋白质结构预测。还讨论了蛋白质组学深度学习的局限性和未来方向。这篇综述将为读者提供深度学习的概述以及如何使用它来分析蛋白质组数据。