Deep Learning in Proteomics. Issue 21 (30th October 2020)

• Record Type: Journal Article
• Authors: Wen, Bo; Zeng, Wen‐Feng; Liao, Yuxing; Shi, Zhiao; Savage, Sara R.; Jiang, Wen; Zhang, Bing
• Other Names: Wen Bo guestEditor.; Zhang Bing guestEditor.
• Is Part Of: Proteomics. Volume 20:Issue 21/22(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-30
• Subjects: bioinformatics -- deep learning -- proteomics; Proteins -- Separation -- Periodicals; Bioinformatics -- Periodicals; Proteomics -- Periodicals; Genomes -- Periodicals; Molecular genetics -- Periodicals; 572.605
• DOI: 10.1002/pmic.201900335
• Languages: English
• ISSNs: 1615-9853

Abstract

Abstract: 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.