Deep Learning in Proteomics
peer-reviewed · Proteomics · 2020
| Date | 2020-07-01 |
| Type | peer-reviewed |
| Venue | Proteomics |
| Publisher | Wiley |
| Contribution | review |
| DOI | 10.1002/pmic.201900335 |
| Citations (OpenAlex) | 164 |
| Venue 2-year citedness | 2.58 |
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.
Methods and tools
- Deep Learning in Proteomics: Broad DL-in-proteomics review (de novo is one of several topics)
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Cited by (8)
- CasanovoGUI: a cross-platform desktop application for deep learning-based de novo peptide sequencing with Casanovo (2026) both
- Zero-shot de novo peptide sequencing with open posttranslational modification discovery (2026) crossref
- Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control (2026) semanticscholar
- Bidirectional Representations Augmented Autoregressive Biological Sequence Generation (2025) semanticscholar
- Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (2025) semanticscholar
- Foundation model for mass spectrometry proteomics (2025) semanticscholar
- Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics (2023) both
- Flying blind, or just flying under the radar? The underappreciated power of de novo methods of mass spectrometric peptide identification (2020) crossref