A transformer architecture for retention time prediction in liquid chromatography mass spectrometry‐based proteomics. Issue 7 (17th March 2023)

• Record Type: Journal Article
• Authors: Pham, Thang V.; Nguyen, Vinh V.; Vu, Duong; Henneman, Alex A.; Richardson, Robin A.; Piersma, Sander R.; Jimenez, Connie R.
• Other Names: Guo Tiannan guestEditor.; Aebersold Ruedi guestEditor.
• Is Part Of: Proteomics. Volume 23:Issue 7/8(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-03-17
• Subjects: deep learning -- DIA‐MS -- retention time prediction -- spectral library -- transformer architecture; Proteins -- Separation -- Periodicals; Bioinformatics -- Periodicals; Proteomics -- Periodicals; Genomes -- Periodicals; Molecular genetics -- Periodicals; 572.605
• DOI: 10.1002/pmic.202200041
• Languages: English
• ISSNs: 1615-9853

Abstract

Abstract: Accurate retention time (RT) prediction is important for spectral library‐based analysis in data‐independent acquisition mass spectrometry‐based proteomics. The deep learning approach has demonstrated superior performance over traditional machine learning methods for this purpose. The transformer architecture is a recent development in deep learning that delivers state‐of‐the‐art performance in many fields such as natural language processing, computer vision, and biology. We assess the performance of the transformer architecture for RT prediction using datasets from five deep learning models Prosit, DeepDIA, AutoRT, DeepPhospho, and AlphaPeptDeep. The experimental results on holdout datasets and independent datasets exhibit state‐of‐the‐art performance of the transformer architecture. The software and evaluation datasets are publicly available for future development in the field.