A transformer architecture for retention time prediction in liquid chromatography mass spectrometry-based proteomics

TY - JOUR

T1 - A transformer architecture for retention time prediction in liquid chromatography mass spectrometry-based proteomics

AU - Pham, Thang V

AU - Nguyen, Vinh V

AU - Vu, Duong

AU - Henneman, Alex A

AU - Richardson, Robin A

AU - Piersma, Sander R

AU - Jimenez, Connie R

N1 - © 2023 The Authors. Proteomics published by Wiley-VCH GmbH.

PY - 2023/4

Y1 - 2023/4

N2 - 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.

AB - 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.

KW - Proteomics/methods

KW - Mass Spectrometry/methods

KW - Peptide Library

KW - Software

KW - Chromatography, Liquid/methods

U2 - 10.1002/pmic.202200041

DO - 10.1002/pmic.202200041

M3 - Article

C2 - 36906835

SN - 1615-9853

VL - 23

SP - e2200041

JO - Proteomics

JF - Proteomics

IS - 7-8

ER -